A Guide To The Internet Of Things

The Internet of Things will change the world. Or, more accurately, a complex and interlinked set
of evolving and increasingly affordable technologies that we collectively describe as the ‘Internet
of Things’ will continue an ages-old trend of new technology having a transformational impact on
businesses, government, and society more broadly. Far from all of this is new.
Many of the concepts that underpin the IoT are decades old. Remote monitoring and management
of distributed assets is hardly a new phenomenon. Similarly many of the business transformation
opportunities associated with IoT, such as switching from a hardware to a services business are
established operational behaviour for market leaders. However, the fact that none of this is really new
does not detract from the excitement around IoT. As William Gibson said: “The future is already here,
it’s just not very evenly distributed.” IoT is interesting and important because we are going through
an era of democratization of tools and business models which in the past were only accessible to
specialists. Due to the falling cost and complexity in the past five years, almost any business can
benefit from IoT today.
We spend a lot of time at Machina Research educating enterprises on the transformational impact
that IoT will have on their activities and organisations. What we note is a number of prevailing
motivations and benefits associated with IoT that are common across most, if not all, deployments.
Fundamentally, the Internet of Things informs. It informs farmers when their crops need watering, it
informs designers when a piece of industrial equipment has a design flaw, it informs fleet owners of
the location of their vehicles. As such, it provides an extra tool to do the job. In doing so, it provides
the competitive differentiator of the 21st century.
Furthermore, feeding more knowledge into the business also helps to level out some costly
knowledge imbalances. The second-hand car market suffers because of this knowledge imbalance.
The seller knows how good (or bad) the car is but can’t communicate it in a meaningful way to the
buyer. Meanwhile the insurance industry derives a lot of value from it, i.e., knowing the risk better
than you do. IoT provides more information upon which to base intelligent decisions.
Many suppliers in IoT are guilty of their own form of knowledge imbalance, inhabiting a world of baffling
acronyms and technology that evolves at lightning pace. If you’re thinking of deploying IoT and you’re
interested in making intelligent decisions then you have already made a great one in reading this book.
Letting Syed Hosain reset your knowledge imbalance with this comprehensive and authoritative book.
Matt Hatton
Founder & CEO
Machina Research
Foreword

THE DEFINITIV E GUIDE
| 2nd EDITION
| By Syed Zaeem Hosain, Chief Technology Officer
THE INTERNET OF THINGS
FOR BUSINESS

The Definitive Guide to the Internet of Things for Business,
2nd Edition
By Syed Zaeem Hosain, CTO, Aeris
Aeris, AerVoyance, and MicroBurst are the trademarks and/
or registered trademarks of Aeris Communications, Inc. All
third-party trademarks are the property of their respective
owners.
Copyright © 2016 Aeris Communications, Inc. All rights
reserved. No part of this book may be used or reproduced in
any manner whatsoever without the explicit permission of the
publisher.
Second Edition: August 2016
Book Design: Delin Design. Covers: ArcherDog Creative.
Editor: Trystan L. Bass.
For further information about this book, contact Aeris
Communications, Inc., 2350 Mission College Blvd, Suite
600, Santa Clara, CA 95054-1574, or www.aeris.com.

2 WHAT IS THE INTERNET OF THINGS?
3 Defining a Moving Target
6 Examples of IoT in Use Today
9 The Guide to IoT for Business
12 IOT NETWORK TECHNOLOGY
12 Basic Internet Concepts
14 Choice of Connectivity
15 ICANN and IP Addresses
18 CELLULAR CONNECTIVITY AND LOCATION
18 Types of Cellular Technologies
26 Cellular Fall-Back
27 How to Determine Location
33 IOT SENSORS AND DATA COLLECTION
33 Typical IoT/M2M Sensors
38 Conversion to Digital Data
41 Calibration and Linearization
44 SCHEDULING, ENCODING, AND PROCESSING
45 Data Transmission Schedules
47 UDP or TCP
48 Content Encoding
52 Gateways
52 Application Servers
53 Cloud Computing
54 Fog Computing
1
2
3
4
5
The Definitive Guide | The Internet of Things for Business, 2nd Edition. i
CONTENTS

CONTENTS
56 SECURITY AND THE
INTERNET OF THINGS
56 Privacy and Security
57 Security Objectives
59 Security Issues for IoT/M2M
61 Risk Management and Assessing Impact of Breaches
63 Encryption as an IoT Tool
64 Choice of Encryption Algorithm
66 IOT SCALABILITY AND
ALTERNATIVE TECHNOLOGIES
68 What Is Scalability?
70 End-of-Life Management
70 Scalability and Connectivity
77 CONNECTIVITY MANAGEMENT PLATFORMS
77 What Is a Connectivity Management Platform?
78 The Difficulties of Managing IoT Connectivity
79 Why Business Needs Connectivity Management Platforms
81 Essential Connectivity Management Platform Features
84 IOT ANALYTICS
84 IoT Data and Analytics
85 Types of Analytics
89 Analytics Tools and Languages
6
7
8
9
The Definitive Guide | The Internet of Things for Business, 2nd Edition. ii

The Definitive Guide | The Internet of Things for Business, 2nd Edition. iii
91 IMPLEMENTING AN IOT SOLUTION
91 Supply Chain Management
92 Cellular Operator Selection
93 Operator Support Service Level Agreement
93 Device Certification
94 Normal Operation Considerations
95 Application Communications Call Flow
95 Customer Support Process
97 IOT LIFECYCLE MANAGEMENT
97 Planning Checklist
99 Lifecycle Management Phases
103 Pitfalls to Avoid
105 THE FUTURE OF THE INTERNET OF THINGS
105 IoT Will Come First
106 Homes Will Get Smarter and More Connected
106 Enterprises Will Spend More on IoT
107 IoT Standards Will Need Better Definition
108 Security Concerns Will Continue
109 IoT Value Will Be Realized Through Data Analytics
111 DIRECTORY OF IOT/M2M TERMS
158 VISUALIZING THE INTERNET OF THINGS
10
11
12

The Definitive Guide | The Internet of Things for Business, 2nd Edition. CH 1: What Is the Internet of Things? | 1
CHAPTER 1
What Is the Internet
of Things?
2 WHAT IS THE INTERNET OF THINGS?
3 Defining a Moving Target
6 Examples of IoT in Use Today
9 The Guide to IoT for Business

The Internet of Things envisions a world where both ordinary and exotic devices are connected
wirelessly to the Internet and to each other. This means devices that do not already have a
network connection may have one added in the future, when it is logical and appropriate to do so.
One of the most basic uses of the IoT is to connect devices to the Internet so they can report
their status or their local environment. For example, an IoT device could be a temperature gauge,
a location sensor, a device measuring humidity, or an integrated circuit that checks vibration.
One or all of these sensors could then be attached to manufacturing machinery, and the data
transmitted would help a business track the machine’s operations. This data could track required
maintenance, improve production efficiencies, reduce downtime, increase safety, and more. Plus,
IoT devices may provide information on the ambient environment of the manufacturing space,
such as the temperature, pollution, and other conditions near the machinery, which may
be particularly relevant for remote installations.
Most IoT projects are motivated by a need to reduce operating costs or increase revenue.
Occasionally, legislation compels companies to deploy IoT applications that support a new law’s
data needs. Mobility is an obvious factor driving cellular adoption in markets like transportation.
Desire for competitive features may inspire IoT applications in consumer high-tech. But whatever
the specific purpose, connected IoT devices can give your business the data and information
needed to streamline workflows, predict necessary maintenance, analyze usage patterns,
automate manufacturing, and more.
The depth and breadth of IoT applications are creating new opportunities, providing new markets
for existing businesses, and improving operational efficiencies. Machina Research says the total
value of the IoT market will rise to $4 trillion USD by 20251
. Gartner predicts that the number of IoT
devices will grow to 26 billion units by 20202
.
1 “Machina Research Expands the Scope of Its IoT Forecasts and Highlights a USD 4 Trillion Revenue Opportunity in 2025,”
Machina Research, May 3, 2016.
2 “Gartner Says 4.9 Billion Connected ‘Things’ Will Be in Use in 2015,” Gartner, November 11, 2014.
CHAPTER 1
What Is the Internet of Things?

Although the concept of the Internet of Everything emerged
as a natural development of the IoT movement and is largely
associated with Cisco Systems’ tactics to initiate a new
marketing domain, IoE encompasses the wider concept of
connectivity from the perspective of modern connectivity
technology use cases. IoE is comprised of four key elements
including all sorts of connections imaginable:
• People—Using end-nodes connected to the Internet to
share information and activities. Examples include social
networks and health and fitness sensors, among others.
• Things—Physical sensors, devices, actuators, and
other items generating data or receiving information from
other sources. Examples include smart thermostats
and gadgets.
• Data—Raw data analyzed and processed into useful
information to enable intelligent decisions and control
mechanisms. Examples include temperature logs
converted into an average number of high-temperature
hours per day to evaluate room cooling requirements.
• Processes—Leveraging connectivity among data,
things, and people to add value. Examples include
the use of smart fitness devices and social networks to
advertise relevant healthcare offerings to prospective
customers.
Internet of Everything (IoE)
However, the very term “Internet of Things,” coined by British entrepreneur Kevin Ashton in 1999,
may no longer hold its original form. IoT is now largely overlapped, confused, and even mystified
with the idea “Internet of Everything” (IoE). The IoE is considered a superset of IoT, and the older
phrase “machine-to-machine” (M2M) communications is thought of as a subset of IoT. Let’s take
a closer look into the differences between IoT, IoE, and M2M that have impacted consumers and
businesses alike.
DEFINING A MOVING TARGET
“Internet of
Everything” (IoE)
is considered a
superset of the
Internet of Things
(IoT), and the older
phrase “machine-
to-machine” (M2M)
communications is
thought of as a
subset of IoT.

3 “Driving Unconventional Growth Through the Industrial Internet of Things,” Accenture Technologies, 2015.
IoE establishes an end-to-end ecosystem of connectivity including technologies, processes,
and concepts employed across all connectivity use cases. Any further classifications—such as
Internet of Humans, Internet of Digital, Industrial Internet of Things, communication technologies,
and the Internet itself—will eventually constitute a subset of IoE, if not considered as such already.
Devices, computers, and machines were already connected by the time Kevin Ashton coined the
term Internet of Things. The concept gained steam for its ability to connect the unconnected—
physical-first objects previously incapable of generating, transmitting, and receiving data unless
augmented or manipulated. Embedding sensors, control systems, and processors into these
objects enables horizontal communication across a multi-node, open network of physical-
first objects. The term is also vaguely used to describe connected digital-first devices such
as wearable gadgets that may be classified as “Internet of Digital” while offering the same
functionality as their physical-first counterparts developed into a smart connected technology.
The meaning and application of the term IoT will continue to evolve as new connected
technologies emerge, replacing physical-first objects with smart connected devices and
use-cases to constitute all new “Internet-of-X” classifications. Some examples of IoT include
connected cars, smart meters, and smart cities.
Internet of Things (IoT)
Industrial IoT is the use of IoT technology in business and manufacturing settings (such as utilities,
petrochemicals, manufacturing, heavy asset companies, and building automation) that are used
for asset tracking, new products/services, greater operational efficiency, etc. The term IIoT is
largely meant to include the IoT as it applies to industrial uses primarily in the manufacturing
industry and is a subset of IoT. The “Industrial Internet” has the potential to add $10-15 trillion to
the global GDP over the next 20 years.3
The Industrial IoT can be separated into three main areas: building automation, intelligent
maintenance, and machine automation.
• Building automation is the application of IoT technology to systems such as heating,
lighting, security, etc. In less than 150 years, we have gone from wood-burning stoves to IoT
automated systems that can accurately control multiple aspects of environments including
managing temperature to a specific degree automatically, based on weather and building
occupancy, without additional human input. Automation systems are already present in over
half the buildings in the US that are 100,000 square feet and above in size.
Industrial Internet of Things (IIoT)

The aptly named IoT subset M2M initially represented closed, point-to-point communication
between physical-first objects. The explosion of mobile devices and IP-based connectivity
mechanisms has enabled data transmission across a system of networks. Now, M2M refers
to technologies that enable communication between machines without human intervention.
Examples include telemetry, traffic control, robotics, and other applications involving device-to-
device communications.
The concepts of IoE, IoT, and M2M are inherently subject to the confusion surrounding limitations
associated with meanings, use cases, and adoption. While there are no industry standards and
regulations from appropriate governing authorities, these concepts will continue to evolve in
response to technology innovation, changing consumer trends, and varied marketing tactics.
Businesses evaluating the promise and potential of connectivity offerings will, therefore, have to
dig into the specifics of each situation instead of establishing conclusions based solely on the
proposed labels of IoE, IoT, or M2M.
4 “Operations and Maintenance Best Practices: A Guide to Achieving Operational Efficiency, Release 3.0,” 4. G. P. Sullivan, R.
Pugh, A. P. Melendez, and W. D. Hunt, Pacific Northwest National Laboratory, U.S. Department of Energy, August 2010.
5 “Industry 4.0: Huge Potential for Value Creation Waiting to Be Tapped,” Deutsche Bank Research, May 23, 2014.
Machine-to-Machine (M2M) Communications
Impact for Businesses and Consumers
• Intelligent maintenance is a subset of IIoT that applies to existing assets and management
systems. The benefits of intelligent maintenance are to reduce unexpected downtime, lower
maintenance costs, and eliminate machinery breakdowns. A government study has shown
that it could save up to 12% of scheduled repairs, reduce overall maintenance costs up to
30%, and eliminate breakdowns by up to 70%.4
• Machine automation incorporates IoT for precise mechanization and more flexible
production techniques to boost manufacturing productivity by as much as 30%.5

While the focus of this book is on business uses for IoT
technology, seeing how it applies to consumer devices
is relevant for a sense of scale and direct application in
everyday lives. These kinds of IoT devices let individuals
control their own network-connected devices from their
smartphones or wearables or get information about their
status from a webpage.
The most popular consumer IoT devices are typically
found in three major categories: The connected home
category includes the smart thermostat, intelligent lights,
connected appliances, and smart door locks. Next,
wearables dominate the consumer market with the smart
watch, activity/fitness tracker, and smart glasses. Finally,
the connected car rounds out the consumer category
with remote car controls, GPS navigation, and vehicle
diagnostics. Here are a couple of examples of popular
consumer IoT applications:
As new as the Internet of Things may seem,
many network-connected devices are already
in use all around us. You’ve probably heard of
connected homes or the smart grid—these
are just a few of the IoT systems aimed at
both everyday consumers and large-scale
enterprises.
IoT innovation is taking place in a wide range of
industries, locations, and types of business. IoT
innovation is only limited by our imaginations as
the technology largely exists although it may not
be readily available everywhere, as of yet. Here
are examples of innovations across a number of
different industries. Much more exists and more
have yet to be created.
EXAMPLES OF IOT IN USE TODAY
Consumer IoT Applications
The most popular
consumer IoT devices
are typically found
in three major
categories: The
connected home,
wearables, and the
connected car.
Figure 1: The connected home

The Nest thermostat is arguably the most well-known of the products in this category. Nest,
which is currently owned by Google, provides a Wi-Fi-connected thermostat that’s capable of
learning a person’s activities and setting room temperature based on those preferences. The idea
behind Nest is to always keep a home comfortable while boosting energy efficiency.
The Nest can be integrated with automated IoT lighting, security systems, and other tools,
making the long-imagined connected home more of a reality.
Internet-connected fitness trackers such as FitBit and smartwatches like the Apple Watch do
everything from act as pedometers to sleep alarms to personal coaches. These devices are
part of a “quantified self” movement that started in the mid-2000s to gain greater personal
understanding through data and technology. Devotees feel that these wearables help to achieve
health goals, and they’re even used by businesses as part of employee wellness programs to
incentivize fitness and potentially reduce health insurance premiums.
The connected car is one area that has witnessed a large increase in features. Devices are being
developed that capture a car’s computer sensor data using the vehicle’s on-board diagnostic port
(OBD) for cars built since 1996. Examples include automatic notification of crashes, notification
of speeding, and safety alerts. Additionally, concierge features provided by automakers or apps
alert the driver of the best time to leave for a prompt arrival for an appointment in the calendar or
sending text message alerts to friends or business associates to alert them of arrival times. Users
can also unlock their cars, check the status of batteries on electric cars, find the location of the
car in a parking lot, or remotely activate the climate control systems. As time passes, we expect
an increasing number of applications including the truly self-driving car made possible by IoT
technology.
To date, most industrial uses of IoT have been for preventive
maintenance. These applications detect when a machine
has variations in vibration, temperature, speed, or other
metrics, to signal that they might require maintenance.
But using IoT for preventative maintenance was just a start.
This didn’t fully tap into the ability of network-connected
devices to talk to each other, thus letting them work
together. For example, a business could use a central
monitoring hub, or even an engineer with a smartphone, to
reach out to the machine and make changes on the device,
or deliver new instructions. More and more enterprises are
realizing that these communications can create greater
efficiencies and reduce production costs far beyond IoT
systems aimed at simple maintenance functions.
Enterprise IoT Applications
The fleet industry
has been one
of the earliest
industries to adopt
IoT because of its
many benefits.

The fleet industry has been one of the earliest industries to adopt IoT because of its many
benefits. IoT-enabled trucks, ships, and vans can be tracked and managed in a more efficient
manner allowing visibility across the transportation ecosystem. Fleet telematics allow the
exchange of information between a commercial vehicle fleet and a central dispatching office.
Now, the physical health of a vehicle can be checked at a fraction of the time and in real-time.
Additionally, GPS tracking can guide a vehicle to its destination in the most efficient manner and
allow the central office to optimize the dispatch of its fleet more effectively. Some of the leaders in
the fleet management space include PeopleNet and Isotrak.
Here are examples of other industries with interesting IoT enterprise applications that are currently
deployed in the field:
Acceptacard is a provider of dedicated card-processing solutions for UK businesses. Its mobile
POS terminal is a breakthrough from what is typically provided by the banking industry in that
there are no multi-year contracts with expensive terminals. Its mobile payment solution is a
terminal-independent solution with reliable connectivity service regardless of the location and is
designed for businesses that want a payment solution on a self-service basis with online access.
Badger Meter, a leading global manufacturer of flow measurement and control solutions, enables
smart water meters for utilities and consumers to better manage their water usage. Their managed
solution allows meters to be read remotely, providing more accurate readings with proactive information
to help organizations identify potential leaks and understand what is happening in their water systems.
Also, water customers will have more control of their water usage through a consumer portal and
smartphone and tablet apps giving end-users the opportunity to see how their water is being used.
Minnetronix is a medical technology company with deep expertise in electronic and
electromechanical devices. The company created a medical device platform for remote medical
device connectivity. The platform can be integrated with any class of medical device and allows
the company to remotely manage, update, locate, and understand how their devices are being
used in the field. With this system, businesses can get real-time access to valuable information
from their devices via the web or a mobile dashboard using cellular connectivity.
Figure 2: eHealth devices

Detectronic helps reduce flooding and prevents water pollution through its intelligent network
monitoring for the British and European water industries. The company’s remote monitoring
devices provide insightful data and permit analysis and reporting that help to prevent pollution
caused by network failure and predict flooding—reducing the risk of catastrophic failures. All of
these operations depend on a reliable cellular network with SIMs capable of working over many
years and sometimes in remote areas.
While many enterprises are using IoT technology to make money, nonprofit organizations and
non-governmental organizations (NGOs) are also showing how IoT can be used to make the
planet a more habitable place and improve people’s lives.
SweetSense is an organization that has teamed with governments and NGOs to put IoT sensors
on water pumps in rural Africa. This enables the NGOs that install the pumps to track the pumps’
functionality and maintain them more efficiently and in a cost-effective manner. In a Rwanda
study, only 56% of the water pumps were working consistently. After adding the SweetSense
technology to track the pumps’ function via cellular IoT systems and analytics, the water pumps
were able to be repaired more quickly, and 91% of the pumps could be kept working on a regular
basis. With projects like this from SweetSense, connected devices can help provide clean water
for more days out of the year for more people, improving their health and well-being.
Using IoT for a Better World
In this book, we will focus on how the burgeoning IoT/
M2M ecosystem can be used by business. In addition to
providing real-time information on devices in the field, IoT
works in the other direction too: it lets companies control
devices from a central location. This can provide everything
from marketing intelligence to improved preventative
maintenance. Companies can use IoT for applications as
diverse as helping medical professionals care for more
patients at the same time or giving retailers the ability to
customize advertising to a single individual.
THE GUIDE TO IOT FOR BUSINESS
You don’t need to
be an engineer or a
data scientist, but
it is useful to have
a grounding in the
concepts of how
IoT systems are
connected.

To get started with IoT and M2M for your business, you’ll need a basic understanding of what
makes it all work. You don’t need to be an engineer or a data scientist, but it is useful to have a
grounding in the concepts of how IoT systems are connected, how they communicate, how the
data is analyzed, and how this can positively impact your organization. We’ll present an overview
on networking and the Internet and describe the Internet of Things in more detail. To do this, we’ll
cover these broad topics:
• The technology that connects the Internet of Things.
• How wireless devices are networked and locate themselves.
• Different types of sensors, how they work, and what they do.
• An overview of security technologies used to protect IoT data.
• How to scale up an IoT project to immense proportions.
• Using Big Data analytics to gain insight from the IoT ecosystem.
• IoT applications and their relationship to the IoT value chain.
• Advice for managing the lifecycle of an IoT deployment.
• A view into the future of the Internet of Things.
All of these aspects of the Internet of Things will be addressed from an enterprise point of view
for those running small to large businesses. While IoT will make an impact on many everyday
consumers’ lives, we feel that the end-user world of smartphones, fitness trackers, and connected
toasters has been sufficiently discussed elsewhere. We want to look behind the scenes into how
these IoT devices are run and managed, where the data they collect goes, and how it’s used. If
you’re in the business of IoT or looking to start up a deployment, this guide is for you.

The Definitive Guide | The Internet of Things for Business, 2nd Edition. CH 2: IoT Network Technology | 11
CHAPTER 2
IoT Network
Technology
12 IOT NETWORK TECHNOLOGY
12 Basic Internet Concepts
14 Choice of Connectivity
15 ICANN and IP Addresses

To understand how the Internet of Things and machine-to-machine communications work, you
need a basic overview of the technology used for the Internet. While technology is always evolving,
certain principles are common to how networking functions. What changes more frequently are the
tools and protocols used to access the network, such as modems, cellular radios, transmitters,
and more.
CHAPTER 2
IoT Network Technology
• IP—Traffic on the Internet uses the Internet protocol (IP)
to transmit data. This communications protocol has a
routing function ideal for Internet connectivity. IP is used
to route data packets across the Internet from a source
host to a destination IP address. Every node in such a
network has an IP address, a unique numerical label.
The computers and printers in your office generally have
private, local-area network IP addresses, while websites
such as Aeris.com have public IP addresses.
BASIC INTERNET CONCEPTS
South Korea leads the
world with the fastest
average Internet
connection speeds of
25.3 Mbps.
• Packet—Data travels across the Internet in packets. Each packet has both a source and
destination IP address, but many packets may be needed to make up one complete “item”.
For example, a single email message can be comprised of many different IP packets that,
when assembled by the recipient’s email program, makes a complete piece of mail. A
webpage retrieved by your browser is also comprised of multiple packets.
• Router—A router connects one network to another. For example, your home or office
wireless router connects the internal network in your home or office to the public Internet
via an Internet Service Provider (ISP). Your ISP connects to other providers and Internet
backbones using routers.

• Modem—A modem is a shortened term for “modulator-demodulator,” and it modulates
signals to encode digital information and demodulates the received signal to retrieve the
information. Wireless broadband modems are a popular way for smartphone and laptop users
to get Internet connections. Early wireless modems used the 2G cellular standards, but most
have moved to the faster 3G technologies. The newest standard is 4G LTE, which is becoming
rapidly available around the world.
• Speed—Internet speed is measured in megabits per second. For example, Netflix HD video
typically requires 5 megabits per second for good video quality viewing, although their service
will work at speeds as slow as 0.5 Mbps. South Korea leads the world with the fastest
average Internet connection speeds of 25.3 Mbps, and Hong Kong ranks second at 16.3
Mbps, while the United States has an average best speed of 11.5 Mbps as of 2014.
Figure 3: Data packets
Figure 4: ISP speed test

An Internet Service Provider (ISP) connects offices and homes to the Internet by taking that
network traffic and forwarding it to other networks until it gets to the desired destination. An
ISP could be, for example, Telstra in Australia. But it doesn’t stop there, because an ISP has
to connect to other ISP networks. For example, while Telstra runs a large Internet network in
Australia, it still has to connect to other networks within the country and around the world.
ISPs such as Telstra connect to Tier 2 or Tier 3 networks and up to Tier 1 networks that form
the Internet backbone. These top-level networks become the principle routes for Internet data
transmission around the world.
Wireless operators like Aeris connect IoT/M2M deployments to the Internet or private networks
in a similar fashion. A wireless operator has a cellular network that uses fixed transceivers or cell
towers instead of wires to transmit signals from the cellular devices into the network. Much like
ISPs using other ISPs, wireless operators can also connect to Tier 1, Tier 2, or Tier 3 networks.
This is how they deliver traffic on the wireless network when a mobile device requests data.
A home, office, or an IoT/M2M-networked device can be
connected to the Internet either via a wired or wireless
connection. If the connection is wired, it’s generally
connected directly into an Internet router, and the device
needs to remain stationary. A device with a wireless
connection can have a cellular modem, a Wi-Fi router, or
other connectivity technology, and among other things, this
lets the device be physically mobile.
The Internet can be accessed in many ways, depending on your device and application. There
are pros and cons to each form of connectivity technology, particularly when implementing a
large IoT/M2M project.
CHOICE OF CONNECTIVITY
Internet Service Providers
Wired and Wireless IoT Connections
The future holds
promise for more
varieties of wireless
data technologies
such as wider
adoption of 4G LTE.
Wired connectivity was common in the early days of M2M
systems. For example, many factories installed pre-wired
systems for supervisory control and data acquisition. For
business and residential security systems, alarm panels
could use telephone circuits to communicate events—
like a burglary or fire—to central monitoring stations.

However, connectivity was dependent on where an ISP’s lines could extend to, and setups could
be complicated. These early applications tended to be purpose-built, meaning each industry and
company developed its own devices and software systems from scratch.
The 1990s saw a move towards using wireless radio technologies in these applications. Ademco
Corporation, a leader in intrusion and fire detection systems, began to build out a private radio
network to address this need. In 1995, Siemens introduced the first cellular radio module for data
transmission applications. Very shortly afterwards, Aeris introduced its MicroBurst™ data services
using the control channels of the Advanced Mobile Phone System (AMPS) cellular service, and
Ademco became the first major customer to deploy M2M devices using this transport.
These new technologies broke machines free from wires, and more IoT/M2M functions were
possible in different industries and even for consumer products. OnStar was one of the first
connected-car systems in 1995, offering a mix of safety services and entertainment options.
Fleet and container tracking solutions similarly made use of mobile telematics for the trucking and
railroad transportation industries. In addition to being mobile, cellular connectivity could extend
application reach to more remote locations than wired networks could allow.
By the 2000s, changes in cellular technology introduced digital cellular networks with features such
as Short Message Service (SMS), General Packet Radio Services (GPRS), and 1 Times Radio
Transmission Technology (1xRTT). However, there arose two competing types of digital cellular,
CDMA and GSM, and different industries chose each one. The automotive and trucking industries
mostly chose CDMA devices, while the alarm and security industries generally picked GSM. By
2017, the largest American 2G GSM operator will sunset its GSM network, so alarm and security
systems still using this service are upgrading to later technologies or switching cellular systems.
The future holds promise for more varieties of wireless data technologies such as wider
adoption of 4G LTE, and, eventually 5G in the next few decades. Short-range data transport
methods, such as Bluetooth, ZigBee, and 6LowPAN, may augment long-range cellular in some
applications. We are also seeing the commercial deployment of Low Power Wide Area Networks
(LPWAN) that provide long-range communication similar to traditional cellular.
The Internet Corporation for Assigned Names and Numbers (ICANN) manages top-level domain
name assignment and delegates the assignment of lower-level domains so no two domains get
assigned the same address. ICANN works with various regional Internet registries—for example,
the RIPE Network Coordination Centre is responsible for handing out IP address in Europe, the
Middle East, and parts of Asia, while LACNIC is responsible for Latin America. These regional
groups assign IP addresses to different countries. Coordination is important because, among
other things, the world began to run out of top-level IPv4 addresses in 2011.
ICANN AND IP ADDRESSES

Due to the explosion in the number of websites, mobile devices, and always-on IP connections
(the latter of which is crucial to future IoT/M2M deployments), the Internet governing bodies
realized that the IPv4 IP address space would not be sufficient over the long term.
Luckily, the shortage noted in 2011 has not had a serious impact on many people yet because
of techniques such as Network Address Translation (NAT). This allows a router to share the same
external public IP address, or set of public addresses, for all the traffic generated by systems on
the internal network. Because of NAT, many internal systems can share a common IP address for
external Internet access.
But the long-term solution for accommodating the billions of devices constantly being added to
the Internet, especially with IoT/M2M applications, is to upgrade the IP address space to a much
larger number range. Currently the vast majority of systems use IPv4 addresses like:
101.10.101.10
This is a 32-bit number comprised of four 8-bit numbers. There are theoretically
255*255*255*255 or approximately 4.2 billion of these numbers available. In actual practice,
there are fewer IPv4 addresses because of the groupings into IP address classes. Many address
ranges have special uses, like 192.nnn.nnn.nnn for internal networks.
Too Many Internet Devices for IPV4
The problem of not having enough IPv4 address numbers will be resolved when the Internet
world moves to IPv6. In IPv6, the total address space has been expanded to 128 bits (from the
32 bits used in IPv4). This allows 2 to the power 128 (or approximately 3.4 x 10 to the power 38)
IPv6 addresses.
Although not yet fully deployed across the Internet, IPv6 networks are already in use by many
large corporations and websites. For example, Google and Facebook have provided access to
their systems in IPv6 networks.
Ultimately, every device and router will use IPv6 addresses to access the public Internet. In the
interim, gateway systems provide address translation functions—allowing older IPv4 systems to
access future IPv6 networks.
The World Is Moving to IPV6

The Definitive Guide | The Internet of Things for Business, 2nd Edition. CH 3: Cellular Connectivity and Location | 17
CHAPTER 3
Cellular Connectivity
and Location
18 CELLULAR CONNECTIVITY
AND LOCATION
18 Types of Cellular Technologies
26 Cellular Fall-Back
27 How to Determine Location

In many Internet of Things and machine-to-machine applications, knowledge of the physical
location of the remote device as it performs its tasks is an important requirement, particularly if that
application’s behavior and function depends on the location of the device.
Various mechanisms can provide this physical location with varying degrees of accuracy. The
specific accuracy needed depends on the particular application function that uses the location.
In applications where the device moves its physical location as part of its normal tasks, cellular
technologies are commonly used for data transmissions.
This chapter briefly, and generally, describes the cellular technologies used in IoT/M2M
applications and the methods used to determine device location for the applications.
CHAPTER 3
Cellular Connectivity
and Location
This section provides an overview of the cellular technologies available to IoT/M2M devices
and applications for long-range data transmissions. These cellular technologies are changing
and will continue to change over time. You should assume that new cellular technologies will
completely replace existing deployed technologies in time and plan the device and application
lifecycles accordingly.
TYPES OF CELLULAR TECHNOLOGIES
Cellular service has evolved over time. Often, a fairly major change in the technology rendered
a previous technology incompatible and necessitated a replacement of the radios and handset,
along with changes in the network to support the new radios.
In the cellular industry, these major changes are loosely termed “generations” to distinguish and
summarize their technology, the protocols used, the network changes, and the commercial
deployment phases.
Brief History of Cellular

The first cellular service was an analog cellular system1
later termed First Generation (1G). In
North and South America, this was the Advanced Mobile Phone System (AMPS). It was deployed
in the US in the early 1980s and was eventually shut down in February 2008.
AMPS used radio frequencies (spectrum) distinct from other wireless services. In particular, the
technology used relatively low-power transmissions, which restricted the distance of the radio
signals, to reach a tower (also called a base station) where the voice call could be sent into the
landline telephone system.
This allowed re-use of the radio channels beyond a particular distance from a tower—each
tower received and transmitted only to the cellular radio devices within that range. Grouped
into cells (hence, the term “cellular”) like a beehive, the tower radio did not communicate with
devices outside its cell. Cellular devices communicating in remote cells could use the same radio
channels (i.e., hence “re-use” the frequencies) without interfering with calls in the closer cell.
In the US, the spectrum used for AMPS was at 850 MHz (termed “cellular”) that was grouped into
two bands (called “A” and “B”). Thus, each market had two cellular telephone service providers
that customers could select to receive their cellular service.
The A and B bands in each market were subdivided into 30 kHz analog voice channels.
During a voice call, a channel at the tower was dedicated to that particular call, to transmit and
receive from a cellular telephone (also called a “cellphone,” “mobile,” or “handset”). The voice
communication used the entire channel for the duration of the call. As can be appreciated, this
was a very inefficient use of that available spectrum.
ANALOG CELLULAR
1 Although analog cellular is no longer used, it is described here for completeness.
4th Generation
4G LTE
1980s 1990s 2000s 2008 Beyond
Evolution of Cellular Connectivity
2nd Generation
ANSI-136 TDMA
ANSI-95 CDMA
GSM
GPRS, EDGE
1XRTT
3rd Generation
3G CDMA
(EV-DO)
3G UMTS
(HSPA/HSPA+)
5th Generation
5G1st Generation
AMPS

To maintain backwards compatibility with AMPS in the early deployments, technologists in the US
used a mechanism to slice each AMPS radio channel in time. When speaking into a cellphone,
the human voice is converted from the analog electrical signals generated by the microphone,
into digital bits using an Analog to Digital Converter (ADC). To listen to the received voice from the
tower, digital bits are converted into an analog electrical signal using a Digital to Analog Converter
(DAC) and then amplified to drive a speaker in the cellphone.
In ANSI-136 TDMA, each voice call was allocated one-third of the time (the “slot”) that the channel
was active for the transmission of the digitized voice bits. The transmissions were decoded at the
towers into multiple voice paths sent into the landline telephone system to their destinations. Hence
the general term for the protocol: Time Domain Multiple Access (TDMA). Humans are unaware of
the missing “times” when the channel is used for other voice calls, as long as the duration of the
missing time is short enough. The TDMA2
protocol is quite successful at this function.
The standard deployment was called EIA-136 TDMA (eventually ANSI-136 TDMA), and it
improved the efficiency of the channel by a factor of three (since each call only used the channel
one-third of the time). Essentially, each channel could now support three TDMA voice calls
simultaneously rather than one AMPS voice call.
ANSI-136 TDMA
2 The term TDMA is a description of the method and protocol for the data encoding, and “ANSI-136 TDMA” is a specific set of
standards implemented for these cellular transmissions. This distinction will become clearer when the GSM technology is described.
When more and more cellular users began using AMPS, it became clear that the available
channels could not support the business requirements of the operators who provided the service.
Improvements were needed.
Thus, radio technologists began to explore ways to use available wireless spectrum more efficiently.
The first improvement used digital encoding protocols for the communication rather than analog.
Three competing digital systems came into existence: ANSI-136 TDMA, ANSI-95 CDMA, and
GSM. Since this was a major change to cellular technology, these new systems (and the additional
data transmission protocols, see below) were termed Second Generation (2G) cellular.
Eventually, AMPS and other analog cellular services were discontinued in most parts of the world
(in the US, this was the “AMPS Sunset” in February 2008).

In general, CDMA protocols are more spectrally efficient
than TDMA protocols. This has allowed the deployed CDMA
technologies to survive longer than other protocols. The time
slots in TDMA are not necessarily optimal for all use cases
and essentially have a hard limit when every slot in a channel
is in actual use for calls.
In CDMA, additional calls are combined (or “spread”) using
mechanisms that are beyond the scope of this book to
describe. Suffice it to say that the number of possible calls
in a given channel may not be entirely deterministic. A very
rough estimate (and this is subject of some heated debate)
is that ANSI-95 CDMA was probably ten to twenty times
more efficient than AMPS, while ANSI-136 TDMA was three
times more efficient than AMPS.3
You should assume
that new cellular
technologies will
completely replace
existing deployed
technologies in time
and plan the device
and application
lifecycles accordingly.
In the 1990s, another new digital protocol was also deployed. Rather than using TDMA encoding,
the digitized human voice bits are combined, or multiplexed, with “codes” using a mathematical
algorithm. Thus, this encoding protocol is called Code Division Multiple Access (CDMA).
The combination of voice bits combined with codes allows the data to be transmitted over a
single wider channel. In ANSI-95 CDMA, each channel is approximately 1.25 MHz wide. The
bits are essentially “spread” across the spectrum width of that channel, and it is thus a “spread-
spectrum” communications system.
ANSI-95 CDMA
In Europe (and eventually most of the world), a different approach was used for the first digital
cellular deployments. Although the encoding mechanism is still TDMA, the available spectrum
was grouped into 200 kHz channels with eight time slots, rather than 30 kHz channels with
three time slots in ANSI-136 TDMA. This system was termed Global System for Mobile
Communications (GSM)—a marketing term that described this digital cellular service.
The bandwidth allocations and channel differences in the TDMA transmissions in ANSI-136
TDMA and the TDMA transmissions in GSM are incompatible. A GSM cellphone could not
operate in an ANSI-136 TDMA network and an ANSI-136 TDMA cellphone could not operate in a
GSM network. Of course, there were other network differences too (such as the messages used
in the control channels of the technologies), but the radio technical differences were fundamental.
GSM
3 The term CDMA is a description of the method and protocol for the data encoding, and “ANSI-95 CDMA” is a specific set of
standards implemented for these cellular transmissions. This distinction will become clearer when newer technologies are described.

GSM rapidly became popular in Europe and in other parts of the world. This was particularly true
because the early analog cellular systems in many countries were entirely replaced very quickly or
were not deployed in the first place in some other countries.
This rapid growth of GSM networks and services made it a popular choice outside the Americas
and a few other countries. With the far larger deployed base of cellphones, the economies of
scale meant that GSM cellphones rapidly became lower in cost than ANSI-136 TDMA cellphones.
Thus, the operators in North and South America eventually abandoned ANSI-136 TDMA in favor
of GSM to take advantage of this reduced cost.
When cellular systems became digitally encoded, it was natural to consider treating the
transmitted digital bits as something other than human voice encoded bits. This allowed the
deployment of data transmission services for purposes other than human voice. This included
communications from mobile radio devices (data handsets) and data cards for mobile computers
(laptops) to access the increasingly important Internet and the World Wide Web.
The mechanism for treating the digital bits as application data, rather than human voice, was
different in the deployed technologies. ANSI-136 departed too quickly for any significant data
protocols to be deployed, but both 2G GSM and ANSI-95 CDMA experienced this evolution.
Data Transmissions
GSM introduced a practical data transmission technology
called General Packet Radio Service (GPRS), followed by an
improvement called Enhanced Data Rates for GSM Evolution
(EDGE) with higher throughput.
These technologies were popular for cellular data
communications, although the throughput rates are
extremely slow by today’s expectations for smartphones
that access the Internet. In IoT/M2M applications, however,
where the throughput requirements are lower, GPRS is a
perfectly good technology for data transmissions.
Thus, GPRS is commonly used around the world for cellular IoT/M2M applications. But it
encountered spectral efficiency issues that make it impractical for use for high-end human
smartphone applications. In the US, the largest operator providing 2G GSM announced that it will
stop providing GPRS and EDGE data services (and hence, sunset 2G GSM) on January 1, 2017.
2G GSM DATA: GPRS, EDGE
In the US, the largest
operator providing 2G
GSM announced that
it will stop providing
GPRS and EDGE data
services (and hence,
sunset 2G GSM) on
January 1, 2017.

Other US operators are likely to follow this example at some time in this decade too, but this is
less of a problem for countries where competitive business pressures for wireless spectrum are
not as high. For example, GPRS is very likely—albeit not certain—to remain in use in Europe
through the middle of the next decade.
EDGE was rarely used for IoT/M2M applications, since GPRS (in GSM) and 1xRTT (in CDMA, see
below) was sufficient for the vast majority of such uses, and newer data technologies became
common for power smartphone users quickly enough.
Like GPRS in GSM, the CDMA operators in many countries deployed a data transmission
technology called 1x Real Time Transmission (1xRTT). This was faster than GPRS in its base
throughput rate and has also proven to be very successful for many IoT/M2M applications.
Defined into the ANSI-2000 standard, it provided (and continues to provide) a reliable, extensive
coverage data network for IoT/M2M applications.
In the US, the wide availability of 1xRTT makes it an easy choice for physically mobile
applications, such as the automotive and trucking industry, that need coverage across the
continent. The early deployment and expansion of CDMA and 1xRTT (while the other camp was
busy with a transition from ANSI-136 to GPRS) led to excellent coverage across the country.
However, the complexity of the CDMA data encoding protocol compared to TDMA also resulted
in a higher cost for the radio modules, since chipsets for CDMA radios are more complex. Thus,
due to the greater deployment of GSM and economies of scale, 1xRTT modules are more
expensive than GPRS radio modules.
For smartphone users, the CDMA data standards were substantially improved to enhance their
data throughput rates. Technology change cycles added EV-DO Rev. A and EV-DO Rev. B to the
portfolio (renaming the original implementation as EV-DO Rev. 0). The changes were added to a
new standard called ANSI-2000, which detailed the 1xRTT and EV-DO technologies.
Although used by some IoT/M2M applications, 3G EV-DO has not been extensively used for
these kinds of applications, since the higher throughput (compared to 1xRTT) is not strictly
required. The excellent coverage and availability of 1xRTT service in the US essentially made it
unnecessary to do so, since the radio module costs are higher for EV-DO.
2G CDMA DATA: 1XRTT
3G CDMA (EV-DO)

In the GSM technologies, it became clear over time that the 2G GSM voice and data transports—
GPRS and EDGE—that used the TDMA encoding protocol, were not sufficiently spectrum-
efficient. The cost of adding new spectrum became much higher, as national governments began
auctioning new spectrum for smartphone data uses.
Thus, the standards bodies began defining and deploying a new technology called Universal
Mobile Telephone Service (UMTS). They abandoned the TDMA protocols in favor of a new
CDMA protocol since CDMA is more spectrum-efficient than TDMA. However, in UMTS, a wider
5 MHz channel differentiates it from the ANSI-95 and ANSI-2000 deployments. The differences
are substantial enough that the UMTS protocol is often referred to as using Wide-Band CDMA
(W-CDMA) to distinguish it from ANSI-2000 CDMA.
In UMTS, the data technologies have evolved quickly. Early “faster in one direction” transports—
such as High-Speed Downlink Packet Access (HSDPA) and High-Speed Uplink Packet Access
(HSUPA)—have been mostly replaced by High-Speed Packet Access (HSPA), including variants
called HSPA+ that allow for yet faster throughput.
In most IoT/M2M applications, using 3G HSPA is not needed since the performance and
throughput of this data technology is high. Indeed, the 5 MHz channel allows it to provide faster
overall throughput than EV-DO with its 1.25 MHz channels. However, since 2G GSM data
transports (GPRS) have a finite availability in North American markets, there is a need to change,
and 3G HSPA is one service that can fill that need.
On the other hand, 3G HSPS is a relatively recent technology and does not have the coverage
of 2G GSM GPRS or 2G CDMA 1xRTT. And 4G LTE networks are also being rapidly deployed.
Thus, many 2GSM IoT/M2M applications are either switching to 2G CDMA 1xRTT for an interim
solution or leapfrogging 3G to go directly to 4G LTE in the near future. This choice is generally a
function of the cost of available radio modules and service coverage.
One limitation of 3G technologies is that they use fixed-width channels. With the ever-increasing
number of smartphone data users, the availability of wireless spectrum has created many new
bands that are not always optimally usable by 3G technologies. National governments have
auctioned a large number of new bands for smartphone users.
To use these new bands, the standards entities developed a new technology for more flexible
spectrum use. Since they also had the opportunity to select the encoding protocols to use these
new bands, Long Term Evolution (LTE) was designed to use a new protocol called Orthogonal
Frequency Domain Multiple Access (OFDMA). Again, the specific encoding details of OFDMA
is beyond the scope of this book, but it has been termed a Fourth Generation (4G) technology,
since it is quite different from 3G and also meets some of the original performance requirements
set for new cellular implementations under the umbrella of a 4G service.
3G UMTS (HSPA/HSPA+)
4G LTE

What is quite important, however, is that LTE is very flexible in terms of the channel widths that
can be used, and thus the available spectrum bands can be partitioned into smaller blocks with
greater ease. And it also allows existing spectrum to be partitioned into multiple blocks, which
can allow an operator to deploy 4G without having to entirely remove older technologies.
The flexibility comes at a price. There are more than 30 bands available for LTE use, and countries
have not auctioned or made available the full set of possible bands. Indeed, some bands may be
impossible to use for LTE in certain countries because they are dedicated to other uses.
Thus handsets that can be used for LTE everywhere must support a number of different bands,
and the addition of each band adds cost, since filters and power-amplifiers inside the radios must
support each band. For IoT/M2M applications, this can increase the overall cost of the radio
module substantially. Smartphones can absorb the higher cost of multiple band support, since it is
a smaller percentage of the overall cost of the phone. This cost issue will eventually drop in impact,
because the ever-increasing number of deployed LTE units will cause economies of scale to apply.
In addition, LTE uses the concept of categories (CAT) to define a set of performance metrics
that are dependent on other parameters (such as the number of spatial layers, antennas, and
protocols). Originally defined as CAT 1 through CAT 8, these provided a different range of
performance, from 10 Mbits/sec download speeds in CAT 1 through 1200 Mbits/sec downloads
in CAT 8.
Most LTE smartphones use CAT 3 and 4 to provide data rates that are sufficient for power users,
and CAT 6 smartphones are becoming available. For IoT/M2M applications, CAT 1 radios would
be sufficient performance, but were not originally developed since the LTE chipsets with CAT 1
support were not deemed adequate for smartphone users. However, recent developments in LTE
chipsets have allowed manufacturers to release CAT 1 modules for IoT and M2M applications.
The standards bodies also defined CAT 0 (and CAT M) radios for LTE that have reduced
performance and network requirements, and these have been recently ratified. These are
expected to be supported in LTE chipsets and within the network (since changes are required in
the network deployments too) within the next few years.
CAT 0 radios that do not support the higher performance requirements of LTE categories should
be even less expensive, since the chipsets should be substantially lower in cost too.4
4 The CDMA operators (who had deployed 2G 1xRTT and 3G EV-DO) as well as the GSM operators (who had deployed 2G
GSM/GPRS/EDGE and 3G HSPA) are all rapidly moving to fully deploy 4G LTE—this has implications for the types of LTE
radios used by these operators.

During the early phases of any new cellular generation deployment, it is often the case that the
newer generation is not fully deployed everywhere. Typically, the geographical coverage starts
small and expands over time. Thus, the cellular devices must support multiple generations of
technologies till coverage is fully complete for the new technology.
The cellular radios essentially “fall back” from newer generations to older generations when the
newer generation service is not available at a particular geographical location. The control of
when to fall back (including which technology to fall back to) is incorporated in the Subscriber
Identity Module (SIM) or other radio firmware.
CELLULAR FALL-BACK
To accommodate this fall-back requirement, in GSM, all 3G cellular devices—modules,
smartphones, and cellphones—are expected to also function in 2G GSM/GPRS and EDGE
modes. This allows them to be used in areas where 3G UMTS service may not be available. This
increases the cost of the cellular device, but is an acceptable trade-off since it is essential to
provide robust service coverage for all users of the services.
Similarly, in CDMA, the 3G EV-DO modules, smartphones, and cellphones are capable of being
used in 2G 1xRTT modes—enabling use in markets where 3G may not be available (this is a
relatively rare situation however).
In 4G LTE, there are two technology fallback mechanisms.
For the CDMA operators who are deploying LTE, the radio
must fall back from LTE to EV-DO and 1xRTT. For the GSM
operators deploying LTE, the radio must fall back from LTE
to UMTS (HSPA) and then to EDGE or GPRS (since 3G is
not available everywhere).
These fall-back mechanisms increase the complexity
and cost of the chipsets within the current modules and
smartphones. In time, when LTE is commonly available
everywhere that cellular services are deployed, it makes
sense to use radios that only use LTE services—called
LTE-Only modules. These have just begun appearing for
purchasing, and more manufacturers will deploy LTE-Only
modules soon.
Two Fall-Back Mechanisms
LTE-Only
The cellular radios
essentially “fall
back” from newer
generations to
older generations
when the newer
generation service
is not available
at a particular
geographical location.

LTE-Only can reduce the cost of modules substantially—with scale, these LTE-Only devices are
approaching, and will become less expensive than the lowest-cost 2G GPRS radios available
today. In a few more years, this should be true for all suppliers that provide IoT/M2M modules.
Customers who want to migrate from 2G to 3G services to 4G may find it worthwhile to wait for
this cost reduction in LTE-Only modules to make the transition.
This transition date is dependent on the customer product longevity requirements—clearly 2G
GPRS units may stop working in markets (such as the US) soon enough that a transition to a 2G
CDMA, 3G HSPA, or a 4G LTE device may be required sooner rather than later.
For many IoT/M2M applications, knowledge of
the physical location of the devices is important—
not only to the device but also to the application
servers that process data from the devices.
For example, in consumer automotive M2M and
IoT applications, knowledge of the exact location,
to a reasonable accuracy, of a vehicle crash is vital
so that emergency first responders can be sent to
the crash site quickly. Seconds may matter!
In truck telematics, a dispatch service may need to know the location of the vehicles in its fleet to
optimize the selection of the correct vehicle to handle the specific event—perhaps it is the nearest
vehicle to the pickup or one that has the available cargo capacity for the job. In both cases, the
knowledge of the device location is important to a particular degree of accuracy (i.e., the error in
the location “fix”).
For emergency dispatch, the US Federal Communications Commission (FCC) has defined
location accuracy requirements that must be made available to Public Safety Access Point
(PSAP) personnel. These are often called the “E911” requirements, since the number 911 is
used to access emergency services from landline phones and cellphones.
The E911 accuracy requirements are not necessarily sufficient for some IoT/M2M applications.
The location error may not allow proper calculation of routes or dispatch with sufficient
optimization. For these applications, more accurate location fix mechanisms must be used.
HOW TO DETERMINE LOCATION
The E911 accuracy
requirements are not
necessarily sufficient for
some IoT/M2M applications—
for these applications,
more accurate location fix
mechanisms must be used.

Location From Cellular Network
Global Positioning System
To support the E911 requirements for physically mobile cellphones used by humans (i.e., which
are not fixed at a particular address like a landline phone), cellular operators have implemented
various device location mechanisms in their networks. These generally rely on classic radio
triangulation techniques that provide the specified degree of accuracy for the E911 requirements.
These network-based location fixes are made available to the PSAP personnel as needed, and
are also available from operators as Location-Based Service (LBS) information, generally for a fee
charged for each location fix of a cellular device. Unfortunately, the cost of these location fixes
may be too high for many IoT/M2M uses, and the accuracy may not be sufficient for some uses,
and thus, has not proven to be a common technique.
Thus, using the GPS (as well as GLONASS, and soon, Galileo) system may well prove to be a
superior solution for most M2M and IoT applications.
In the latter half of the last century, the US Department of Defense deployed a set of 24 satellites
into Earth orbit for a very singular purpose: it allowed a GPS-equipped device to determine its
location on the surface of the earth with very good accuracy.
Many cellphones are now equipped with Global Positioning
System (GPS) support that allows the phones to determine
their location and provide that information to the cellular
network, for E911 and other purposes. Enabling this function
is often an available choice in cellphones equipped with GPS.
In IoT/M2M applications, most modules have built-in GPS
support (sometimes including support for both systems
operated by the US and Russian governments). In the
future, support for Galileo will be implemented in most
modules and handset.
These can be used by the application firmware in the device
when needed for a particular function—such as responding
to a location fix request by a dispatch application.
LOCATION-BASED SERVICES
GLOBAL POSITIONING SYSTEM
Ground-based
references can be
used by certain
receivers to greatly
enhance the basic
accuracy of the GPS
system from 15
meters to less than
10 centimeters.

Originally intended for military uses, the US government made the system and its information
available for civilian use in the 1980s, without any fee or subscription charge. This enabled a large
number of new location applications around the world.
For example, the truck telematics
industry relies heavily on the
GPS system to locate trucks
and trailers. Hikers and off-road
personnel can use hand-held
GPS trackers to avoid becoming
lost. High-accuracy augmented
GPS systems are used by semi-
automated farming equipment
since these can often locate the
vehicle within a few centimeters
on the surface of the Earth!
Survey equipment can use GPS
to accurately measure location for
mapping and thus increase map
quality and improve route guidance
systems in vehicles.
A satellite service similar to GPS, called GLONASS, has been deployed by the Russian
government. The European Union is in the process of launching its own system called Galileo
(named after the historic astronomer). As of this writing, the Galileo constellation of satellites is not
yet operational. The Indian government has introduced its own localized system, called IRNSS,
to determine location, but only over the Indian subcontinent. Similarly, the Chinese government
launched the first version of their satellite location systems, called BeiDou-1, to cover China. They
are now launching new systems, called BeiDou-2 and BeiDou-3, for global location coverage
similar to the US and Russian systems.
In time, Galileo will provide a free, low-precision location fix with an accuracy of 1 meter, but high-
precision fixes will only be provided for a fee. Since it is a new system, it also has new features that
are not available in the older US GPS and Russian GLONASS systems. For example, Galileo has
radios that are planned to support a unique relay service for Search-and-Rescue (SAR) distress
signals, allowing emergency dispatch around the planet.
In addition to the satellite GPS system transmissions, enhancements are available to dramatically
improve the location accuracy. For example, a set of ground-based references can be used by
certain receivers to greatly enhance the basic accuracy of the GPS system from 15 meters to less
than 10 centimeters. This enhanced system is called Differential GPS and enables farms to use
automated equipment that need a very high accuracy location fix.
Figure 6: Global positioning satellites

In the US GPS system, 24 GPS satellites orbit the Earth
twice a day5
in a very precise manner at an altitude
of approximately 20,000 km while transmitting accurate
time signals from their on-board atomic clocks to ground
GPS receivers.
These GPS receivers take the received time data and use
triangulation (more correctly, “trilateration” using points of
intersection of circles on a sphere; angles are not measured)
techniques to determine the location of the receiver.
The receiver essentially compares the time a signal was
transmitted by a GPS satellite to the time it was received.
This time difference allows the receiver to determine its
distance from that satellite.
When this time difference and distance is determined from a number of GPS satellites, the
location of the receiver can be determined within about 5 to 10 meters of accuracy on the surface
of the Earth. At least three satellites must be used for a latitude-longitude fix on the surface of the
Earth, and a fourth satellite can then determine the altitude of the receiver.
It should be emphasized that the above is a very general description of the method used to
determine location from the GPS satellite signals. There are a number of other factors that affect
the accuracy and are taken into account by sophisticated receivers.
For example, the more satellites the receiver listens to, the better the accuracy. Thus, a 10- or
12-channel GPS receiver (which allows it to listen to 10 or 12 GPS satellites simultaneously) will
generally provide a more accurate location fix than an older 4- or 6-channel receiver. Modern
GPS chips can listen to more than one satellite system simultaneously—i.e., both GPS and
GLONASS satellites—for best accuracy.
Furthermore, since the GPS satellites are in motion and are quite far above the Earth’s surface
(i.e., operating in reduced gravity), Einstein’s Special and General Theories of Relativity must
be used to correct the data, since time literally flows at a different rate for the satellite clocks
compared to the Earth-bound clocks.
HOW DOES BASIC GPS WORK?
Einstein’s Special
and General Theories
of Relativity must
be used to correct
the data, since time
literally flows at a
different rate for
the satellite clocks
compared to the
Earth-bound clocks.
5 Contrary to popular belief, GPS satellites are not in a geo-synchronous orbit above the same spot on the Earth.

Without proper compensation, the relativistic effects of the speed of the satellites combined
with their height could create a net error of about 38 microseconds per day at the satellite clock,
compared to an identical ground-based clock. This may seem quite inconsequential, but the
difference in time can make the location fix inaccurate within a matter of minutes, to beyond the
5 to 10 meter accuracy of the system. Then, accumulated errors could make the location fixes
completely unreliable and unusable in a matter of days to weeks since the GPS system requires
nanosecond time accuracy.
Fortunately, the GPS system uses these Einsteinian Relativity calculations and corrects to ensure
that the time and location accuracy is excellent, and remains excellent, under most conditions.
With multiple location fixes (i.e., taken over time), the data fixes can also be used to determine
other information such as speed and direction (i.e., velocity). Sophisticated GPS tracking devices
can use the data to display the location and provide route guidance in friendlier ways than a simple
latitude-longitude-height-time record—displayed on a graphical moving map, for example.
Location fixes from GPS are not perfect. In “urban canyons” (i.e., within cities with tall buildings),
it may be difficult for GPS receivers to lock onto more than a few satellites, since the signals
may be blocked by the buildings. This may reduce the accuracy substantially. Regardless, it
may remain sufficiently capable for many uses of that location data. A higher-performance GPS
receiver with many channels may perform better in urban canyons since it has a better chance of
listening to satellites that may be “visible” and not blocked by tall buildings.
The signal strength is low enough that many GPS receivers cannot listen to the signals from the
satellites when inside buildings and underground garages. This limits their use in indoor applications.
Sometimes, heavily overcast days can reduce the strength of the GPS signals, enough to prevent
the receiver from locking on to the signals, particularly when the receiver has been re-started from
a power-off condition. If the internal clock of the receiver is not sufficiently accurate, the measured
time may have drifted, and the receiver could be attempting to listen to a set of satellite signals that
are not present. Those particular satellites may not be visible. Under these conditions, it may take a
while for the receiver to lock onto the satellite transmissions and provide sufficient accuracy.
IoT and M2M applications that depend on the accuracy must take this potential for incorrect
location fix data into account when deploying the device into the field.
LIMITATIONS OF GPS

CHAPTER 4
IoT Sensors and
Data Collection
33 IOT SENSORS AND
DATA COLLECATION
33 Typical IoT/M2M Sensors
38 Conversion to Digital Data
41 Calibration and Linearization
The Definitive Guide | The Internet of Things for Business, 2nd Edition. CH 4: IoT Sensors and Data Collection | 32

When deploying Internet of Things and machine-to-machine application devices, the connected
device generally needs to report more than just its physical location. In this chapter, we describe a
few of the more common sensors and what they do.
For example, an IoT/M2M device may measure a particular physical parameter at that location.
These physical parameter measurements require sensors that are capable of recording the specific
value of that parameter for the device application to fulfill its functions.
Sensors are often integrated circuits that are designed for these kinds of IoT/M2M applications,
since the small size and low cost of these chips make them appropriate choices. For example,
many of the sensors described in this chapter are available in high-end smartphones. These
include accelerometers, thermometers, gyroscopes, magnetometers, and heart-rate monitors—
just to name a few—but there are other sensors that are unique to a particular industry or market.
In this chapter, we describe a few of the more common sensors, what they do and how to use them.
CHAPTER 4
IoT Sensors and
Data Collection
In most of these typical sensors, the specific mechanism
used to measure the physical parameter depends on
the ranges being measured, the sensitivity and accuracy
desired, whether the sensor could be exposed to adverse
environmental conditions, the cost target, etc.
Since it is quite impossible to list every possible sensor, its
type, and its capabilities, this section focuses on general
descriptions of a few types of sensors rather than making
specific recommendations.
TYPICAL IOT/M2M SENSORS
Vibrations detected
by an acceleration
sensor could be an
excellent indicator
of a potential problem
with a moving part.

Z
Y
X
Acceleration is a measure of a change in
velocity (change of speed or direction).
Accelerometers are devices that measure
acceleration. The parameter being measured
may be a static force, such as gravity exerted
on a device. Other sensors make dynamic
force measurements to measure motion
changes and vibration.
An example of an acceleration sensor is
a chip in a moving vehicle that measures
changes of speed and uses high acceleration
readings (such as during an accident) to
trigger an airbag to protect the passengers.
In some industrial applications, the vibrations detected by an acceleration sensor could be an
excellent indicator of a potential problem with a moving part, such as a motor with bearings
that are worn. Timely transmission of the data from vibration sensors enables early detection of
problems where preventative maintenance could avoid catastrophic failures.
Accelerometers
MULTI-AXIS ACCELEROMETERS AND SENSITIVITY
In some applications, there is a need to measure the change in speed or vibration in more than
one direction (or dimension). Thus, some accelerometers take readings in more than one axis.
Typically, a two-axis sensor measures motion changes and vibration in two dimensions, and a
third axis on a three-axis sensor can provide information for three-dimensional physical motion
detection.
Accelerometers use differing techniques for measuring the motion changes. Generally, there is a
physical component that changes an electrically measured characteristic (such as capacitance or
resistance) in a material when motion is sensed.
Due to the types of accelerations being measured, the sensitivity of the accelerometer is often
over a limited range to the required accuracy that is specific to a particular application use.
The choice of sensor to use thus depends on the specific range of acceleration values to be
measured for that application.
For example, a shock sensor designed to release a vehicle airbag in an accident measures quite
a different range compared to a sensor that measures vibration on a high-speed motor to monitor
its bearings. The sensitivity and accuracy required for these widely disparate applications is
naturally quite different.
Figure 7: Accelerometer

Temperature Sensors
Temperature is a parameter that is often measured and
reported—particularly in industrial applications where an
accurate temperature reading may be needed for process
control. Depending on the desired measurement range,
there are various types of available sensors for measuring
temperature.
Silicon chip (semiconductor) sensors are easily used in the
range from -50 to +150 degrees C. These are quite accurate
and linear—to within 1 degree—without the need for extensive
calibration. They are as rugged as most integrated circuits
(package and metal-can style) and relatively inexpensive.
Thermistor sensors can cover a wider range—from -100 to +450 degrees C—for more
applications. A thermistor is often more accurate than a silicon chip temperature sensor, albeit at
a slightly higher cost per sensor. More importantly, they require a complex correction to achieve
good linearity and accuracy over the desired temperature range.
Resistance-Temperature Detectors (RTD) provide yet more range, from -250 to +900 C, but are
quite difficult to use since they are more fragile than other types of temperature sensors. They are
the most accurate—often a hundred times more accurate than a silicon chip sensor, although
this carries the same complex solutions for linearization as thermistors, and some models can be
quite expensive.
Finally, for the widest temperature range, particularly for high temperatures, a thermocouple is the
correct choice. They are quite rugged and can be used from -250 to +2000 C for many industrial
applications, such as chemical process monitors and high-temperature furnaces used in the
semiconductor industry.
One important fact about temperature sensors: the response time for the sensor data can be
quite slow, since temperature changes are not as “rapid” as other measured physical parameters.
The sensors must settle and equalize to the same temperature as is being measured. This must
be taken into account when taking readings.
The response time for
the sensor data can
be quite slow, since
temperature changes
are not as “rapid”
as other measured
physical parameters.

Light sensors cover a broad range of potential applications, from automated brightness control
in cellphones to medical diagnostic equipment. Not surprisingly, there is also a wide range of
available light sensors that use different methods for measuring the ambient light.
A very early example of ambient light sensors used in local consumer applications are photocells
within lamps that automatically turn the lamps on at dusk and turn them off at sunrise. These are
simple light detectors, with equally simple sensitivity controls that are manually adjusted by the
owner of the product. The actual value (in lumens) of the ambient light is not measured or reported.
Simple light sensors can also be used for proximity detection. Counters in manufacturing systems
use the presence or absence of light on photocells to measure products being moved past the
counter on conveyors. Garage door systems can reverse direction to avoid hurting children or
pets that cross under a closing garage door and temporarily cut the light from a source sending a
beam of light across the door opening onto a photocell.
Often, light sensors can be used with light that is not visible to human eyes. Infra-red light
sensors can be used as motion sensors in alarm systems or to automatically light a driveway
or passage when people and pets come into range. Full-range sensors are used when the light
measurements need to correspond to human vision.
As with other types of sensors, the mechanism used to measure ambient light varies depending
on the application. Simple Cadmium Sulphide (CdS) or Cadmium Selenide (CdSe) photo-
resistors change their resistance as a function of the ambient light. This resistance change can be
measured in electronic circuits to provide an indication of a change in the ambient light. It should
be noted that these photocell devices can be significantly affected by temperature and are quite
unsuitable when accuracy is required.
Common uses of photo-resistors include automated light controls in lamps, dimmers in alarm
clocks and audio system displays, control of street lighting systems, etc., where the accuracy of
the reading is not a paramount requirement.
Photo-diodes and photo-transistors, with active semiconductor junctions, are used when greater
accuracy is required, since the ambient light is converted into a measurable current that can be
amplified or converted for a measurement. This measured current can be used to determine the
amount of ambient light on the sensor. Indeed, since semiconductor junctions are affected by ambient
light, integrated circuits where this effect is not desired must be enclosed in opaque packages.
Light Sensors

In modern, high-end smartphones, integrated chip sensors to
measure motion, direction, pressure, magnetic fields, rotation
speeds, etc., are becoming quite common. These can be
used to augment the location information and human motion
in the cellphone.
In chip form, these are usually Micro-Electro-Mechanical
Systems (MEMS) sensors for many different parameter
measurements. The implementation of MEMS uses ultra-
miniaturized physical structures—beams, arms, and
associated electronics—to measure the motion of the
structures when the chips move. The physical motion is
converted to electrical signals that can be measured for the
specific function being measured—for example, whether it
is rotational motion or air pressure. The device essentially
converts a mechanical motion into an electrical signal.
A gyro sensor, for example, senses rotational motion and changes in orientation. These can be
used in a variety of applications, such as correcting for hand-held shake in video and still-image
cameras and human motion sensing for video games. In smartphone applications, a screen
display can be automatically rotated from portrait to landscape display modes when the phone
is physically rotated.
MEMS sensors are generally manufactured in the same large-scale facilities as semiconductors
or chips. This means that the mechanical precision of the devices can be very high and allow for
excellent, reliable performance at low cost.
At the low end of the sensor markets are the simple state or position sensors that provide an
“open” or “closed” state information. A door or window sensor used in security systems is often
a simple magnetic reed relay switch that opens or closes an electrical circuit depending on the
position of a small magnet located close to the switch.
These simple magnetic reed relay switches can be used for sensing when a cabinet—such as
a medicine cabinet, oven door, or food storage compartment—has been opened in a senior
citizen’s home-monitoring IoT application. A detection of the change of state of such a switch—
from open or closed or vice-versa—can be interpreted as evidence that the monitored parent has
performed their expected regular daily routine.
MEMS Sensors
Simple Switch Sensors
MEMS sensors can
be used in a variety
of applications, such
as correcting for
hand-held shake in
video and still-image
cameras and human
motion sensing for
video games.

Because of the wide variety of sensors, the types, the parameter being measured, and
what physical phenomenon is converted into a measurable signal, it is difficult to provide
implementation details. Thus, this section must necessarily discuss general concepts rather than
specific information.
Sensors are often used in local applications, where their signal is processed using circuitry
designed for that local application. However, in a sensor that is used for remote data transmission
of the measurement, the electrical signal must be converted into a digital value, or number, for
the transmission.
The specific electrical signal from different sensors may vary over a wide range of current or
voltage or other electrical parameters (such as resistance or capacitance) and often must be
converted and amplified into a voltage that can be measured.
If necessary, the signal must be electronically filtered to eliminate signal noise or to reduce the
frequency of the measurement for the requirements of the application. For example, a temperature
sensor generally changes its value relatively slowly as the sensor matches its environment.
Therefore, a rapid change in reported temperature may be an inaccurate reading, which should be
filtered to reduce potential errors.
CONVERSION TO DIGITAL DATA
In devices that measure sensors for data transmissions, two input capabilities are generally available:
• A digital input pin that reports an electrical “high” or “low” value in a single digital bit
(sometimes grouped into multiple pins and multiple bits).
• An analog input pin that receives a voltage from a sensor and converts that voltage, using an
Analog-to-Digital Converter (ADC), to a digital number that represents the sensor value.
These devices may also have output pins where a received value from the network is used to:
• Set a digital output pin to either “high” or “low” state based on an instruction to do so.
• Set an analog output pin to an analog voltage, using a Digital-to-Analog Converter (DAC),
representing the received digital number.
Input and Output Pins (I/O)

In simple switch applications, where the state is “open” or “closed,” using a digital pin to measure
this state and report its value (“0” or “1”) is an easy choice. For more complex needs with simple
switches, the device may also report when the transition from one state (such as “open”) to the
other state (such as “closed”) occurs.
That is, it may be equally, or more, important to report a change of state rather than the present
state of the simple switch.
In sensors that measure parameters over a range, a single
bit is insufficient—the range of the sensor values must be
converted into a range of digital values for the application.
However, the specific signal from a sensor may differ widely
in its current or voltage or resistance value. This signal—
whether it is a current or resistance change—must be
“conditioned” or converted to an analog voltage. If the signal
from the sensor is a voltage, it might not be in the correct
range for an ADC to convert to a digital number and thus
may require amplification to a higher or lower range.
For example, a commonly available semiconductor temperature sensor provides an electric current
of 1 microAmp per degree Kelvin when power is applied to it. Over a useful range of -50 degrees
C (or 223 degrees Kelvin) to +150 degrees C (423 degrees Kelvin), this current is approximately
223 microAmps to 423 microAmps.
This current can be used in a circuit with an Operational Amplifier (Op-AMP) and other
components (resistors, capacitors, and diodes) to convert to a voltage in the desired operating
temperature range being measured. This voltage can then be measured by an ADC and
processed by the device taking the temperature measurement for the application function.
Some, usually more complex and expensive, sensors may have built-in functions for converting
the measured physical parameter directly to a number that is sent to the application processor or
communications module for transmission.
For example, a GPS device may report continuous position and time readings on a serial port
using the common National Maritime Electronics Association (NMEA) formats called NMEA 0183
or NMEA2000. This signal is already conditioned in a text format that can be used by a device
processor to communicate and transmit the location data.
Simple Off and On Switches
Range of Data Values
Some sensors may
have built-in functions
for converting the
measured physical
parameter directly
to a number that is
sent to the application
processor.

When converting the analog voltage signal
from a conditioned sensor to a digital value
or number, the ADC has a pre-defined
resolution. This means that the full range of
the measured analog signal varies from a
zero value to a maximum higher numerical
value, with incremental steps defined by the
resolution of the ADC.
For example, an 8-bit ADC will convert the signal from a low value of 0 to a high value of 255 (with
integers in between) to represent the value of the analog signal in approximately equal steps. This
may quite sufficient for many IoT/M2M applications.
In other applications, it may be necessary to use a 12-bit, or even a 16-bit, resolution ADC for
the conversion. A 16-bit ADC provides a digital numerical value between 0 and 65535 based on
the input analog voltage. With higher resolutions—particularly with low signal levels, the signal
conditioning and amplification circuits may need special care to ensure that electrical noise does
not cause erroneous readings.
It is important to note that resolution is not the same as accuracy or linearity. It merely identifies the
number of steps between the lowest and the highest value being converted.
A full discussion of these concepts is beyond the scope of this book. Interested readers can refer
to the data sheets and applications notes from ADC and DAC suppliers for more information.
Quite often, the communications modules or modems—particularly cellular products used in
industrial IoT/M2M applications—have multiple input and output (I/O) pins that can provide the
conversions. This can be a simple on/off state using digital input pins, or an analog voltage reading
with an on-board ADC on an analog input pin, that is converted into a number that is transmitted
on the communications network.
Some modules also have digital output pins for setting a state external to the application—for
example, to activate a relay to turn on a light, power on an electrical device, disable a vehicle, or
perform some similar remote function.
A few modules and modems also have DACs that take a digital number received from the
communications network and output an analog voltage on an analog output pin in a specific
voltage range. This may be used where an analog voltage is used to control the position of a liquid
flow valve or the speed of a motor in an industrial IoT or M2M application.
ADC and DAC Resolution
Modules or External Processors
DACADC
Figure 8: ADC and DAC resolution

As described earlier, a simple application (such as a photocell
that controls a lamp to turn on or off based on ambient
light) may not need an accurate reading or sensor value.
However, when accuracy is important for an application,
calibration of the sensor signal may be needed to ensure that
the data reading is accurate to the required degree.
For example, the semiconductor temperature sensor
mentioned earlier can provide a reading of 1 microAmp
per degree Kelvin for the environment it is in. However,
does a reading of 273 microAmps actually mean that the
temperature is exactly 273 degrees Kelvin (0 degrees C)?
Or can the reading be incorrect to a certain degree of error?
Without calibration, it is difficult to be completely certain,
although it is a good estimate of the temperature.
Other temperature sensors are even more problematic. For example, an RTD can be a hundred
times more accurate than a semiconductor temperature sensor, but without correction, its
readings are quite useless. The RTD requires precise signal conditioning, linearization, and
calibration to achieve that accuracy.
These corrections are often applied digitally, as when the reading from an RTD is first converted
to a digital value, and then the correction is applied. Different types of RTDs need different types
of corrections. For example, a platinum RTD has two distinct relationships to temperature with
different polynomial equations describing its resistance above and below 0 degrees C.
In an RTD, to achieve the best accuracy, the measured signal can be corrected using a variety of
techniques: direct math, single linear approximation, or piecewise linear approximation. Each has
its advantages and disadvantages.
It is beyond the scope of this book to describe the specifics for correcting the readings from
sensors (for example, for correcting an RTD measurement). Suffice it to say that developers
designing IoT/M2M applications must take linearization and calibration into account for the
specific needs of their application, particularly if the resulting accuracy is important to the
function and features.
CALIBRATION AND LINEARIZATION
To achieve the
best accuracy, the
measured signal can
be corrected using a
variety of techniques:
direct math, single
linear approximation,
or piecewise linear
approximation.

In industrial and simple applications, many standard sensors have been developed and
commercialized over the years. These have evolved and improved over time. The costs and sizes
of sensors have been reduced with increasing efficiency and practicality.
Recently however, particularly with the start of the IoT/M2M revolution, there has been a great
demand for a variety of new parameters to be measured at ever-larger scale and lower cost.
The healthcare industry is among those at the forefront of this revolution. New methods to
measure human medical parameters are being researched and commercialized, and this has seen
an explosion of new techniques (and sensors) to measure these parameters. In medical monitoring
applications, the need for new measurements, reduction in size of devices, and adoption of
wearable fitness and medical products, is driving significant research and growth.
Beyond the sensors that are incorporated into hand-held or wearable products (e.g.,
smartwatches, clothing, and bracelets) for reading basic body functions or medical monitoring
products (such as continuous blood sugar monitors and insulin dispensers), there is also a need
for semi-permanent sensors implanted within the human body. The research into tiny, implantable
sensors has been energized by the availability of semiconductor and MEMS solutions including for
mission-critical applications such as cardiac monitoring and vision correction.
For example, medical startups are developing MEMS sensors that are implanted into pulmonary
arteries using cardiac catheter procedures similar to angioplasty. These sensors can measure
artery pressure and transmit the readings to a nearby wireless device by the patient at home, and
the readings can then be wirelessly sent to a database for review by medical practitioners.
These new sensors, and devices using these sensors, that have been commercialized in the past
five years and those that will also be introduced in the next decade, will completely revolutionize
the medical health care industry in ways that we cannot even imagine today.
Specialized Sensors

CH 5: Scheduling, Encoding, and ProcessingThe Definitive Guide | The Internet of Things for Business, 2nd Edition. | 43
CHAPTER 5
Scheduling, Encoding,
and Processing
44 SCHEDULING, ENCODING,
AND PROCESSING
45 Data Transmission Schedules
47 UDP or TCP
48 Content Encoding
52 Gateways
52 Application Servers
53 Cloud Computing
54 Fog Computing

The Definitive Guide | The Internet of Things for Business, 2nd Edition. CH 5: Scheduling, Encoding, and Processing | 44
As mentioned in the previous chapter, data and sensor readings are generally transmitted to
Internet of Things and machine-to-machine application programs for processing, storage, and
business actions.
This may be a relatively short-range transmission. The sensor readings can be delivered to a
smartphone application using a short-range wireless technology such as Bluetooth, ZigBee, or
Wi-Fi, for an action by the owner of the smartphone. For example, a heart-rate monitor may send
heartbeats-per-minute to a smartphone application during exercise, and this can be monitored to
modify the specific physical activity. The data can be logged by the application to ensure that the
desired fitness goals are being met.
For other IoT/M2M applications, the data may be sent over a longer-range transmission to servers
and programs, where it is processed for actions or stored for analytics. The data (or patterns in
the data) may lead to business actions if appropriate for that specific application. For example,
an airbag deployment notification from a vehicle can be sent to an automotive Telematics Service
Provider (TSP) that contacts the driver and connects them to public safety personnel for dispatch
of emergency services.
This chapter describes the systems and methods used to encode, transmit, store, and process
the data in a server application.
CHAPTER 5
Scheduling, Encoding,
and Processing

If possible for
an application,
randomizing the
transmissions can
have a major impact
on the capacity
requirements of the
connectivity and the
server systems.
Devices may transmit their data in real-time, or a scheduled
rate, or when the device firmware requests a report of an event.
Devices that send their data continuously in real-time or
near-real-time are “streaming” applications. The processing of
this streamed data requires systems capable of handling the
throughput from a large number of devices, particularly if the
content is to be analyzed in real-time for specific actions at a
remote site.
The cost of transmitting real-time streaming data on
“metered” communications networks that charge for “quantity
of bytes sent” may be prohibitive for many applications.
DATA TRANSMISSION SCHEDULES
In some applications, devices transmit on a regular schedule—sometimes sleeping to conserve
power until they are woken up by scheduled timers or to report an event.
Devices with accurate time (such as those equipped with GPS capability) must be careful when
using regularly scheduled transmissions. In large deployments, if all devices were to wake and
transmit at exactly the same time, the simultaneous connection attempts could overwhelm the
connectivity paths and the server systems that receive and process the data. If possible for an
application, randomizing the transmissions can have a major impact on the capacity requirements
of the connectivity and the server systems.
There are simple ways to achieve this randomization. For example, a device identification
number—such as the last four digits of the Mobile Directory Number (MDN) in a CDMA cellular
device, modulo 3600—can be used to select the “number of seconds past the hour” when a
regular transmission is sent.
Scheduled Transmissions

In most IoT/M2M applications, it is typical for the device to transmit “on demand” when an event
requires it to do so. For example, a business or residential security system may transmit a signal
when a break-in occurs; a car may transmit an accident notification when an airbag deploys or
when the driver pushes a concierge button for assistance. These are generally sporadic enough or
spaced temporally sufficiently well that they do not create traffic (and server) spikes.
Often, devices that transmit to report sporadic events are also set to transmit a periodic
“heartbeat” to report their condition and health. These heartbeat transmissions should also be
randomized.
Transmit On-Demand
Figure 9: Transmit on demand
Utilities
Enterprise
Retail
Sensors
Vehicles MedicalMobile
Devices

Using these two
protocols is not
mutually exclusive
for a given IoT/M2M
application.
We are often asked whether a device should use User
Datagram Protocol (UDP) packets or use Transmission Control
Protocol (TCP) streaming sessions for the data. The answer, not
surprisingly, is: “It depends!”
The Internet Engineering Task Force (IETF) has detailed
definitions, but let’s briefly describe these two protocols to
understand why one may be better than the other for certain
M2M and IoT data transmissions.
It is important to note that both UDP and TCP are used over an
underlying Internet Protocol (IP) connection.
UDP OR TCP
The UDP format was first defined in an IETF Request For Comment (RFC) specification: RFC
768. This protocol provides a procedure for applications programs to send messages to other
programs with a minimum of protocol overhead. This protocol is transaction-oriented, and delivery
and duplicate protection are not guaranteed.
If an application requires ordered, reliable delivery of streams of data, UDP is not the preferred
protocol. However, the format has lower overhead than TCP—i.e., fewer bytes are sent in the
headers of the packets in UDP than TCP.
The TCP format was first defined in an IETF RFC specification RFC 761. TCP is a connection-
oriented, end-to-end reliable protocol and is intended for use as a highly reliable host-to-host
protocol between hosts in IP networks and especially in interconnected systems of such networks.
TCP requires that a connection be opened and managed for the duration of the IP data
transmission. Within the protocol, transmitted and received packets are acknowledged by the
device and the servers.
This format has more overhead than UDP—i.e., more bytes are sent in the headers of the packets
in TCP than UDP.
User Datagram Protocol (UDP)
Transmission Control Protocol (TCP)

In general, the choice of UDP vs. TCP must take into account:
• The desired balance between the reliability of TCP and the lower cost of UDP, since UDP uses
fewer bytes of overhead to transmit the same amount of application data.
• The increased complexity of TCP, where the module must open a data stream to a remote
server where programs await connections.
• Careful design of TCP server programs to allow easy scaling as the number of deployed
devices increases.
• A requirement for the acknowledgments provided by TCP sessions.
However, it is also important to note that using these two protocols is not mutually exclusive for a
given IoT/M2M application.
For some uses, a simple transmission of a UDP packet to a remote host may be quite sufficient—
including using independent acknowledgments via UDP. If an acknowledgment is expected, but
not received, either side can retry…intelligently (i.e., with limits on number of retries, variable delays
between retries, etc.)
For other uses, even in the same application perhaps, a device may open a TCP connection to a
server and communicate with the higher reliability of a TCP streaming session to a program that
accepts these connections and transmissions.
Often, the amount of data may require TCP. For example, if a device needs to transmit a large
file (i.e., more than a kilobyte), it is better to use TCP, since the consequences of an error during
transmission via UDP could mean that the entire file might require a complete retransmission.
Which to Use?
When a device transmits its data to the servers and receives
commands and instructions from the servers, there is a format
required for the information sent in both directions. In every
application, the devices and servers must agree on the format
and information that is sent.
CONTENT ENCODING
These protocols
fall into two basic
categories: human-
readable (JSON,
XMPP) and non-
human-readable
(CoAP, MQTT).

Devices for a particular application and the servers may use a proprietary format for the data encoding.
This allows the devices and servers to encode and interpret the content in ways unique to the needs
of that application and can often minimize the amount of data sent in that connection session.
Proprietary formats are more difficult to implement initially—since they must be relatively complete
for that application to be deployed—as well as difficult to maintain and update later when changes
are needed. Most proprietary formats tend not be extensible.
In addition to proprietary formats and early standardized formats such as Extended Markup
Language (XML), there are some industry formats and protocols in use for IoT/M2M data
communications for messaging needs, for example:
• JavaScript Object Notation (JSON)
• Constrained Application Protocol (CoAP)
• Message Queuing Telemetry Transport (MQTT)
• Extensible Messaging and Presence Protocol (XMPP)
These protocols fall into two basic categories: human-readable (JSON, XMPP) and non-human-
readable (CoAP, MQTT).
The human-readable ones are generally much more verbose, but easier to debug during
development. The other, non-human-readable, ones are lighter-weight and efficient and can
minimize the amount of data sent over the communications path.
Each format and protocol has its pros and cons when used for IoT. The specific choice depends
on the needs of the application, the bandwidth of the communications network, the computing
power in the sensor or remote device, and other factors.
Proprietary Formats
Common Industry Formats for IoT
XML
JSON
CoAP
MQTT
XMPP
IoT
M2M

JSON is an open-standard format that sends key-value pairs of information. The “key” is generally the
attribute or description of the content sent in the “value.” The protocol is described in RFC 7159 from
the Internet Engineering Task Force (IETF). See www.ietf.org/rfc/rfc7159.txt for more information.
The JSON format is human-readable and language-independent, and public code for parsing and
generating JSON text data is readily available in a variety of programming languages. The format
is effectively self-describing, since the definition and value are right next to each other.
For example, the following simplified text illustrates the encoding of a temperature reading of 25
degrees Centigrade from a sensor with a hypothetical sensorID of 123456789:
{
“sensorID” : “123456789” ,
“temperature” : “25” ,
“units” : “Centigrade”
}
As you can see, the JSON content is verbose and very human-readable. The key-value pairs
immediately identify the attribute and its value.
JSON format messages can also be extended readily. For example, the physical location and
manufacturer might be added, along with a time-stamp noting the time that temperature was
measured.
Of course, the presence of this additional information depends on whether it should be
transmitted. In the above example, the sensorID could be used to look up the location and
manufacturer in a server database (assuming it was stored there at installation of the sensor).
However, sending the time-stamp from the device can be more useful since it provides the time
when the data was collected (assuming the device knows that time information, of course).
As the name implies, CoAP is a format and protocol intended for use in bandwidth limited
networks or where minimizing the size of each message transmission is important. The core of the
protocol is described in RFC 7252 from the IETF, although extensions to add unique requirements
for IoT/M2M are currently in progress. See http://www.ietf.org/rfc/rfc7252.txt for more information.
CoAP is a simple protocol that is well-suited for transmissions from small electronic components,
such as sensors, and can also be used to control the devices from remote servers. CoAP also
includes the concept of “multi-cast” (or “one to many”) group communication, where many
devices can receive the control information at the same time.
Proprietary Formats
COAP

MQTT is another light-weight messaging protocol that is designed for data transmissions from
devices operating in bandwidth limited networks. The devices transmit the data to message
brokers that are then responsible for sending the content of the messages to clients who are
interested in that data and who subscribe to the feed.
This mechanism is the essence of a “publish-subscribe” approach, where data from the devices is
published to a broker, and subscribers to that broker can access the data.
Originally developed by IBM, the MQTT protocol was transferred to the OASIS standards body
and is now supported by that entity. See http://www.mqtt.org for more information.
MQTT was originally designed for the IoT/M2M markets for devices transmitting using TCP/IP. To
allow simpler electronic devices (such as sensors) to use this protocol, a version called MQTT for
Sensor Networks (MQTT-SN) has also been released to extend the protocol beyond TCP/IP.
XMPP is an open-standard communications protocol for messages based on XML. It is intended
for near-real-time exchange of messages between two (or more) elements on any network. Like
XML, it is extensible and can also be used for publish-subscribe message systems.
There are multiple RFCs from the IETF that specify the XMPP standards: the core ones are RFC
3922, 3923, 6120, 6121, and 7622 (see http://www.ietf.org), although the XMPP Standards
Foundation (see http://www.xmpp.org) is also actively extending XMPP further.
The XMPP protocol evolved from an earlier open-standard protocol called Jabber and was used
for Instant Messaging (IM) services as well as Voice over IP (VoIP) control messages. In this last
application, XMPP competes with the Session Initiation Protocol (SIP).
When XMPP extensions are used for publish/subscribe services, they are useful for IoT/M2M data
applications. However, like JSON, they are human-readable and verbose—perhaps even more
verbose than JSON due to the XML roots. This may make it difficult for a small sensor to encode
XMPP directly, but a communications device could make the necessary conversion from raw
sensor data.
In XMPP, binary files and content can be encoded (using base64 conversion of the binary data to text)
and sent using XMPP, but this is likely to be more overhead than is desirable for IoT/M2M applications.
The protocol provides two types of message: requests and responses using a “type-length-value”
(TLV) coding that is different from the JSON format. These CoAP messages are sent using a UDP
transport to adhere to the concept of low overhead for the messages.
MQTT
XMPP

In most low-cost sensors—even newer ones that “speak IP”—it is difficult to provide the
data encoding and decoding capability within the sensor. Often, the sensors use short-range
communication paths—either wireless or wired—to a device with more computing capacity to actually
encode the data and transmit to a remote server.
This device may be a unit serving a single sensor and associated application. More often, a gateway is
a product with multiple short-range wireless and wired connections to local sensors and a long-range
wireless or wired connection to the remote servers.
For example, gateways used in home-automation applications
communicate with sensors using Bluetooth, ZigBee, and
Wi-Fi, and to the remote servers with cellular or wired
Ethernet connections. The gateway is a good location in the
communications path to implement the data encoding as well
as security best practices, with software agents that take the
raw information from the sensors and encode the data in the
formats described above.
The remote data is transmitted to application programs running on the servers that may be dedicated
to the task of processing that data—whether it is streaming data or message oriented.
Typically, these servers are deployed in data centers on the customer premises or in data centers. The
programs on the servers receive the data and process them for the specific business action of the
IoT/M2M application. This may include storing the data in traditional databases, filtering for erroneous
information, alerting when the information is outside pre-determined bounds, displaying the data,
reports, etc. The needs vary greatly.
Often, remote devices, even those that are transmitting lightly, cannot tolerate server downtime for
any significant duration. Processes and network infrastructure to automatically balance the loads on
redundant servers, including at multiple sites, are critical.
For large-scale deployments, the application servers must literally be running continuously with high
availability and processing redundancy (including geographic redundancy), particularly for mission-
critical applications. With the projected growth of the IoT/M2M market, this will place an immense
burden on servers and data centers. This creates a capital and operation cost of systems, physical
site maintenance, power distribution, cooling, etc.
GATEWAYS
APPLICATION SERVERS
The gateway is a
good location in the
communications
path to implement
the data encoding as
well as security best
practices.

The choice of which server platforms, operating systems,
programming languages, etc., is entirely dependent on the
entities deploying the IoT application. Traditional IT departments
have all the relevant expertise to make these decisions.
However, in most cases where massive growth is expected
to occur, IoT/M2M deployments often should consider taking
advantage of newer IT methods like Cloud Computing and
data traffic reduction methods such as Fog Computing.
IoT/M2M deployments
often should consider
taking advantage of
newer IT methods like
Cloud Computing.
In recent years, the phrase called “cloud computing” or simply “the cloud” has been coined to
describe the systems that allow processing and storage of information and data in extremely large
data centers for a fee. Cloud vendors provide the ability and flexibility to start and stop computing,
storage, and networking resources based on the specific needs of the customers and applications
using these cloud services.
This has transferred the need for entities and corporations to maintain their own physical hardware,
data centers, and data networks, etc., to the cloud providers. This eliminates traditional operational
burdens of physical site maintenance, electrical power management, environmental conditioning, and
system redundancy.
The specific compute, storage, and transport requirements for the cloud customers can then be
adjusted fairly dynamically to conform to the needs of the applications being executed. The latest
techniques and software for managing the large amount of data can be applied to the data gathered
from the devices in the IoT/M2M applications.
These compute elements, storage, and data transport are, of course, provided for a fee. The charges
can vary, but can often be high for large-scale applications and large numbers of device deployments.
CLOUD COMPUTING

Some data stored
Some data processed
and used in real time
IoT endpoints
real time
Data
The volume of data gathered from a large number of sensors and devices could overwhelm
the communications path (transmission and connectivity) or the remote storage capacity and
systems that process the data at the customer sites.
While cloud solutions alleviate this problem, the cost could become very expensive—particularly
for streaming applications. Often, a general approach is a “transmit everything and process in the
cloud” implementation.
However, if actions based on the data must be processed in real-time or near-real-time, it may be
better to process or filter the data remotely—at the device, or elsewhere hierarchically in the data
flow before it gets to the remote storage. This remote processing and filtering has been termed
“fog computing” by Cisco.
Fog computing is not without its issues and concerns. If the filtering removes essential
information that could be better processed at a central site (such as the cloud) to determine
patterns, its use could result in a weaker application.
Sometimes, the specific filter used at the remote device may need to evolve or change. Thus the
devices must be programmable or configurable to the required degree, increasing the complexity
of the overall solution.
One significant advantage of fog computing is the concern about security. Good security practices
can be implemented farther away from the central servers, where a device (or groups of devices)
that have been compromised could result in less damage to the overall application deployment.
It also reduces the transport costs of sending a lot of data—much of which may be meaningless
or repetitive or simply not needed—on metered transports where the transport of a large set of
data could be expensive.
FOG COMPUTING
Figure 11: Fog computing

The Definitive Guide | The Internet of Things for Business, 2nd Edition. CH 6: Security and the Internet of Things | 55
CHAPTER 6
Security and the
Internet of Things
56 SECURITY AND THE
INTERNET OF THINGS
56 Privacy and Security
57 Security Objectives
59 Security Issues for IoT/M2M
61 Risk Management and
Assessing Impact of Breaches
63 Encryption as an IoT Tool
64 Choice of Encryption Algorithm

In her keynote speech at the Consumer Electronics Show in January 2015, the US Federal Trade
Commission Chairperson Edith Ramirez noted “any device that is connected to the Internet is at risk
of being hijacked.” Whether that device is a smartphone, an automobile infotainment system, an
automated diabetes monitor, or a GPS-guided farm tractor, specific protections for security of Internet
of Things and machine-to-machine devices and applications must be built into the entire solution.
Traditional financial and consumer markets have been targets for misuse of information stored
on their systems—including personal credit information, identify theft, misuse of credit cards by
unauthorized persons, personal privacy violations, and loss of corporate intellectual property. The
financial losses sustained by these security breaches are in the billions of dollars. While attempts
have been made to criminalize such nefarious activities, they continue to occur with increasing
frequency and are a serious problem for governments, businesses, and individuals.
Business deploying IoT/M2M solutions for their customers and themselves will be held responsible
for protecting data and devices, as well as corporate proprietary information. Recent media reports
of security compromises in the medical and automotive industries have shown that aspects of such
device deployments can be used for purposes other the applications for which they were designed.
This chapter covers basic requirements of security implementations and the different methods
commonly used to increase the overall security of IoT/M2M data and applications.
CHAPTER 6
Security and the
Internet of Things
In the context of IoT/M2M, privacy is concerned with ensuring
that data access is limited to the appropriate and authorized
parties only. While using tools such as data encryption is an
important part of this process, it is just one part of the puzzle,
and there are other mechanisms and methods to protect
privacy (although not just for IoT/M2M applications):
PRIVACY AND SECURITY The US Federal
Trade Commission
Chairperson Edith
Ramirez noted “any
device that is connected
to the Internet is at
risk of being hijacked.”

• Physical access security (for example, secured entrances to data centers)
• Security training (to employees on how to secure computers and devices and to understand
data safety)
• Intrusion detection (for systems that process and store the data)
• Software updates (to implement the latest versions of software for security fixes)
Individuals have an expectation of privacy with regard to their personal data, and it is crucial for
businesses to consider implement relevant security methods. In particular, financial and medical
industries have specific governmental regulations that govern their products and services in their
respective markets. The new IoT/M2M implementations that companies in these industries are
deploying may have special testing and certification requirements—particularly in regard to security
and privacy issues.
There are four overall security objectives that must
be met for IoT/M2M security implementations:
• Authenticated sender and receiver
• Sender and receiver accessible
• Trust in the data content
• Confidentiality of information
SECURITY OBJECTIVES

In any data connection, it is important for the sender and receiver of information to be
authenticated to each other, regardless of whether the device is the sender (for remote data
gathering and transmission) or the receiver (for data and control messages from the server). As a
security principle when transmitting data, the device must ensure that it is sending its information
to the correct server, and when receiving data and control messages, it must ensure that the
information is coming from the correct server.
In any network, the sender and receiver must always be accessible when needed. If the network
is not functional, or the server is not executing the correct programs to receive the data, the
purpose of the application may be lost. Mission-critical applications, such as automatic crash
notification or medical alerts, may fail to work properly if the connection is not reliable. The lack of
communication itself means a lack of security.
The accuracy in the content of the transmitted data is essential. If a device does not encode and
transmit data correctly, or the connection is not error-free, the quality and accuracy of the data
becomes suspect. Even good data becomes unreliable, and business actions that are taken on
the content of the data may not be appropriate.
Mission-critical information is particularly important to keep as error-free as possible. The cost of
business actions taken on receipt of incorrect data may be high.
Finally, the confidentiality of the information must be maintained. Only the correct recipient should
have access to the transmitted data, since it may contain proprietary or confidential information.
Indeed, privacy laws in many countries require extra care with information regarding individual
citizens—for example, in the US, the Health Insurance Portability and Accountability Act (HIPAA)
provides specific rules for individually identifiable medical information.
Authenticated Sender and Receiver
Sender and Receiver Accessible
Trust in the Data Content
Confidentiality of Information

Security risks can be recognized and understood, and the implementation of security methods should
be incorporated in the IoT/M2M device and software associated with that application. The nature of
these new deployments brings new complexities to creating secure solutions.
Most obvious holes in security can be resolved quickly and efficiently. In general, the potential for
problems can be managed with confidence in the chosen security methods. However, it is vital to
recognize that risks cannot be completely eliminated, and there is no single security solution for all
possible security requirements for all applications.
Thus, it is critical to assess the level of security implementations
that are appropriate for different kinds of data. This assessment
must be done early—during the design of the application, not
as an afterthought once many devices have been deployed!
Before choosing how to secure the application, there are a
number of issues to be considered:
• Authenticating presence on multiple transport networks
• Authorization for multiple types of services
• Scaling to manage the large number of devices in IoT/
M2M solutions
• Automation for application functionality
• Long lifecycles for deployed devices and applications
• Implementing security updates in remote devices
SECURITY ISSUES FOR IOT/M2M
It is vital to recognize
that risks cannot
be completely
eliminated, and
there is no single
security solution for
all possible security
requirements for all
applications.
Some IoT/M2M devices operate in more than one transport network or technology for redundancy
or hybrid solutions. In these devices and solutions, security may be more of a concern in one
network compared to the others. For example, a short-range wireless technology such as Wi-Fi
can have quite a different security threat vector and potential for breaches compared to a long-
range cellular service.
Multiple Networks

Applications and devices may be using multiple services, where the required authorizations for
allowing a device to access a particular service may differ from one application to another. The
authentication mechanisms may also differ, and developers must minimize the risk of a less secure
service authentication system from allowing a device to be compromised.
In IoT/M2M deployments, there are predictions of explosive growth in the near future—billions of
potential devices within the next 5 to 10 years. Thus, in any application where a security problem
exists, the overall problem could be greatly magnified by the large numbers of devices that may
be affected. This could result in network and data security issues that are difficult to solve, since
replacing all the compromised devices could be extremely difficult, perhaps impossible.
In many IoT/M2M applications, the data is acted upon by automated programs that process
the received data and take business actions based on the content. If the transmitted data is
compromised, any simplistic responses or automated functions to that compromised data could
cascade into difficulty. If some set of devices transmit excessively due to a program error, the
servers processing that incoming data could overload and not provide a response to the devices.
Simplistic retry algorithms in the devices may create a data storm as a result.
Unlike handsets used by people who change them every
few years on average, IoT/M2M devices—particularly in
industrial applications—may be deployed for many years
and operate relatively continuously over that time. Often,
the devices use electrical power rather than batteries
(unlike handsets that shut down when battery energy is
depleted), and the IoT/ M2M devices could continue to use
the networks for years. Devices with compromised security
could stay operational for lengthy periods.
It is essential to plan and design for device updates over-the-air (OTA). When a device security
breach is sufficiently critical that the device programming must be updated, the ability to re-
program the functionality remotely is vital. The devices may be in inaccessible locations or a large
number of devices must be modified rapidly.
Multiple Types of Services
Scaling Growth
Automated Functionality
Long Lifecycles
Remote Updates
When a device security
breach is sufficiently
critical that the device
programming must
be updated, the ability
to re-program the
functionality remotely
is vital.

For some data, the issue of security may not be as critical.
For example, if an IoT/M2M application device is collecting
temperature information from a residence for monitoring
(not control) purposes, the security needs for this data is not
as high as a device that collects and transmits credit card
information.
Thus, the effort and level of security implementations and
methods necessarily differ for these two examples. One
may require anonymizing the data source for simplicity and
privacy, and the other may require data encryption to prevent
unauthorized access to the data.
In all IoT/M2M deployments, it is important to assess the potential for damage caused by a
security breach, and implement security solutions accordingly. Ask the following questions, to
start:
• If a single device is compromised, can it be used to compromise other devices? The data
transport used by that application? The remote application servers? That entire application?
• If an application is compromised and misused, what impact does that security event have?
Is it life-threatening to one individual? To more than one individual? An entire population in a
region?
• Can a data content breach cause financial harm to an individual? More than one individual?
The entire set of people depending on a particular IoT/M2M application to function well?
• How quickly can the specific breach or intrusion be detected? Is it using a well-known target
mechanism (such as might exist in a widely used cellular device operating system)?
• Can a compromised device or set of devices be isolated from the application rapidly?
The opportunities for implementing security best practices occur at different points and differing
capabilities in the IoT/M2M data chain. IoT/M2M developers should assess the opportunity for
implementing security best practices (authentication, encryption, etc.) at every point during the
design of the application.
For example, the source of data could be a sensor. These are not likely to be compromised easily,
since they are so specific to their function, but they still need protection. Because of the simplicity
of such sensors, it is often difficult to implement a security solution for them.
RISK MANAGEMENT AND ASSESSING IMPACT OF BREACHES
Even if we could
determine all possible
threat vectors, the
cost of designing
preventative measures
to counter every threat
might be prohibitively
expensive.

SENSORS
SMART
HYBRID TECH
GATEWAYS
SHORT RANGE
Zigbee, Wi-Fi, etc
MEDIUM RANGE
Cellular
Low-power RF
LONG RANGE
Satellite
NON-WIRELESS
IDENTITY MGMT
Provisioning
Authentication
Authorization
Access Control
INFORMATION
Control Messages
Data Transport
SERVERS
CORPORATE
NETWORK
ACCESS
CONTROL
HUMANS
PROCESSES
AUTOMATION
INTERNET
VPN
POINT-TO-POINT
OTHER
SOURCE TRANSPORT NETWORK
INFRASTRUCTURE
DATA
COLLECTIONS
HOST
SYSTEMS
RECIPIENT
However, a compromised sensor could be used to inject false data into the application, where an
incorrect action could be taken by a server or human at the remote end of the chain.
A more complex source device, such as a multi-technology gateway connecting to multiple types
of sensors, or a cellular modem, offers more opportunity—both for breaches to occur, as well as
a location in the chain for implementing a good security solution. For example, a gateway device
could have the compute capacity to implement strong encryption algorithms, thus securing the
content further along the chain.
In general, the “further towards the device” that security best practices can be implemented, the
less impact a security breach can have on the overall application.
Each business and its IoT/M2M application implementations will require its own risk assessment
to determine the relevant security needs, and organizations have to understand the trade-offs
they make upfront. It is simply impossible to determine all possible methods by which all such
applications could be compromised.
Even if we could determine all possible threat vectors for a particular application, the cost of
designing detection and preventative measures to counter every threat might be prohibitively
expensive for that application. The best we can do is understand and minimize the risk as best
as we can upfront and design the devices and application processes to be as easily updatable as
possible.
While server programs and accessible elements of the data chain can be updated more easily, the
ability to re-program the devices using OTA updates is key to ensuring that the impacts of security
breaches can be contained and repaired.
Figure 13: Data security flow

One of the most basic technological tools to secure the
content and data in an IoT/M2M deployment is to encode
the data so that only the authorized recipient (whether
program or human) can decode the data.
After the data is gathered and transmitted by the remote
device (or is sent by the server to the device), the content
can be encrypted at various points along the network and
also when the data is stored.
The basic goals of encryption are to provide:
• Proof that the sender is valid—Encryption can make the data’s source irrefutable.
Techniques such as electronic signatures on a document can be a sign of irrefutability. Proof of
who sent data is crucial so that a hacker doesn’t steal a session and then pretend to be that
user; this is called spoofing.
• Proof that data was not altered—Encryption functions can be used to ensure that a
change to the data renders the content unusable.
• Proof that data cannot be read by a third party—Encryption protects data from being
read in transit or upon receipt, except by someone with the correct decryption method.
Data encryption can protect the content in each of these areas to different levels, depending on
the need and the specific type of encryption that is used.
ENCRYPTION AS AN IOT TOOL
The Heartbleed
security bug in
OpenSSL, discovered
in 2014, affected about
17% of the world’s web
servers.
No encryption method is perfect. Depending on the computing power available at a particular
location or the time used by the encryption method, the algorithm may be weak or strong.
Strong algorithms may seem impossible to break, but applying enough compute resources to the
task could reveal weaknesses that allow the data to be decrypted by unauthorized systems or people.
Indeed, bugs may be discovered in the method itself, or in the particular software implementation.
A recent example is the Heartbleed security bug in OpenSSL discovered in 2014. This affected
about 17% of the world’s web servers and potentially allowed encrypted data to be read. Patches
were made to OpenSSL, and a majority of web servers have since been updated.
Weaknesses in Encryption

It is beyond the scope of this book to describe or recommend a particular encryption algorithm.
The specific requirements of the IoT/M2M application or the computing power available at a place
in the data chain may drive a preference for a particular algorithm.
Security experts can provide guidance for selecting an approach and should be consulted during
the design of the application.
The information technology departments at each company may also have specific encryption and
security requirements—for example, the use of Virtual Private Networks (VPN) to transport data
into its servers for processing and storage.
CHOICE OF ENCRYPTION ALGORITHM

The Definitive Guide | The Internet of Things for Business, 2nd Edition. | 65
CHAPTER 7
IoT Scalability and
Alternative Technologies
66 IOT SCALABILITY AND
ALTERNATIVE TECHNOLOGIES
68 What Is Scalability?
70 End-of-Life Management
70 Scalability and Connectivity
CH 7: IoT Scalability and Alternative Technologies

The Definitive Guide | The Internet of Things for Business, 2nd Edition. CH 7: IoT Scalability and Alternative Technologies | 66
Over the years, the predictions for growth in the Internet of Things and machine-to-machine
markets have been staggering:
• 2010, IBM: “A world of 1 trillion connected devices” by 2015.1
• 2010, Ericsson’s CEO, Hans Vestberg: “50 billion connected devices” by 2020.2
• 2013, ABI Research report: “30 billion devices” connected to the IoT by 2020.3
• 2013, Morgan Stanley report: “75 billion devices connected to the IoT” by 2020.4
• 2014, an Intel infographic: “31 billion devices connected to Internet” by 2020.5
• 2014, ABI Research updated report: “40.9 billion active wireless connected devices” by 2020.6
• 2015, Gartner Research: “6.4 billion connected ‘things’ will be in use worldwide in 2016…and
will reach 20.8 billion in 2020.”7
• 2016, an Intel infographic: “A projected 200 billion smart objects by 2020.”8
Although the specific predictions and the numbers differ, what is remarkable is that the numbers
predicted for 2020 have been consistently extremely high over the years. The markets are
experiencing explosive growth around the world, and the numbers are still performing at what
Gartner calls the “peak of inflated expectations” in its well-known “Hype Cycle” diagrams. The
Gartner Hype Cycle showed the Internet of Things had hit the peak of this curve in 2014, so we
appear to finally be moving beyond the hype into reality.
1 “IBM: A World with 1 Trillion Connected Devices,” ReadWrite.com, June 7, 2010.
2 “CEO to Shareholders: 50 Billion Connections 2020,”Ericsson.com, April, 13, 2010.
3 “More Than 30 Billion Devices Will Wirelessly Connect to the Internet of Everything in 2020,” ABI Research, May 9, 2013.
4 “Morgan Stanley: 75 Billion Devices Will Be Connected to the Internet of Things by 2020,” Business Insider, October 2, 2013.
5 “The Internet of Things in 2020,” VisualCapitalist.com, August 23, 2014.
6 “The Internet of Things Will Drive Wireless Connected Devices to 40.9 Billion in 2020,”ABI Research, August 20, 2014.
7 “Gartner Says 6.4 Billion Connected ‘Things’ Will Be in Use in 2016, Up 30 Percent From 2015,” Gartner, November 10, 2015.
8 “A Guide to the Internet of Things Infographic,” Intel.com, 2016.
CHAPTER 7
IoT Scalability and
Alternative Technologies

Even if the huge numbers forecasted are inaccurate by large percentages, or even a factor of
10 or more, they still represent enormous growth. Indeed, the estimated number of connected
devices by 2020 exceeds the projected population of the entire planet by many multiples.
This explosive growth needs to be managed and planned, if we are indeed going to come close
to the predictions for what these markets and industries can do for all of us. Furthermore, cellular
is not likely to be the dominant data transport for these large volumes. Indeed, it may account for
less than 10% of the total devices deployed in IoT/M2M applications.
All of this anticipated growth in the IoT/M2M market will bring new challenges:
• Scaling for the growth in the numbers of devices and applications
• Providing effective security solutions for the content and solutions (as discussed in the
previous chapter)
• Storing the data and providing rapid analysis for action
• Deploying new wireless and wired connectivity technologies for the increased traffic
• Managing the connectivity and device “subscriptions” for large numbers of devices
Therefore, this chapter will also briefly review some of the alternative technologies that are likely to
be used for large-scale IoT deployments.
Figure 14: Gartner Hype Cycle, August 2014

In the context of IoT/M2M, scalability is the ability to
grow the application, the solution, and platform to keep
up with the projected growth in the number of devices,
the data traffic from these devices, the applications
servers that process and store the received data,
the real-time (or near-real-time) streaming data alert
systems, the pattern and predictive analytics, etc.
Essentially, this is the ability of the IoT/M2M ecosystem, both for any given application and all such
applications in general, to grow at the same rate as the predictions—to make them a reality rather
than hype. The demand for IoT/M2M applications, devices, and services will continue to grow
exponentially, and companies with connected devices will need to scale their resources accordingly.
Solutions for managing the application must be scalable and designed for growth. For example,
most IoT/M2M platforms let the customers rapidly provision the cellular devices for service at
volume. Requests are not sent in by humans; rather, automated systems make the requests, and
automated systems process the requests.
WHAT IS SCALABILITY?
Successful organizations
plan for the entire
application lifecycle, from
development to operation
to scaling to end-of-life.
Many companies run into difficulty after deploying their first few hundred or thousand IoT/ M2M
devices—sometimes, rarely, after tens of thousands of devices. This is not totally surprising,
because planning for scalability is difficult and involves many factors, both technological and
business-related.
Sometimes, systems and processes simply reach design capacity, and it is time-consuming and
costly to change the architecture of the solution or add capacity.
Or the cost of operations becomes higher than expected or planned for, which has a deep impact
on smaller companies and startups that are resource-constrained. Even seemingly simple tasks
such as generating end-user bills and invoices can place unexpected burdens on organizations
when scaled up.
The key issue for businesses caught in this growth stall is that planning for growth was secondary
to getting their products and services launched. This is quite common, but it doesn’t have to
happen. Successful organizations plan for the entire application lifecycle, from development to
operation to scaling to end-of-life.
THE GROWTH STALL

The predictions for deployed numbers of devices are clearly enormous numbers. This has created
a need to change some of the resources used for IoT/M2M applications.
Even before the IoT needs became evident, the number of computer systems on the public
Internet had increased to the point where the Internet address and numbering method called IPv4
had been exhausted some years ago. The approximately 4 billion possible IPv4 addresses had
essentially been used up, as discussed in earlier.
And, with the ever-increasing number of IP devices, including cellular smartphones that need an IP
address, it is no longer possible to consider using stop-gap measures such as Network Address
Translation (NAT), which were introduced for the Internet, for IoT/M2M devices.
Thus, IPv6, which was introduced to increase the number of potential addresses, is a
requirement for all future deployments. In theory, this range is large enough that it is unlikely to
get exhausted for millennia.
Computing resource can also be scalable, particularly if the device traffic and application
processing can be stored and processed. New databases technologies have been deployed that
are far more expandable than the traditional databases used in the past three or four decades for
data processing.
As mentioned in previous chapters, cloud computing technologies have provided a scalable
solution for storing and processing the data gathered by IoT/M2M devices.
Since the numbers of devices are growing rapidly, systems to process the data must grow equally
quickly. Adding capacity at private data centers is not easy for most companies, since purchasing
the physical space, providing for additional power and cooling, increasing the network throughput,
installing the computing systems, etc., can take significant effort and time.
Cloud computing suppliers excel at this task. It’s their business to provide the compute, network,
and general facilities for exactly this growth purpose. Customers using cloud services can “spin
up” resources as needed, in step with the IoT/M2M application growth.
HOW BIG CAN IOT RESOURCE REQUIREMENTS GROW?
CLOUD COMPUTING REVISITED

Many M2M and IoT devices have an end-of-life that must be managed. The in-service period is
generally much longer than the typical period we expect for electronic devices today, particularly for
industrial applications. But, once the end-of-life of a device, or all devices within an application, is
reached, their removal from service must be managed, to avoid tying up resources.
For example, in cellular networks, devices have a number that identifies them to the network for
operational, accounting and authentication purposes. In CDMA, this is the Mobile Identification
Number (MIN) or Mobile Directory Number (MDN); in GSM, this may be the International Mobile
Subscriber Identity (IMSI) or the Mobile Station ISDN (MSISDN).
These numbers are often from an allocated range, or
number pool, and is a resource that must be managed.
Ideally, the numbers are re-used when devices are
removed from service permanently.
Devices removed from business service may still have a
presence on the networks and impact overall network
performance if they are still electronically operational. For
example, cellular devices used in automotive applications can
be removed from service but could still attempt to “register” on
the cellular network every time the vehicle is turned on and off.
Thus, it is important for devices to have an ability to be turned “off”—permanently or temporarily—
with code in the firmware and software of the device. This would allow the application servers to
effectively remove the device from service, and in the case of permanent removal, allow the device
resources (such as numbering) to be re-used for other devices or applications.
When building scalability into an IoT/M2M deployment, selecting appropriate network connectivity
is crucial. The range of available data transport technologies for IoT/M2M devices is varied, and
new options are becoming available. When planning for scalability, it’s important to understand
current choices and what’s on the horizon. However, this decision is largely dependent on the
type of application. The first question to be resolved is whether the application is fixed or mobile.
For simplicity, IoT/M2M applications can be classified into two categories: those that are fixed
in one location and those that are physically in motion, while providing the function of the
application. These two categories have differing characteristics that affect the specific network
selection and implementation for the transport of data from the devices.
END-OF-LIFE MANAGEMENT
SCALABILITY AND CONNECTIVITY
Once the end-of-life of
a device, or all devices
within an application,
is reached, their
removal from service
must be managed,
to avoid tying up
resources.

In fixed location applications:
• The devices are installed at a single location.
• They generally do not move during the normal day-to-day operation of the applications
(although they could be re-installed at some other location during their lifetime).
• During this operation, they are generally in a single service boundary.
• The devices often use wired networks in deployments where easy wiring solutions are available.
• Wireless networks are also used, however, since network wiring may not be convenient
or available.
• The solutions may be hybrid: using short-range wireless to reach a WAN gateway that uses a
cellular or wired connection to connect to the servers.
Fixed location devices are often wired. This could be with a Local Area Network (LAN) such as
Ethernet using IP protocols. Older deployments used dial-up telephone lines to reach a remote server
directly or connected to the Internet, and cable modem connections are also used where available
(also using IP protocols).
In physically mobile applications:
• The devices are installed on moving objects to provide the functionality.
• They physically move from one place to another during the normal operation of the applications.
• During this operation, they often traverse multiple service boundaries (for example, cellular
switch boundaries).
• Using some form of long-range wireless network is natural and required.
• In this category, using cellular or satellite networks is quite common.
• For some applications that must transmit while traversing service boundaries, the technology
must be a Wide Area Network (WAN) with mobility management.
Cellular
LPWAN
Wired
ZigBee
5G
Wi-Fi
Bluetooth

For short-range data transmissions, where using a wired solution may not be practical, wireless
technologies such as Bluetooth, Wi-Fi, ZigBee, etc., are quite popular. These are common
industry standards for which low-cost implementations of the wireless radio and their protocols
are available in integrated circuits. The low cost of these short-range wireless technologies enables
using them directly within sensors.
These short-range wireless technologies are generally quite limited in range—from a few meters to
a few hundred meters. If the data needs to go further, the short-range communication is typically
sent to a gateway modem that then connects to the servers using cellular, cable or some other IP
network transport.
For medium ranges (for the wireless transport), typical implementations of IoT/M2M solutions use
cellular for communication to a nearby tower (generally within a few miles) that then backhauls the
data into the Internet or a remote server.
When cellular is not available, such as on ocean-bound ships or remote geographies with
low human presence, long-range satellite data services provide a global reach for devices to
communicate to a distant server program for that IoT/M2M application.
Whichever of these two categories the implementation falls into, fixed or mobile, will drive the
selection of the network and communications path for the application.
Wired connections are typically used for fixed location IoT/M2M applications. For reaching a server,
the cost of the transport is “shared” with general Internet access. For many device deployments,
this is often a very low-cost solution, since the ISP generally does not charge a metered rate—i.e.,
the fairly low amount of data sent by the devices at a fixed location does not trigger a high cost.
With wired connections, the overall service requires an ISP service or another LAN. The quality
of the service and general network availability also depends on the ISP. If it is not able to provide
continuous service, some mission-critical applications may experience problems with outages.
Service coverage and availability for cellular and
satellite are generally excellent. Cellular service
is available wherever people live and along major
highways in most countries.
Wired Data Connections
Cellular and Satellite Connectivity
A hybrid cellular-satellite
device, with multiple
radios, can provide truly
global data access.

If cellular is not available in a truly remote location, such as an ocean-bound ship or in mountain
regions, the coverage from satellite data services is excellent, although some of the satellite
services may have relatively higher latencies (the time for a data packet to traverse end to end)
than other technologies. Coverage inside “urban canyons” with tall buildings is usually difficult for
satellite data services, but this is where cellular services can excel. If required, a hybrid cellular-
satellite device, with multiple radios, can provide truly global data access.
In both cellular and satellite, the cost of the radio can be high relative to the rest of the device,
and the radios general consume substantially more electrical energy to transmit—the data range
is relatively long. For example, it would not be practical to equip low-cost sensors or simple IoT
application devices with cellular or satellite transports. These would be far better served by the
short-range wireless technologies such as Bluetooth or Wi-Fi, etc.
There is one other concern with cellular technologies: the longevity of deployment is driven by
smartphone users. Thus, the technologies evolve relatively rapidly and devices using cellular
services must be replaced after some period of time—longer than typical smartphone user
turnover, but less than older traditional wired technologies
In many IoT/M2M applications, short-range wireless data technologies such as Bluetooth, Wi-Fi,
ZigBee, etc., are in common use. For certain consumer IoT applications that only transmit to
a nearby smartphone belonging to an individual—such as fitness application devices—using
Bluetooth and low-power Wi-Fi are common choices. These allow the users to gather data via
applications on their smartphone. The need to further transmit the data to central servers for
processing is not a paramount requirement but can be done with ease from the smartphone,
if necessary.
Short-range wireless is also relatively low-energy, so battery-powered devices are easily
designed and deployed. In some low-use applications, the battery may last for months or years
before it needs to be replaced. This is a key advantage over cellular and satellite applications
that require far more frequent energy replacements (for example, using rechargeable batteries
that might last a few days).
For many home and business applications, a gateway modem that provides one or more short-
range wireless technologies for deployed sensors and low-power, low-cost, data transmitters
are ideal for a number of IoT/M2M applications. The gateway communicates to the application
servers using cellular or wired ISP connections.
Short-Range Wireless

Recently, the need for low-cost, low-power applications that offer longer transmission ranges
(between 2 to 20 miles) has seen the development of a number of new technologies and services
competing for the large-scale deployment of consumer and industrial IoT/M2M devices and
applications.
These are called LPWAN technologies and include commercial service networks deployed by
Sigfox and its licensees in some countries in Europe (and a few cities in the US as of this writing).
Similar (but not identical) data transports for IoT/M2M include the technologies developed and
deployed by Ingenu (formerly Onramp Wireless) and nWave, and the open standards efforts
by the LoRa Alliance, that appear to be geared to private network solutions rather than public
access data networks. However, a number of operators have opted to deploy certain LPWAN
technologies for public access by IoT and M2M applications.
These technologies currently use unlicensed wireless spectrum at various frequencies. Thus they
experience congestion and have technology and data rate limitations that are solved in different
ways. For some LPWAN transports, the data rate and message size is low enough that a simple
approach to overcome the congestion problems is possible, although the data is mostly one-
way (from the device) for low-power use. Others provide more complex data encoding to reach
the tower networks, leading to more expensive radio solutions that may work for some IoT/M2M
solutions, but not necessarily all.
Finally, the International Telecommunications Union (ITU) is working on a set of standards that
extend the 4G LTE technology for use with low-power, and potentially low-cost, radios. This
technology is tentatively called “LTE-M” (where M stands for Machine) and could compete strongly
with the other LPWAN technologies being deployed today. Since LTE-M is not available today, and
not likely to be available for another two to four years, there is an opportunity for these LPWAN
technologies to gain a foothold.
Recently, the 3GPP standards body ratified the NB-IoT
standard for use with IoT and M2M applications. Devices and
networks to use this standard are expected to be deployed in
the next few years and provide an alternative to the services
offered by SigFox, Ingenu, nWave, and LoRa.
Low-Power Wide Area Network (LPWAN)
A number of
operators have opted
to deploy certain
LPWAN technologies
for public access
by IoT and M2M
applications.

Although it is much too early to discuss 5G in any depth, it is important to note that the
requirements for the 5G cellular services include a need to accommodate large-scale deployment
of IoT/M2M applications and devices.
Overall, the 5G requirements are to provide:
• The transport of 1000x more data volumes that the smartphone users are using today
• More than 10x to 100x the number of connected devices in use today
• Dramatically lower latency (for end-to-end data packets) below a few milliseconds
• Projected 10x longer battery life for low-power devices, up to 10 years
The ITU has projected that the first set of 5G standards will not be available until 2020, although
work is already underway to discover what solutions and technologies—including new radio
encoding protocols—will be needed to meet the 5G requirements.
Fifth Generation (5G) Cellular

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CHAPTER 8
Connectivity
Management Platforms
77 CONNECTIVITY
MANAGEMENT PLATFORMS
77 What Is a Connectivity
Management Platform?
78 The Difficulties of Managing IoT Connectivity
79 Why Business Needs Connectivity
81 Essential Connectivity Management
Platform Features
CH 8: Connectivity Management Platforms

The Definitive Guide | 2nd Edition, The Internet of Things for Business. CH 8: Connectivity Management Platforms | 77
Advanced, diversified, and cost-effective connectivity is integral to the success of Internet of Things
and machine-to-machine communications initiatives. Smart devices and network endpoints generate
unprecedented amounts of data that must be collected, stored, and analyzed to perform IoT-
driven business operations and services. These processes involve data transmission using various
connectivity services and technologies that organizations must manage effectively to maximize the
value potential of their IoT deployments.
Connectivity Management Platforms (CMPs) are developed to achieve these goals as IoT companies
and connectivity service providers leverage multiple data communication solutions. This chapter
explores the role of CMPs in the modern IoT infrastructure, IoT organizations, and the evolving IoT
enterprise landscape.
CHAPTER 8
Connectivity
IoT devices have evolved into smart network endpoints that
extend the reach of cloud operating systems and perform
intelligent actions on their own. These devices don’t require
unique software embedded onto every hardware device.
Large IoT networks leverage multiple connectivity providers to
address diverse business needs. The result is an increasingly
complex network of smart devices that must be managed
as a ubiquitous computing system. These complexities not
only cause problems from a technology perspective, but may
drain financial and management resources merely to keep IoT
systems operational as a unified network.
With limited resource availability and increasing complexity
of IoT networks, organizations must automate the way they
manage, configure, control, and track IoT device information. CMPs automate these processes
to enable effective deployment, management, and utilization of IoT networks that span disparate
geographical locations, connected with multiple service providers and designed to scale exponentially.
WHAT IS A CONNECTIVITY MANAGEMENT PLATFORM?
With limited resource
availability and
increasing complexity
of IoT networks,
organizations
must automate the
way they manage,
configure, control,
and track information
from smart endpoints.

Managing cellular IoT deployments
is a complicated endeavor. Consider
the geographic considerations of
IoT networks that span multiple
countries, each presenting its own
set of financial, legal, compliance,
and technology challenges. Lack
of visibility and control is inherent
in these circumstances, especially
when operational excellence of
multinational organizations is tied with
supply chains, inventories, logistics,
and departments located in different
locations—all of which use connected
systems and devices to operate.
Merely keeping IoT deployments connected is a challenge in itself. Even with high signal strength,
IoT networks may be impacted with hardware, firmware, configuration, or application level issues.
Fast and effective issue resolution is dependent upon real-time monitoring and issue-tracking
capabilities. Failure to resolve connectivity issues not only increases operational costs but also
risks system-wide outages and downtime that may lead to reduced non-compliance and legal
implications, as well as customer dissatisfaction and damaged brand reputation.
Organizations need multiple connectivity options as they support global deployments with the
most reliable, cost-effective, high quality, and advanced connectivity services. With multiple service
providers, organizations need to manage multiple agreements and numerous billing and rating
systems. Service providers may use different types of connectivity technologies, which gives rise to
several integration, stability, and performance concerns in an otherwise unified IoT system. These
organizations must also collect, standardize, and analyze data from various platforms to perform
desired IoT operations. Significantly large resource investments and expertise are required to
address these challenges in favor of operating a global IoT network or at least one that leverages
multiple connectivity providers.
Organizations pursuing scalable IoT solutions to yield business growth should therefore proactively
manage and monitor their IoT deployments, connectivity channels, and the other associated
financial, legal, and technical aspects. This capability ensures reliable and high value performance
of IoT systems—regardless of deployment location, network span, and operator diversification.
THE DIFFICULTIES OF MANAGING IOT CONNECTIVITY
Figure 16: Interoperability
Support for
a diverse
set of IoT
applications
Connect to
network
through any
transport

The Internet of Things presents vast strategic business
advantages for the modern enterprise. We have already
seen the transformation led by automation in the industrial
segment. IoT brings automation and intelligence to everyday
objects, devices, and things that look to revolutionize the
consumer and enterprise market segment alike. However,
deploying IoT systems alone will likely lead to new concerns
that organizations must address before scaling their IoT
deployments and yielding true business value from the IoT.
CMPs are a viable solution to address the consolidation
challenges associated with operating large deployments of IoT
devices and how they fit in the IoT system. The following areas
will be key for any business looking to gain value from a CMP in
a highly scalable IoT environment.
WHY BUSINESS NEEDS CONNECTIVITY MANAGEMENT PLATFORMS
CMPs are a viable
solution to address
the consolidation
challenges
associated with
operating large
deployments of IoT
devices and how
they fit in the IoT
system.
The economies of scale for using IoT platforms will affect all CMP consumers, from the connectivity
provider to the OEM / application service provider to the developers. Scalable, flexible CMPs will
be essential for these partners to transition from custom vertical solutions to horizontal platform-
based solutions.
This has the main advantage of enabling them to work with high velocity, volume, and variety—or
the “three Vs.” High velocity in a CMP will spread the cost of infrastructure across many industry
verticals. High volume will result in shorter design cycles and rapid deployments. And variety as
part of a CMP means integrated network information can enable a richer application experience.
It’s also crucial to recognize the diversity in connectivity options as a primary driver of the need
for CMPs. Enterprises will need to first choose between wired and wireless connectivity. Within
the realm of wireless, options range from varieties of cellular to short-range wireless technologies
such as Wi-Fi and personal area networking including Bluetooth LE or ZigBee. Any reputable CMP
should be able to deal with all of these technologies and manage the protocols effectively.
Market Drivers for CMPs

Connectivity Management Platform (CMP)
Cellular Connectivity (LTE, CDMA, HSPA) BYOC Connectivity
Connectivity Adaptation Layer (Cellular, LPWA, WiFi, PAN)
Connected Devices
Web Portal
Interface
Rest API
Enterprise
Application
Integration (EAI)
Figure 17: Architecture of a CMP
The CMP plays a vital role in an IoT technology stack, enabling the promised technology and
business functions within the IoT infrastructure. Enterprise users expect “single pane of glass”
access for multiple connectivity technologies, whether they’re using cellular or Wi-Fi, and the CMP
must also support a diverse set of IoT applications.
The most integral connectivity management functions sought by IoT connectivity operators as well
as enterprise consumers and developers range from network visibility and control to data security
to end-user management, billing, and reporting.
Diverse vertical applications and environments will need to access a typical CMP, for industries
such as healthcare, automotive, and manufacturing. Ease of integration with enterprise
applications, including APIs, network data feeds, etc., is essential.
CMP Architecture
CMP Technology Needs

CMPs can offer a diverse range of capabilities to serve your
organization’s unique requirements. Getting the right fit is critical
for effective connectivity management. While not every solution
in the market addresses all of the fundamental enterprise and
operator needs, you’ll want to consider these factors in your
prospective CMP:
• Core Cellular CMP Functions—Your CMP should enable
a range of business and technology functions associated
with the cellular connectivity of your IoT endpoints.
These include the management of zones, devices, users,
accounts, pricing, policies, billing and invoicing, alerts and
reporting, and SIM lifecycle management, among others.
• Global and Multi-Carrier Footprint—IoT networks that span
disparate geographical locations and multiple countries use services from multiple providers.
Your CMP must support this capability.
• Security—Your CMP should offer features that ensure high data security, availability, and end-
user privacy. These features typically include identity and access management, audit trails,
anomaly detection, denial of service prevention, and strong encryption.
• BYOC Connectivity—Your CMP must support multiple connectivity technologies, from cellular
(LTE, GSM, HSPA, CDMA) to non-cellular (Wi-Fi, Bluetooth, ZigBee, LPWA). And you should
have the flexibility to bring your own connectivity from the operator or carrier of your choice.
All types of data transmission protocols, technologies, and standards should be supported
across different transmission layers as required by your IoT systems.
• Quick Time to Market—It takes long design and deployment cycles before custom vertical
solutions are released and create value for your IoT service organization. Using CMP as a
horizontal platform readily available in the market ensures that IoT connectivity is managed
right from the beginning.
• Flexible Pricing—Enterprises need to accommodate a range of pricing tiers from multiple
connectivity providers to reduce total cost of connectivity services. Your CMP should help you
optimize pricing for different IoT app needs in different business functions and spread the cost
infrastructure across multiple tenants and verticals.
• Billing and Real-Time Data—Accurate billing information updated in real time ensures that
you only pay for what you consume.
• Variety of IoT Application Needs—Different IoT applications present different needs for
ESSENTIAL CONNECTIVITY MANAGEMENT PLATFORM FEATURES
Your CMP must
support multiple
connectivity
technologies—and
you should have the
flexibility to bring your
own connectivity.

connectivity management. Your CMP should support these diverse and evolving needs as
more functionality is added to your IoT applications.
• Open System—Strong integration with your infrastructure and other enterprise applications
via open APIs.
• Flexible Accounts and User Access—Progressive and agile organizations scale teams
rapidly to meet evolving resource requirements. The ability to create and manage new
accounts and streamline user access enables effective connectivity management.
• Diagnostics and Fault Resolution—CMPs can extend the monitoring and diagnostic
capabilities of your IoT systems with rich graphical representation of connectivity patterns and
defined performance metrics.
• Light Touch Integration—The CMP should operate as a standalone tool and connect with
other enterprise applications only when and as required.
• Highly Scalable and Elastic—Your IoT needs will change. Consider a connectivity
management solution that accommodates these changes.
Without a single-pane-of-glass view for managing multiple connectivity services across different IoT
deployments, organizations risk connectivity issues that present severe business, technology and legal
implications. The traditional practice of using manual processes or individual platforms to manage IoT
connectivity service is both time consuming and ineffective, and CMPs can help solve this issue.
IoT deployments are driving business model innovation among progressive organizations pursuing
smart technologies and advanced connectivity solutions to generate unprecedented new revenue
streams. Intelligent IoT devices generate invaluable data in real-time that is transmitted to backend
systems through various transport systems including cellular and Wi-Fi communication. Progressive
and agile organizations increasingly rotate data between different processes, functions, and teams.
Advanced IoT connectivity systems that must scale rapidly to meet varying organizational
demands tend to create a performance bottleneck among agile enterprises if users, technologies,
performance, and processes are not managed effectively. These issues will only grow with the
rapid adoption of IoT technologies. Gartner predicts the IoT market is expected to reach the $4
trillion mark by the year 2020. And since every company is a technology company in the present
IoT-driven enterprise IT landscape, organizations will pursue a diverse range of connectivity
solutions to serve their exploding IoT demands.
Effective management of IoT connectivity will eventually surpass all key enablers of IoT-driven
business success. The ability to leverage CMP technologies to address this need from the very
beginning will drive competitive differentiation, operational excellence, and promise strong IoT
growth for any company pursuing IoT initiatives in the near future.
CMPS Are Integral to the IoT Environment

CHAPTER 9
IoT Analytics
84 IOT ANALYTICS
84 IoT Data and Analytics
85 Types of Analytics
89 Analytics Tools and Languages
CH 9: IoT Analytics

The Definitive Guide | The Internet of Things for Business, 2nd Edition. CH 9: IoT Analytics | 84
This chapter will describe what is meant by analytics in the context of Internet of Things and
machine-to-machine applications. Looking at data and finding meaningful patterns in that data is
the basis of analytics. These patterns could describe the state of the data, predict an outcome,
find correlations between variables, project trends in the data, and more. Analytics is used in
many aspects of business, from marketing to risk management. In this chapter, we’ll discuss
analytics as it relates to IoT/M2M data.
Over time, IoT applications can generate vast amounts of data—this is part of the Big Data
revolution that is much hyped in the media. For example, the Aeris IoT/M2M network manages
traffic from over one billion IoT events each day. The more IoT/M2M data points being transported,
the more sophisticated analytics are needed to understand and gain value from the patterns.
New ways to process and store computing data has made it possible to apply analytics to
business problems faster and at a greater scale than ever before. Successful organizations
take advantage of these tools and analyze the data their IoT/M2M deployments collect so they
can gain insights into everything from how to streamline their manufacturing processes to how
satisfied their customers are.
CHAPTER 9
IoT Analytics
M2M and IoT devices usually report data in constant streams, and these must be processed in
real-time or near-real-time. This is a shift from traditional programming frameworks that open a
file, read it, process the contents, and then close it. Today, real-time analytics are possible on
streaming IoT/M2M data.
Another key to IoT/M2M analytics processing has been the development of open-source
distributed storage and distributed processing frameworks such as Hadoop. This allows
processing of very large data sets over computer clusters. Hadoop can also be deployed as a
cloud-computing service by smaller organizations.
With these scalable technologies capable of managing streams of Big Data, businesses can use
various types of analytics to better understand and use the data their IoT/M2M sensors collect.
IOT DATA AND ANALYTICS

1
DESCRIPTIVE
ANALYTICS
What Happened?
2
DIAGNOSTIC
ANALYTICS
Why Did It Happen?
3
PREDICTIVE
ANALYTICS
What May Happen?
4
PRESCRIPTIVE
ANALYTICS
How to Reduce Risk
Increase Revenue?
Analytics can be grouped into four broad categories: Descriptive statistics, diagnostic analytics,
predictive analytics, and prescriptive analytics.
TYPES OF ANALYTICS
Descriptive analytics, also called descriptive statistics, gives a numerical representation of the
data that is on hand right now. It provides a way to express in absolute, unambiguous terms, a
quantitative measurement of the current state. This analysis can draw conclusions of data from the
past as well.
In a broad sense, descriptive analysis answers these questions:
• What happened?
• How often did it happen?
• How reliable was it?
• How accurate was it?
Knowing the current status of the IoT/M2M data provides a baseline to compare future states
against. It is possible to compare basic data from the past to present, tracking progress.
Descriptive analytical tools can be as simple as tracking website traffic or more complicated such
as cluster analysis in market research.
Descriptive Analytics
Figure 18: Types of analytics

This type of analytics is often merged with descriptive
analytics, and together they can give data greater interactivity.
Where descriptive analytics asks “what happened?”,
diagnostic analytics asks “why did this happen?” The
diagnostic tools can be applied to the data to look for the
root causes behind the results observed in the original data.
Usage-based insurance implemented with vehicle telematics
is one example of descriptive and diagnostic analytics in
action. This type of car insurance bases the driver’s insurance
premium rates on behavior that is tracked via a GPS-enabled
cellular transmitter in the car. The distance a person drives,
when, and where are tracked, and this data is used to
calculate the insurance cost.
Diagnostic Analytics
Usage-based
insurance
implemented with
vehicle telematics
is one example
of descriptive
and diagnostic
analytics in action.
Prediction is one of the main reasons that businesses use analytics in the first place: predictive
analytics provides a means of projecting what will happen next, based on what has happened in
the past. By finding patterns and trends in the data, it may be possible to predict future results.
While assuming future behavior will be the same as past behavior isn’t always the case with,
for example, the stock market or consumer purchasing habits, machine behavior is generally
highly predictable. In a factory, vibration and temperature data broadcast from an IoT/M2M-
connected device can indicate, with a high degree of accuracy, when a machine needs preventive
maintenance. Businesses can use predictive analysis in their own IoT/M2M deployments as part of
supply chain management and manufacturing processes to increase efficiencies.
Brake balancing in trucking fleets is a complex and expensive maintenance issue. Without regular
maintenance, the risk of a truck jackknifing is high. It takes a highly trained technician a great deal
of time to check for the combination of brake temperature and pressure on a truck fleet to know
when to make an adjustment to the vehicle’s brakes.
But in Michael Lawrence-Smith’s study, “Cooperating Artificial Neural and Knowledge-Based
Systems in a Truck Fleet Brake-Balance Application,” he describes how machine-learning
techniques used predictive analysis to improve brake maintenance. These computer-aided
systems have a 90% success rate at predicting when to replace brakes, resulting in an annual
savings of at least $100,000 for larger trucking companies.
Predictive Analytics

Prescriptive analytics is the logical next step from predictive analytics. It asks what a business
should do based on the data collected and analyzed. Prescriptive analytics uses models to both
recommend actions and forecast outcomes to reduce risk.
Much as descriptive and diagnostic analytics work well together, predictive and prescriptive
analytics work hand-in-hand. As past data is used to calculate future results, prescriptive analytics
can be used to make better choices and take advantage of opportunities.
Google’s self-driving cars, for example, use prescriptive
analytics to make countless driving decisions, according to
Data Informed. The cars communicate with the cloud using
IoT/M2M systems to obtain data on traffic and weather,
which becomes part of their driving computations. The
vehicle’s on-board computers apply machine learning to the
problem of what a car should do based upon predictions
of future outcomes. For example, the car’s computer may
predict traffic based on the time of day and then determine
what route to take and what speed to travel safely.
A business operating an IoT/M2M service for its customers must keep track of data and manage
billing and usage data. Connectivity analytics can provide this data to an organization for
administering an IoT/M2M deployment.
For example, a company has to provision IoT/M2M devices on the network, as well as remove
or replace any devices that are operating incorrectly. A problematic device could be trying to
authenticate on the IoT/M2M network multiple times per minute, which uses up resources, so it
will need to be fixed quickly. Connectivity analytics can immediately pinpoint the problem device so
it can be removed from the network or repaired remotely.
Prescriptive Analytics
Connectivity or Communication Analytics
Much as descriptive
and diagnostic
analytics work
well together,
predictive and
prescriptive analytics
work hand-in-hand.

For greater understanding of connectivity analysis, a business could use Aeris’ AerVoyance™,
which provides visibility and insight into its IoT/M2M deployments. Focusing on devices,
connectivity, and billing information, this tool helps companies effectively manage their IoT/M2M
systems through an intuitive, visual presentation.
AerVoyance identifies devices with connectivity issues and allows zooming in on a specific device,
time, or usage activity including data, SMS, voice, sessions, cellular registrations, etc. This allows
customers to find specific devices that are behaving abnormally. In addition, there is a summary
view of the device portfolio including billing, usage, and status. This type of connectivity analytics
tool helps businesses understand their aggregate and average connectivity usage over time and
better deployments.
AERVOYANCE FOR CONNECTIVITY ANALYTICS
Figure 19: AerVoyance™ dashboard

While many different statistical models, tools, techniques,
and programming languages can be used in all of these
types of analytics, below are few specific ones used to
provide analytics functions for IoT/M2M applications.
ANALYTICS TOOLS AND LANGUAGES
Planning in advance
for analysis of the
data that will be
collected and stored
is a crucial part of any
IoT/M2M project.
R is a programming language for statisticians. Common in academic research, R has also become
quite popular with data scientists in the corporate world as business needs for analytics have
grown dramatically.
This language can be used to draw graphs and return the numeric results of algorithms run against
the data. R has functions to let the programmers run different analytics tools against the data. This
makes it easier because it contains packages or functions contributed by different people around
the world as open-source software.
MapReduce is a programming model that can be used to query very large data sets, such as
those in Hadoop databases. What MapReduce does is take a query and run it across hundreds
or thousands of computers to derive an answer to an analytics question. It does this in two
steps: map and reduce. The map step collects data on each node in the Hadoop Distributed File
System. Reduce eliminates duplicates and produces the result set.
People can write MapReduce in programming languages like Java. But there also exist specific
languages for Hadoop to make the job of creating MapReduce programs easier.
These are just some of the analytical methods and tools available for IoT/M2M solutions. Planning
in advance for analysis of the data that will be collected and stored is a crucial part of any IoT/
M2M project.
R Language
MapReduce and Hadoop

CHAPTER 10
Implementing
an IoT Solution
91 IMPLEMENTING
AN IOT SOLUTION
91 Supply Chain Management
92 Cellular Operator Selection
93 Operator Support Service Level Agreement
93 Device Certification
94 Normal Operation Considerations
95 Application Communications Call Flow
95 Customer Support Process
CH 10: Implementing an IoT Solution

The Definitive Guide | The Internet of Things for Business, 2nd Edition. CH 10: Implementing an IoT Solution | 91
An Internet of Things and machine-to-machine communications deployment has to either
increase business revenue or reduce business costs (or both), otherwise there’s no reason for a
company to pursue it. Either of these objectives can provide a return on investment. It’s up to the
project manager to determine the specific goals and measurements of this ROI.
Related drivers for IoT/M2M projects can be new regulations and industry requirements,
greater efficiencies, more consistent control over processes, predictive visibility into patterns or
opportunities, and gaining competitive features that can meet customer requirements. As you
build your IoT business model, these factors will weigh differently depending on the product
needs and industry.
CHAPTER 10
Implementing an IoT Solution
Whether or not the company’s business is
manufacturing, you will have some level of
supply chain management. This is simply
planning for the flow of materials and services
into and out of the business, managing all
the goods required to make your IoT/M2M
deployment happen. If your company is
building its own IoT/M2M devices from scratch,
you’ll have a great many materials, parts, and
suppliers to account for. If you’re assembling
devices from readymade components, you
need to deal with fewer suppliers. Even if you
buy a complete, off-the-shelf device, that still
requires sourcing, testing, and managing supply
and demand.
SUPPLY CHAIN MANAGEMENT
Figure 20: Supply chain management

The service provider must be able to deliver several essential
requirements for the IoT/M2M project, including low costs,
reliable network connectivity, robust service agreements,
effective application integration, and flexible rate plans. If
they can’t deliver on these prerequisites, they are not going
to be the right partner.
To help you select the ultimate service provider with the
capacity to manage a successful deployment, you may
want to ask these questions of any cellular carrier during the
selection process:
• What are the costs for the entire lifecycle, not just per
Kb rates? Make sure you won’t be hit with hidden costs
from your cellular operator that drive up your IoT/M2M
service bill.
• Are they carrier-agnostic? Can the service provider expand cellular coverage beyond its
own cell towers? Traditional operators only optimize their cellular coverage based on their
cost of delivery, and they always prefer to use their own towers, even if the coverage
they provide is weak or intermittent. A carrier-agnostic provider, like Aeris, can expand
coverage where needed, and will offer the strongest signal, regardless of operator, with no
interruption in service.
• Do they offer remote troubleshooting as well as hands-on support? Cellular carriers with
remote, real-time troubleshooting capabilities can save you significant costs. Also, an
operator with a network operations center support team that deals only with IoT/M2M-related
issues is going to be more knowledgeable about your devices and connectivity issues.
• Do they offer a dedicated IoT/M2M network? A network dedicated solely to IoT/M2M traffic
won’t experience the delays caused by crowds of consumer handsets. The lower latency of
an IoT/M2M-dedicated network means you’ll be able to rely on mission-critical transmissions
to get through the first time.
• Do they have APIs for easy integration with your existing systems? Can the cellular operator
provide a full suite of free APIs that let you extend the capabilities of your customer-facing
applications and back-office solutions, leveraging business applications such as those from
SAP, Oracle, and PeopleSoft? These applications are integral elements of enterprise resource
planning-based supply chains and are linked to back office systems with APIs.
CELLULAR OPERATOR SELECTION
The lower latency
of an IoT/M2M-
dedicated network
means you’ll be
able to rely on
mission-critical
transmissions to
get through the
first time.

• Do they offer pay-per-use as well as per-device billing plans? Can the cellular operator offer
rate plans that are flexible enough to meet your needs? When managing IoT/M2M services,
it often makes more sense to go with a pay-per-use plan than with a per-device or fixed-data
plan. Pay-per-use is most cost-effective for lower-usage device profiles. If your devices have
higher-usage levels—10 MB or more—a per-device data plan is your best option.
These are some of the top-level concerns your company should consider when choosing a
cellular operator. You’ll want to partner with a service provider that suits your business needs and
can support your IoT/M2M project over the long term.
The Service Level Agreement (SLA) you negotiate with the operator defines the scope of your
contract with the operator. This is where your business defines its relationship with its network
provider, so it’s important to specify what will keep your IoT/M2M deployment running.
Things to consider in your SLA include:
• What are the expectations for connectivity? How reliable is the operator’s network historically?
• What are the geographic restrictions of the operator’s network, if any? Some carriers may not
guarantee service at all towers or all sections of particular metro areas.
• How long will it take the operator’s customer support to acknowledge and then take care of a
problem?
When entering into an SLA, make sure the agreement is realistic, actionable, measurable,
calculated, well-defined, mutually exclusive, and completely exhaustive in covering all aspects of
the concerned networking services.
OPERATOR SUPPORT SERVICE LEVEL AGREEMENT
Devices must be approved or certified to run on the
operator’s network. For this certification, the focus is
generally on testing the cellular behavior of the device.
One example of this might be the behavior of the retry
algorithm used by the device if it fails to connect to the
application server in your data center. A continuous retry by
thousands of your devices at the same time could overload
the operator’s network. Implementing a random back-off
algorithm and testing it prior to certification is a better idea.
DEVICE CERTIFICATION
Additional
certification may be
required by standards
organizations,
regulatory agencies,
or even your
customers.

Here are a few of the concerns to be dealt with when IoT/
M2M devices are deployed:
• What is the definition of “normal”? What are the
baseline transmission patterns and server performance
measurements?
• What happens if the IoT/M2M device can’t connect to
the cloud platform? In addition to having a random back-
off retry algorithm, what will the device do with its data?
Remember that stale data would be inaccurate when
transmitted too late. The device needs to know when to
generate an alarm.
• What should a mobile IoT/M2M device do if it loses cell signal? The device needs to know
when it is appropriate to hold the data in its queue and retry later.
The range of normal operations will vary for each IoT/M2M deployment, so you’ll need to set initial
parameters for all aspects of the program. Then you can track performance against this baseline
moving forward.
NORMAL OPERATION CONSIDERATIONS
Your developers will
need to outline each
aspect of the IoT/M2M
application’s call flow,
accounting for both
standard, predictable
behaviors and for
outliers.
Operator certification is also an opportunity to use the application host server software for
additional tests that stress the interaction between the device and the server. Unusual scenarios,
such as delayed responses from the server (that might be observed during congestion or server
scaling), can be used to see if a device handles them gracefully.
In certain markets, such as the healthcare industry, additional regulations for device performance in
medical environments and data privacy rules may apply. Additional certification may be required by
standards organizations, regulatory agencies (such as the Federal Communications Commission
in the US), or even your customers, particularly if there is end-user integration. Each company
deploying such IoT and M2M applications must determine how to best meet all the regulations
that apply to them.

This is where the details of the IoT/M2M transmission are agreed upon. Some design issues are:
• Should the device assume there will be a connection when needed or should it be able to
queue data for delivery later?
• Will the application “fire and forget” data or will it wait for an acknowledgment? At the network
layer, “fire and forget” means to use the UDP protocol for transmission.
• The general call flow is to establish a connection, transmit data, wait for acknowledgment,
then disconnect. This is generally a TCP protocol implementation.
• Does the data need to be encrypted? That can increase the amount of data being sent.
Your developers will need to outline each aspect of the IoT/M2M application’s call flow, accounting
for both standard, predictable behaviors and for outliers.
APPLICATION COMMUNICATIONS CALL FLOW
Support staff will need to be trained on the product
features and how to operate them, but it’s also very
important for the support team (especially tier-2 support)
to receive training on identifying connectivity issues. This
is where a rich set of diagnostic tools from the carrier, if
available, become a huge benefit.
If your tier-2 engineer can log into a portal and see if the
device in question has registered on the carrier network
and started a data session, then the engineer can
observe the recent behavior and can immediately focus
the investigation on the root of the problem. Using this
observation, the engineer can provide quick feedback
to their customer. If these tools are not available, then
support sessions are much slower.
Implementing an IoT/M2M network project requires a great deal of forethought. But this advance
planning pays off in a scalable product with a higher return on investment. The next chapter gives
an overview of how to manage your IoT/M2M project’s lifecycle once you’ve implemented the
network itself.
Figure 21: Customer support alert
CUSTOMER SUPPORT PROCESS

CHAPTER 11
IoT Lifecycle
Management
97 IOT LIFECYCLE
MANAGEMENT
97 Planning Checklist
99 Lifecycle Management Phases
103 Pitfalls to Avoid
CH 11: IoT Lifecycle Management

The Definitive Guide | The Internet of Things for Business, 2nd Edition. CH 11: IoT Lifecycle Management | 97
To plan and deploy a successful Internet of Things and machine-to-machine communications
project, you first have to analyze your needs. The IoT/ M2M application has to make a strong
return on investment, either by increasing revenue or reducing costs. Once you have built a case
for making the deployment, then you can outline a full plan that accounts for all the processes,
methods, and the design principles for this deployment. After identifying the business needs, you
should then evaluate the best practices for your IoT/ M2M implementation. Key considerations
can include regulatory requirements, operational costs, and customer requirements.
IoT/M2M deployments are long-term investments. Companies offering connected devices
and applications must ensure that their solutions are planned with everything from growth
through end-of-life in mind. IoT/M2M deployments need reliable, secure, and high-performance
connectivity to support device maintenance and troubleshooting, data collection and analysis,
and consideration of future upgrades in hardware, software, and connectivity standards.
CHAPTER 11
IoT Lifecycle Management
An IoT/M2M project manager needs to define the
deployment’s scope from a business point of view as well as
integrating the technical considerations. While you plan for
the intensity of the initial deployment activities, you should
make sure your company invests in a robust connectivity
management platform.
In addition, application and data analytics must be part of
your IoT/M2M program. Key stakeholders should understand
the fixed and recurring costs, while the IT organization needs
to be supportive of the IoT/M2M deployment.
PLANNING CHECKLIST
The key interface
areas may include
manufacturing and
supply chain, IT
systems, operations,
support, finance, and
regulatory approvals.

The project manager must oversee the following considerations:
• Go-to-market business model: How your company will market and sell to customers.
• Supply-chain management: Consideration of device manufacture/assembly, device
provisioning on operator’s network, testing the communications link at the end of manufacture,
and also channel-to-market goals.
• Application communication call flow: How the device will interact with the network.
• Definition of expected usage patterns: Normal operating patterns, over-the-air updates,
troubleshooting, dealing with rogue devices.
• Cellular operator selection: Consideration of technologies, geographic coverage, bandwidth,
uptime, support, load-balancing capabilities, security, scalability, etc.
• Device operator certification: Are the devices guaranteed to work on the operator’s network?
• Operator integration and API integration: How the operator’s platform will support your IoT/
M2M applications for device management and data collection.
• Operator support Service Level Agreement (SLA) understood and in place: Making sure the
service level agreement satisfies all parties involved.
• End-to-end application test plan: Testing of the communication links across different RF
conditions.
• Customer support process definition and support team training: How you will support your
end-customers once the IoT/M2M deployment is up and running.
These are just the basics of what you must plan for as you begin the IoT/M2M lifecycle process.
You’ll want to work with all the related areas of your business and make sure they are onboard and
prepared for their part of the project as well.
The project manager should be aware of how the IoT/M2M deployment impacts other key
stakeholders so they can provide input to planning and execution as well as ensure that their own
requirements are met. The key interface areas may include manufacturing and supply chain, IT
systems, operations, support, finance, and regulatory approvals.

A crucial decision to begin with is whether your company will be manufacturing the IoT/M2M
device itself, assembling it from parts, or purchasing a complete device from a supplier. You must
understand your business needs and whether a customized or off-the-shelf solution is the best fit.
Whichever route you select, you’ll have to plan for the whole lifecycle of the IoT/M2M product.
Consider the longevity of your product—many IoT/M2M devices are deployed for long periods,
operating 24/7, and possibly for more than 10 years. These devices may also be in rough or
remote locations, so it’s important to think about the physical durability of the design. Consider
how the device will function under whatever variety of temperatures, humidity, and vibration
specifications it may be subjected to in the field.
Finally, what data do you need to collect and transmit? Different sensors are relevant for different
needs. Some can be used in combination to provide unique results. Whether you’re building an
IoT/M2M solution from scratch or from modules or buying a complete device, it all comes down to
having the right sensors. Refer to chapter 4 for an overview of commonly used sensor categories.
Product Design
A guide to managing the lifecycle of an IoT/M2M deployment could be an entire book in itself. As
a brief overview, we’ll discuss the phases of product design, scaling for growth, product testing,
installation, operations, and end-of-life.
LIFECYCLE MANAGEMENT PHASES
Product
Design
Product
Testing
End of
Life
Operations Installation
Figure 22: IoT/M2M product lifecycle

Comprehensive testing is not only important to test the core functionality, it’s also essential to
ensure the IoT/M2M solution operates correctly over your selected cellular network. This usually
begins with lab testing in a controlled environment. In the lab, engineers typically ensure that the
device has a strong signal level that will provide a high-quality cellular connection and achieve
consistent data rates across the air interface.
This is fine for basic functional testing, but negative test cases should be introduced to force poor
signal quality conditions that are sometimes present in the field and lead to low bandwidth and
long latency conditions. Applications requiring near real-time communication may not operate
correctly under these conditions, so it is critical to consider this when designing the application,
selecting the technology (for example 2G, 3G, and LTE each have different latency characteristics),
and when testing the application.
Field-testing a small device population can provide an indication of how the devices will behave
after launch. Providing you select an appropriate sample size and deployment environments, if
you see a problem with 1% of your devices then, within a margin of error, you are likely to see the
same problem in 1% of your devices after launch. If you can address the problem and get the
problem device count within an acceptable margin, then there should be a high level of confidence
after launch that the percentage of devices with problems will remain manageable.
Product Testing
Installation
If you want to deploy large numbers of devices, your manufacturing or assembly process and
server and network capacity must all be designed for the expected growth. Without planning for
growth, the volume of data from devices may eventually overwhelm your host server systems—for
example, for databases used for information storage, as well as any real-time applications that
analyze the transmitted content for processing.
Businesses that don’t plan for scalability at the beginning may get caught in a “growth stall” after
deploying their first hundred or thousand devices. Successful organizations include growth in their
lifecycle management. For a discussion of scaling up IoT/M2M deployments, see chapter 7.
SCALING FOR GROWTH
Placing the IoT/M2M devices in the field, connecting them
to the network, and provisioning them are key steps to
installing a deployment. Each of these aspects has its
own requirements. Field installation, for example, may be
done by third parties who aren’t necessarily experienced
with your product, so your developers will need to write
detailed installation procedures. Automated post- installation
verification methods and clear troubleshooting procedures for
installation failures can also be very useful.
“Provisioning”
means registering
the device on the
IoT/M2M platform,
such as a cloud-
based application.

Connecting the devices to the network and provisioning them must be done correctly before data-
collection can begin. “Provisioning” means registering the device on the IoT/M2M platform, such
as through a cloud-based application. Once devices are provisioned and on the network, the IoT/
M2M application can begin functioning as designed.
Once an IoT/M2M deployment is installed, the devices, their applications, and the data generated
have to be managed. For the devices, this includes managing device traffic, analyzing device
performance, troubleshooting problem devices, managing rogue devices, and updating devices
remotely, as well as billing, suspending, and canceling devices. For applications, your operations
plan must account for secure and reliable collection, storage, analysis, and publishing of the IoT/
M2M data produced by all of your devices.
During initial deployment—after a set of devices are installed and used by end-users—it is a good
idea to verify that the device behavior (transmission patterns, retry algorithms, etc.) is as expected.
Early detection of problems is important, so that serious problems can be fixed before deploying
larger numbers of devices, thus reducing the cost of repair or replacement.
You may also want to track device transmission patterns
and server performance measurements to establish a
baseline for future monitoring. Later, any data transmission
pattern change from the baseline can be an early indicator of
problems. These early measurements also provide guidance
for scaling—the server performance load can be recorded
for future expansion planning. During normal operation,
the device transmission patterns should continue to be
monitored and measured against the patterns recorded
during development and initial deployment.
Operations
Over the Air update
capabilities can
add cost to the
device but will be
easily justified the
very first time an
IoT/M2M device
needs updating.

IoT/M2M devices will generally need at least one or more application firmware updates during their
lifetimes. Planning for this download is critical to reduce costs. Frequently, devices are deployed
in remote or hard to reach locations, where sending out a technician would be expensive.
Businesses should add over-the-air (OTA) firmware update capabilities to their IoT/M2M devices.
This may require extra hardware components; for example, sufficient memory to hold at least
two “images” of the firmware—one active and one new—as well as code for the download and
verification of the images, restoration of old images, etc. This can add cost to the device but will
be easily justified the very first time an IoT/M2M device needs updating.
Developers can also deploy an “incremental” update capability, using software that allows
sending smaller chunks of firmware. This uses less memory in the hardware, reduces the
update transmission time, etc., but it can increase the coding support and complexity
required in the device.
OVER-THE-AIR UPDATES
Figure 23: Over-the-air updates

Products may have a limited lifecycle, whether due to technology, demand, or other business
factors. The entire deployment or specific devices may need to be disabled. Businesses need to
plan how to effectively remove devices, transition to new technology, or shut down the deployment
altogether. Billing issues, customer support, and logistics all must be accounted for.
Device Removal and End-of-Life
In general, addressing a problem early, as soon as it arises, is less expensive, particularly if a
device recall is required or a large number of devices must be physically serviced by a technician
for a replacement or firmware update. Some major problems can be avoided if troubleshooting
capabilities are designed into the device, the servers, and the complete IoT/M2M application and
product. This can avoid major cost issues after deployment.
IoT/M2M deployments are not immune to disasters and factors beyond human control. When
a service disruption is inevitable, the IoT/M2M enterprise must guarantee immediate fixes to
the problems affecting its customer base. IoT/M2M solutions can experience both front-end
and back-end service failures at times, so you’ll need plans in place to deal with the worst.
Your network operator should be able to provide assistance and share its expertise to minimize
downtime, while your customer support staff addresses end-user concerns directly.
PITFALLS TO AVOID

CHAPTER 12
The Future of the
Internet of Things
105 THE FUTURE OF THE
INTERNET OF THINGS
105 IoT Will Come First
106 Homes Will Get Smarter and
More Connected
106 Enterprises Will Spend More on IoT
107 IoT Standards Will Need Better Definition
108 Security Concerns Will Continue
109 IoT Value Will Be Realized Through
Data Analytics
CH 12: The Future of the Internet of Things

The Definitive Guide | The Internet of Things for Business, 2nd Edition. CH 12: The Future of the Internet of Things | 105
Imagine a future where enterprise customers and consumers will routinely ask product
companies about the sensor capabilities accessible via a mobile app when purchasing a new
refrigerator or car. In this future, cars will talk back to the driver not just drive for them, people
will wear clothes connected to the Internet, reading glasses will be connected to the Internet to
provide additional context to enrich the user’s experience, and more than one-half of the Internet
traffic to homes will go to their appliances and devices and not to children’s video games. Sound
unlikely? We think this future may not be too far away.
Given the tremendous growth and change that is taking place in the IoT industry, the future is
certainly hard to predict, but here is what we see taking shape in the near future:
CHAPTER 12
The Future of the
Internet of Things
The most competitive companies,
products, and solutions will be those built
around the concept of “IoT First.” This
means products will be designed from the
outset with IoT as a primary consideration,
and enterprises will begin planning
projects and building systems with IoT in
mind. Today’s typical approach of adding
connectivity to an existing product or
service will continue to exist, of course,
but those initiatives will not generate as
much value as IoT-First projects.
IOT WILL COME FIRST
IoTFIRST

As the cost of sensors, processors, and networking goes down and consumers are increasingly
aware of the benefits of the smart home, IoT home automation will provide greater peace of
mind through security implementations. Remote access is the greatest selling point for IoT
technologies these days. Consumers can check status on their home from their smartphone—
for example, consumers can ensure that the front door is locked or view security cameras.
Security systems can push alerts to consumers, as well as security agencies, in the case of a
break-in or abnormal activity inside the home. Many people see the value of such systems if they
are away from home often or traveling. A quick check can provide a sense of control even if they
are thousands of miles away.
The second aspect of the connected home of the future is in
the area of energy usage. Consumers can automate lighting,
temperature, and irrigation operations remotely. Home
automation devices and sensors identify rooms that are
occupied and adjust HVAC systems and lighting to ensure
energy conservation. Appliances with IoT technologies are
self-monitoring and can determine changes in operation
due to potential maintenance issues. Sensors in outdoor
landscaping determine the current moisture saturation level
of the ground and adjust the sprinklers. Smart meters provide
utilities with information about energy usage patterns to
assist in planning power consumption across a designated
area. Discounts and incentives are given to consumers to
change usage patterns to benefit a community. The result of
the connected home is increased safety, convenience, and
freedom from mundane decisions to allow consumers to
enjoy more of their life.
HOMES WILL GET SMARTER AND MORE CONNECTED
Currently, many think of IoT in terms of consumer devices, but industry growth is headed towards
enterprises spending far more on IoT than consumers. McKinsey forecasts that of the $12 trillion
in economic value generated by 2025, the majority (70%) of this value generated from IoT by
2025 will be from B2B deployments.1
Furthermore, IDC believes that the IoT installed base will
be split 70% in the enterprise and 30% in the consumer market by 2018.2
However, enterprises
will account for 90% of the spending. In terms of the source of value, it will be derived from
productivity enhancement and cost reductions made by companies everywhere.
ENTERPRISES WILL SPEND MORE ON IOT
The result of the
connected home is
increased safety,
convenience, and
freedom from
mundane decisions
to allow consumers
to enjoy more of
their life.
1
“The Internet of Things: Mapping the Value Beyond the Hype,” McKinsey Global Institute, June 2015.
2
“IDC FutureScape: Worldwide Internet of Things 2016 Predictions,” IDC, November 4, 2015.

The IoT space has undergone fast growth in a relatively
short amount of time, so it’s no surprise that standards are
nascent and not yet well defined or widely adopted. With the
proliferation of devices, sensors, connectivity technologies,
and IoT platforms that need to be integrated to organizational
systems, it can be challenging to find technology- and vendor-
agnostic solutions that will work well with each other. However,
interoperability is key to ensuring the long-term success of
IoT initiatives so companies need to be careful when going
down the path of a vendor-specific solution that may not play
well with others. Examples of issues due to lack of standards
include legal implications for the accuracy of data, safe harbor
issues between Europe and the US, and concerns around
liability in accidents with autonomous vehicles.
Vendors, solutions providers, and distributors will also undergo changes as a result of the lack
of standards. Beyond consolidation in the industry that is typical of any fast-growing, dynamic
sector, IoT companies will move to providing fully connected solutions instead of just products
and services. Additionally, we will see a shift in IoT from technology and vendor-specific solutions
to agnostic solutions. The primary driver for this change is to help reduce the complexity of
incorporating and integrating point solutions which slows down the number of purchases but also
increases the value of the solutions services provided to the customer.
In terms of defining standards, the good news is many organizations are taking leadership roles.
Here are a few groups furthering education, standards, and best practices for creating, integrating,
deploying, and maintaining an IoT program:
• The IoT M2M Council (IMC) is focused on proving the business case of the IoT technology to
those that would adopt it. It aims to stand for connectivity as its own global industry and not
viewed through the narrow lens of a technical standard or single vertical industry.
• The Automotive Security Review Board (ASRB) is a nonprofit research consortium committed
to mitigating the cybersecurity risks of connected and autonomous vehicles, while
encouraging technological progress and innovation. It is founded by Intel, Aeris, Uber, and
other companies that are paving the path to the connected car of the future.
• The Healthcare Information and Management Systems Society (HIMSS) is a healthcare-focused
organization that globally leads endeavors to optimize health engagements and care outcomes
through information technology. Its large membership is representative of the healthcare
industry and is helping shape interoperability standards that will be successfully adopted.
IOT STANDARDS WILL NEED BETTER DEFINITION
Interoperability is
key to ensuring the
long-term success
of IoT initiatives so
companies need to be
careful when going
with a vendor-specific
solution that may not
play well with others.

Data security will become a significant part of most business’ IoT budget, and security concerns
could slow down IoT adoption if they aren’t approached thoughtfully. Given the massive amount
of data generated from these information-intensive programs, the countless number of connected
devices, and the need to provide secure connectivity, the IoT enterprise must be careful to not
risk the privacy and security of individuals. Although defending against sophisticated cyber-
attacks is not entirely possible for end-users of connectivity technologies on their own, following
industry-proven best practices and designing programs with security and privacy in mind is
critical to the successful adoption of IoT.
Additionally, if an enterprise keeps security in mind from the onset and incorporates it at a level
appropriate to the use case, we believe that most concerns will be mitigated. Data security must
be relative to the application so as to be affordable, scalable, and user-friendly. With better insight,
businesses may find that not all use cases require the most robust of security measures. For
example, fitness trackers typically track activity such as the number of steps, number of calories
burned, etc., so the device may not need a password to activate or need to be physically chained
down. However, the complex, connected machinery that builds the parts of an airplane warrants
physical security in addition to a series of virtual locks to prevent unauthorized access as well as
vetting of individuals(s) who are permitted to remotely operate that machinery.
Lastly, the enterprise needs to keep in mind that not all
potentially security problems need to be solved right away.
Security should be about incremental changes and should not
pose a fundamental challenge that is insurmountable. In other
words, we don’t have to build security such that is bulletproof
for the next 20 or 30 years. To aim for such a goal is
impossible as we don’t know, with certainty, what innovations
will occur or how the market will change in the future.
SECURITY CONCERNS WILL CONTINUE
Security should be
about incremental
changes and
should not pose
a fundamental
challenge that is
insurmountable.

As billions of devices, things, and processes become interconnected, they will create a massive
volume of data that will drive the need for IoT analytics. IDC predicts that 25 million applications
will be created, and 50 trillion gigabytes of data will be generated by 2020. Analysts predict that,
by 2019, IoT-created data will be stored, processed, analyzed and acted upon close to, or at the
edge of the network, thus relieving part of the data proliferation challenge.3
The need for data analytics will be so great that it will drive
demand for jobs for individuals that specialize in data
science. While the issue of how to get data off devices and
into back-end systems is increasingly being resolved through
edge computing, the challenge of who gets to monetize the
data is not yet resolved. Also, there isn’t much clarity in who
plays the role of the broker of all that data and who manages
the data repository. There might be multiple roles or one.
As the increasing demand for data scientists to help make
sense of the data is met, organizations will start to utilize the
data for predictive purposes so it drives better, more efficient
organizational decisions rather than as a passive activity of
analyzing the data after the fact.
A world where there are more connected devices than people has already come and gone. As
with any new, fast-growing technology, the Internet of Things is not without challenges. However,
the opportunities are vast, and those that navigate the waters thoughtfully can find great success.
IOT VALUE WILL BE REALIZED THROUGH DATA ANALYTICS
The need for data
analytics will be
so great that it will
drive demand for
jobs for individuals
that specialize in
data science.
3
“IDC FutureScape: Worldwide Internet of Things 2016 Predictions,” IDC, November 4, 2015.
Data
Modeling
Data
Analytics
Business
Intelligence
Reporting
Data
Architecture
Structure
Data
Migration
Extract,
Transform
Load
Figure 25: Data analytics

Directory of IoT/M2M
Industry Terms
111 DIRECTORY OF IOT/M2M
INDUSTRY TERMS
The Definitive Guide | 2nd Edition, The Internet of Things for Business. Directory of IoT/M2M Industry Terms | 110

The Definitive Guide | The Internet of Things for Business, 2nd Edition. Directory of IoT/M2M Industry Terms | 111
Directory of IoT/M2M Terms
1xEV-DO 1 times Evolution Data Optimized (used in ANSI-2000 CDMA).
1xRTT 1 times Radio Transmission Technology (used in ANSI-2000 CDMA).
2.4 GHz
A short-range wireless band commonly used in wireless technologies such as
Wi-Fi, Bluetooth, and ZigBee.
2G
The second generation of GSM cellular technology that improved performance
by adding to the cellular radio spectrum to help solve coverage issues and
drops in signal due to urban obstacles. It was also the turning point in moving
from analogue transmission methods to digital, adding digital encryption and
paving the way for cellular data usage.
3G
The third generation of GSM cellular technology, offering substantially improved
data transfer rates over its predecessor, 2G. While the original release of 3G
used the UMTS method, improvements have been made to increase capacity
and data speeds with additional protocols including HSPA.
3GPP Third Generation Partnership Project (GSM family of technologies).
3GPP2 Third Generation Partnership Project 2 (CDMA family of technologies).
4G
The fourth generation of GSM cellular technology and the latest upgrade to
the GSM network, providing greater data transfer speeds. 4G is also referred
to as LTE.
6LoWPAN
A communication protocol that compresses Ipv6 packages for small, low
power-devices to let them communicate within the Internet of Things.
802.11ah
New Wi-Fi protocol that uses sub 1 GHz license-exempt bands as opposed to
conventional Wi-Fi that operates in the 2.4 GHz and 5 GHz bands.
868 MHz
License-free RF band commonly used for short-range applications such
as thermostats, burglar alarms, and industrial uses.

92 MHz
License-free RF band commonly used for short-range applications. The low
frequency allows for better penetration through walls and obstacles; however it
has a low data transfer rate.
AAA Authentication, Authorization, and Accounting (see also RADIUS).
ACaaS Access Control as a Service.
Acceleration
Sensing
A MEMS concept referring to the increase in movement of an object from one
point to another along a straight line or axis. Typical applications include remote
control, pointing devices, gesture recognition, fitness monitoring equipment, etc.
Accelerometer
A tool that measures changes in gravitational acceleration in the unit it may be
installed in. Accelerometers are used to measure acceleration, tilt, and vibration
in many devices.
Access Control
A system that determines who, when, and where people are allowed to enter
or exit a facility or area. The traditional form of access control is the use of door
locks, but modern access control may include electronic systems and wireless
locks. Access control may also apply to cybersecurity.
Access Control as
a Service (ACaaS)
A recurring fee-based system where a facility manager outsources electronic
access control to a third party. Each facility need not maintain a dedicated
server.
Access Point A Wi-Fi node that allows users entry to a network, typically a LAN.
Active Sensor A sensing device that requires an external source of power to operate.
Actuator
A device that introduces motion by converting electrical energy into mechanical
energy in an electromechanical system. (An actuator may also stop motion by
clamping or locking.) A dynamo is an example of an actuator.
ADAS Advanced Driver Assistance Systems.
Additive
Manufacturing
The industry-specific term for 3D printing, involving building products by adding
layers rather than the traditional technique of removing material via milling.

Address
Resolution
Protocol (ARP)
A communication protocol used to convert an IP address into a physical
address. This way, computers can communicate with each other, despite only
knowing each other’s IP addresses, by sending an ARP request that informs
them about the other computer’s MAC address.
Addressability
The capacity for an entity to be targeted and found. To be addressable, an
entity must be uniquely identifiable, meaning that it must be associated with
something — typically an alphanumeric string — that is not associated with
anything else that exists within that system.
Advanced Driver
Assistance
Systems (ADAS)
Digital features incorporated into vehicles to enhance driver safety and
performance. ADAS functionality includes digital vision for lane departure
warnings, blind spot detection, radar for collision avoidance, and V2V
communication for multiple vehicles operating near each other. The data and
connectivity integral to ADAS transforms vehicles into IoT devices.
Advanced
Encryption
Standard (AES)
The specification for encryption of electronic data established in 2001.
Operates on a public/private key system, and planning for key management is
an important aspect when implementing AES encryption.
Advanced
Message Queuing
Protocol (AMQP)
An open-source standard for business message communication. Main features
include message orientation, queuing, routing, reliability, and security.
Advanced
Metering
Infrastructure
An architecture for automated, two-way communication between a smart utility
meter with an IP address and a utility company.
Advanced Mobile
Phone System
(AMPS)
An analog cellular mobile system using FDMA. Analog AMPS has been
supplanted by digital in much of the world.
Amazon Web
Services (AWS)
The name given to a collection of remote computing services, offered by
Amazon.com, that combine to make a cloud computing platform.
Ambient Assisted
Living (AAL)
Intelligent systems to assist the elderly and others with daily care activities,
often through IoT technology. Application fields are security (for example,
observation), functionality (such as automated light switches), and even
entertainment.
Ambient
Intelligence
Sensor-filled environments that interpret and react to human events and activity,
and learning to adapt over time, the environment’s operation and services
change based on that activity.

AMPS Advanced Mobile Phone System.
AMQP Advance Message Queuing Protocol.
Android Wear
An open-source platform that extends the Android system to wearables. The
software development kit includes an emulator.
Anomaly
Detection
A statistical technique that determines what patterns are normal and
then identifies items that do not conform to those patterns. Unlike simple
classification where the classes are known in advance, in anomaly detection the
users don’t know what they are looking for in the data.
ANSI-136 American National Standards Institute Standard 41, for TDMA cellular.
ANSI-2000 American National Standards Institute Standard 41, for CDMA2000 cellular.
ANSI-41
American National Standards Institute Standard 41, for control signal
messaging on SS7.
ANSI-95 American National Standards Institute Standard 41, for CDMA cellular.
API Application Programming Interface.
Application
Programming
Interface (API)
A collection of commands and protocols used to interact with an operating
system, device, or specific software component. In IoT, an API lets the developer
access the functionality of a device or sensor, such as a thermometer’s readings.
APIs can be public or restricted to authorized users only.
Application
Software
Programs that enable specific, end-user actions. This means the software uses
the given potential provided by computers to form an application. Examples
include Microsoft Word (text editing), Adobe Photoshop (image editing), and
many other programs.
Application
Specific Sensor
Nodes (ASSN)
Integrating sensors and sensor fusion in a single device, ASSNs have a built-
in intelligence to cope with the complexity of applying multiple sensors to a
specific problem such as augmented reality, navigation, positioning, and more.
Bosch Sensortec’s BNO055 is an example of an ASSN.
Arduino
A single-board microcontroller used for prototyping without having to deal with
breadboards or soldering. The software to operate an Arduino is free and open source.

ARP Address Resolution Protocol.
ASSN Application Specific Sensor Nodes.
AT Commands
Attention commands, developed by Dennis Hayes, that are used to set data
connections. The set of short string commands allow developers to set up calls
with a modem, as well as perform far more complex tasks. For an example of
an AT command set, take a look at Telit’s 3G module, the HE910, AT command
directory.
Audio Profile
Hardware profile used with Bluetooth applications that include custom
AT commands and functionality dedicated to wireless streaming of audio.
Examples include A2DP, which allows for streaming of audio to devices such
as speakers, where as an audio gateway profile allows for two-way audio
communication used in devices such as headsets.
Augmented Entity
A physical entity is represented by a virtual entity on the digital level. An
augmented entity combines the two and stands for any combination of the two
entities.
Automated
Identification and
Mobility (AIM)
Technologies
A group of technologies that are used to identify, store, and communicate data.
An example would be a barcode, though there are many technologies in this
area that are used for different services and are often used in combination.
Automatic Call
Delivery
A feature that allows a user or device to receive calls when roaming outside of
the device's home coverage area.
AWS Amazon Web Services.
BAN Body Area Network.
Band
A range of frequencies used by a technology for communication purposes. For
example, the 2.4 MHz band is used for Wi-Fi and Bluetooth communication.
Bandwidth
In signal processing, the measure of the width of a range of frequencies. In
computing, the rate of data transfer. Both are referenced in IoT.
Base Station (BS)
The radios and other equipment at the cell sites that are used to communicate
with the cellular devices.

Base Station
Controller (BSC)
The equipment that consolidates and controls multiple BS sites (usually, more
than one BS is attached to a BSC).
Base Transceiver
Station (BTS)
This is a machine that enables wireless communication between user
equipment, for example a mobile phone or a computer, and networks like the
GSM network. The data is received through an antenna and is then processed
and transmitted by the BTS to create a wireless connection.
Beacons
Low-cost devices that communicate with smartphone apps indoors, without
the need for GPS. Beacons use BLE and are key enablers for the smart retail
category, triggering messages as consumers pass through locations or near
products.
Big Data
Data sets so large that they cannot be used with traditional database tools. Big
Data often requires massively parallel computing resources to access, curate
and analyze. Big Data analysis techniques are crucial to such disciplines as
spotting business trends and simulation.
BLE Bluetooth Low Energy.
Bluetooth
Short-range wireless technology standard which operates on the 2.4 MHz
band. Bluetooth can be used for sending both data and audio, with popular
uses including wireless headsets and cordless keyboards. Bluetooth devices
can be set up with different hardware profiles to help perform specific tasks, for
example audio adapter, audio headset, serial, and keyboard profiles.
Bluetooth 4.0
(BLE)
The latest iteration of Bluetooth, also called Bluetooth Low Energy (BLE).
It offers lower power use for portable devices and new profiles including
Bluetooth Mesh, a Bluetooth topology that allows devices to be connected
together, sending/repeating commands from the hub to any connected device.
Apple’s iBeacon is an example of a BLE application, and BLE as many potential
uses for IoT devices.
Bluetooth LE
(BLE)
Bluetooth Low Energy.
Bluetooth Low
Energy (BLE)
The latest iteration of Bluetooth, also called Bluetooth 4.0. It offers lower
power use for portable devices and new profiles including Bluetooth Mesh,
a Bluetooth topology that allows devices to be connected together, sending/
repeating commands from the hub to any connected device. Apple’s iBeacon
is an example of a BLE application, and BLE as many potential uses for IoT
devices.

Body Area
Network (BAN)
A wireless network of wearable computing devices and physiological sensors,
which may even be embedded inside the body. A BAN may also be referred to
as a WBAN (wireless body area network) or a BSN (body sensor network). A
key use case for BANs is e-Health applications.
Brick
Slang term for accidentally rendering a device inoperable by changing its
configuration or shorting one of its circuits. Used as a verb, as in “what do I do
if I brick my Raspberry Pi?” The inert device sits there like a brick.
Bring Your Own
Device (BYOD)
Enterprise term recognizing that people are bringing their own Wi-Fi enabled
devices into the corporate network.
Broadband A high-speed, always-on data communications channel.
Brownfield
Brownfield describes the problem and the process of having to consider
already existing systems when implementing new software systems.
BS Base Station.
BSC Base Station Controller.
BTS Base Transceiver Station.
Business Logic
Used to describe processes that are necessary to enable or execute
communication between an end user and a database/server. These processes
decide how data is transmitted, transformed, or calculated. This does not
include the display of data or task-specific commands. It serves as a basis,
consisting of algorithms, code, etc.
BYOD Bring Your Own Device.
CAN Controller Area Network.
CAN Bus
A message-based, multi-master serial protocol for transmitting and receiving
vehicle data within a Controller Area Network (CAN). Sometimes written as
“CANbus,” the CAN Bus connects multiple Electronic Control Units (ECUs)
also known as nodes. Designed initially for automotive applications in 1983,
the CAN Bus can be adapted to aerospace, commercial vehicles, industrial
automation, and medical equipment.

Card Not Present
(CNP)
The type of credit transaction where the merchant never sees the actual card.
CNP has the obvious potential for fraud but is vital for newer services such as
contactless mobile payments.
Carrier A company that provides telecommunication services. See also Operator.
CDMA Code Division Multiple Access.
Cellular Modem
Allows a device to receive Internet access over the cellular mobile networks.
Devices can also be configured to remotely connect to a server or device to
enable off site access and data collection.
Cellular Router
Allows connected devices to access servers and devices by making an IP
connection through the cellular mobile network. Routers allow for multiple
devices to be connected and controlled, while built in Open VPN, IPSEC,
PPTP, and L2TP, and offer extra device and data transfer security to keep your
information safe.
Chief IoT Officer
(CIoT)
One of the CxO class of corporate officers, the CIoT coordinates the integration
of IoT into the enterprise. Successful CIoTs will break down silos between
disciplines such as big data, data analytics, security, communications
protocols, etc.
CIoT Chief IoT Officer.
Class 1 Bluetooth
Offers a greater wireless data transfer distance (over 100m, up to 1km) through
using greater power consumption (100mW).
Class 2 Bluetooth
Short-range wireless data transmission (10-20m) which has low power
consumption of around 2.5mW.
Cloud
Or the Cloud, meaning cloud computing. The name “cloud” comes from
the fluffy cloud typically used in Visio-style network diagrams to represent a
connection to the Internet.
Cloud
Communications
Communication services being provided by third parties that can be accessed
and used through the Internet. The program Skype is one well-known cloud
communications application.

Cloud Computing
An approach where information technology capacities (such as storage or
applications) are separated from the individual computer and are supplied
through the Internet (or an Intranet-based service) at the user’s demand. The
“as-a-Service” moniker is sometimes used for cloud computing services, such
as Software-as-a-Service, Platform-as-a-Service, and Infrastructure-as-a-
Service. The backend for many IoT devices may be delivered via the cloud.
Cloud
Orchestration
The automated management of a cloud. This includes all services and systems
that are part of the cloud as well as the flow of information.
CNP Card Not Present.
CoAP Constrained Application Protocol.
COBie Construction Operations Building Information Exchange.
Code Division
Multiple Access
(CDMA)
Digital cellular phone service method that separates multiple transmissions
over a finite frequency allocation using Spread Spectrum techniques (concept
invented and patented by Hedy Lamar).
Cognitive Vehicles
A term coined by IBM to describe vehicles that will learn from the behaviors of
drivers, occupants, and vehicles around them, plus be aware of the vehicle’s
own condition and the state of the surrounding environment. A cognitive vehicle
will thus be capable of configuring itself to a specific driver, other occupants,
and various conditions.
Communication
Model
Communication models try to capture, explain, simplify, and then model
communication. One of the oldest and most famous models, the Shannon and
Weaver Model, was created in 1949.
Companion Device
In wearables, a companion device requires a parent device, such as a
smartphone, to fully operate. The opposite would be a standalone device
that can do everything on its own. A companion wearable will typically use
Bluetooth to communicate with the parent.
Connected Home
If the devices in a house work interactively and information relevant to residents
is accessed via high-speed broadband, it could be called a connected home.
This may mean that the refrigerator reports the almost empty milk or that the
TV reminds you of your doctor’s appointment because it automatically gets this
information from the doctor’s computer. Related to Smart Home.

Constrained
Application
Protocol (CoAP)
This software protocol is used in small electronics devices and serves as the
interactive communication between those devices.
Construction
Operations
Building
Information
Exchange (COBie)
The COBie approach simplifies the capture and recording of building project
handover data, basically by entering things like serial numbers as the project
progresses. COBie breaks down the design into Facility, Floor, Space and Zone
elements. COBie can be displayed in several interoperable formats.
Control Network
A network of nodes that collectively monitors, senses, and controls or enables
control of an environment for a specific purpose. A home appliance network is
a one example of a control network.
Controller Area
Network (CAN)
In automobiles, a CAN connects Electronic Control Units (ECUs) using a multi-
master serial bus (the CAN bus) to control actuators or receive feedback from
sensors. ECUs can be subsystems such as airbags, transmission, antilock
brakes, or most importantly, engine control. The standard consists of ISO
11898-1 and ISO 11898-2.
COPE Corporate-Owned, Personally Enabled.
Corporate Owned,
Personally
Enabled (COPE)
A compromise around pure BYOD, COPE devices allow the user to control
much of the data on the device, but the enterprise controls the security model.
Cortex-A
Cortex-A refers to a series of processors from ARM that are equipped with
ARMv7 and ARMv8 command sets. They are used for applications that require
a lot of processing power, mainly in the areas of mobile handset (smartphones),
computing, digital home, automotive, enterprise, and wireless infrastructure.
Cortex-M
Cortex-M is a family of microprocessors developed by ARM which is mainly
used in microcontrollers. They range from the cheapest M0 processor up to the
Cortex-M4, which is used for effective digital signal control. Applications are
found in automotive, gaming, and intelligent consumer products.
CPS Cyber-Physical Systems.
CR2032
A battery rated at 3.0 volts commonly used in watches, wireless doorbells, and
other small devices. Sometimes referred to as a “button cell” or “lithium coin,”
the battery is shaped like a coin with dimensions of 20mm diameter x 3.2mm
height (from which the “2032” is derived). The CR2032 is twice as thick as the
CR2016.

Cyber-Physical
Systems (CPS)
Systems that combine computer-related and mechanical aspects. A
smartphone, for example, combines software, hardware, etc., with a physical
device. In general, many mobile or embedded technologies or devices can be
called Cyber-Physical Systems, thus applications are manifold. The systems
often include some form of sensor which can transfer attributes from the real
world to the digital sphere.
Dashboard
A user interface that presents key information in a summarized form, often as
graphs or other widgets. Derived from the classic automobile dashboard, the
design of the interface depends on what information needs to be monitored or
measured.
Data Center
A collective term for the physical site, network elements, systems, etc., that
supports computing and network services.
Data Janitor
A subtask of data science concerned with the cleaning up of dirty or duplicative
data. Oftentimes the janitor must get data into the correct columns and sort it.
Data Lake
Coined by Pentaho CTO James Dixon, a data lake is a massive data repository,
designed to hold raw data until it’s needed and to retain data attributes so as
not to preclude any future uses or analysis. The data lake is stored on relatively
inexpensive hardware, and Hadoop can be used to manage the data, replacing
OLAP as a means to answer specific questions. Sometimes referred to as an
“enterprise data hub,” the data lake and its retention of native formats sits in
contrast to the traditional data warehouse concept.
Data Scientist
A job that combines statistics and programming, using languages such as R, to
make sense of massive data sets. IoT sensors, for example, create mountains
of data, and the data scientist’s role is to extract valuable information and
detect anomalies.
Data-Driven
Decision
Management
(DDDM)
An approach to business governance valuing decisions that can be backed up
with verifiable data.
Datakinesis
A termed coined by Marc Blackmer, datakinesis occurs when an action taken
in cyberspace has a result in the physical world. Industrial Control Systems, for
example, are vulnerable to datakinetic attacks where physical equipment such
as valves and sensors are compromised and damaged by hackers. Stuxnet is
one such example.
DDDM Data-Driven Decision Management.

DDS Digital Data Storage.
Degrees of
Freedom (DoF)
An engineering concept used in MEMS that describes the directions in which
an object can move and generally the number of independent variables in a
dynamic system.
De-identification
The stripping away of personally identifiable information from data prior to its
use. The process must include the removal of both direct identifiers (name,
email address, etc.) and the proper handling of quasi-identifiers (sex, marital
status, profession, postal code, etc.).
Demand Response
(DR)
The voluntary reduction of electricity use by end users in response to high-
demand pricing. Demand response can reduce electrical price volatility during
peak demand periods and help avoid system emergencies. An example of DR
would be a utility paying Nest to have thermostats turn down air conditioners in
empty homes on a hot day.
Device Attack
An exploit that takes advantage of a vulnerable device to gain access to a
network.
DG Distributed Generation.
Digital Data
Storage (DDS)
This format is used to store computer data on audio tape. It was developed by
HP and Sony in 1989 and is based on the digital audio tape (DAT) format and
was a widely used technology in the 1990s.
DIN Rail A metal rail used for mounting electrical equipment and racks.
Distributed
Generation (DG)
Decentralized, modular, and flexible power generation located close to the
serviced loads. Distributed microgrids can control smaller areas of demand with
distributed generation and storage capacity.
DL or D/L Downlink.
DNP3 Protocol
An open, standards-based protocol for the electric utility industry with
interoperability between substation computers, remote terminal units, intelligent
electronic devices), and master stations. Groups of enabled things are
organized into namespaces.
DoF Degrees of Freedom.

Domain Model
A model that contains all areas and terms related to a certain field of interest.
It includes attributes, relations, constrains, acts, etc., that are relevant for a
certain task.
Domotics
A combination of domestic and robotics. Also a composite of the Latin domus
and informatics, domotics includes home automation systems, home robots,
whole house audio/visual systems, and security systems. Domotic devices
have the ability to communicate with each other.
Downlink (DL or
D/L)
The process of downloading data onto an end node from a server/target
address. In a cellular network this would be seen as data being sent from a
cellular base station to a mobile handset.
DR Demand Response.
EAN European Article Number.
ECU Electronic Control Unit.
EDGE Enhanced Data rates for GSM Evolution.
E-Health
Or eHealth, telehealth, telemedicine, and related to mHealth. This is the support
of medical processes and applications through information and computer
technologies. It can include the gathering and communication of data as well as
automated responses of certain devices and processes.
Electronic Control
Unit (ECU)
Also known as a node, an Electronic Control Unit is a device, such as a sensor
or actuator, that is connected to other devices via a CAN Bus. A vehicle can
contain dozens of ECUs for functions such as mirror adjustment, window
power, airbags, cruise control, entertainment, and, most significantly, engine
control. To form a CAN, two or more ECUs are needed.
Electronic Serial
Number (ESN)
Unique identification numeral for mobile devices in CDMA. Replaced by the
MEID.
Electrostatic
Discharge (ESD)
This sudden flow of electricity can occur if two electrical objects with different
electrical charge come in contact with each other. The difference in charge is
often due to friction. Sometimes, the short process is accompanied by sparks,
as can be seen with lightning. ESD can lead to severe damage to electrical
devices (such as generators).

Embedded Device
Hacking
The exploiting of vulnerabilities in embedded software to gain control of the
device.
Embedded
Firmware
The flash memory chip that stores specialized software running in a chip in an
embedded device to control its functions.
Embedded
Software
Specialized programming in a chip or on firmware in an embedded device to
control its functions.
Embedded System
Security
The reduction of vulnerabilities and protection against threats in software
running on embedded devices.
Embrace, Extend,
and Extinguish
A strategy associated with Microsoft to defeat open standards with proprietary
extensions. Many IoT projects are open source, so this strategy would be
anathema to open development.
EMD Enterprise Mobile Duress.
EMI Protocol
An extension to the UCP (Universal Computer Protocol). It’s used to connect
to Short Message Service Centers which store, transform, and send short
messages.
Energy-
Harvesting
Technologies
Technologies which use small amounts of energy from close proximity to power
small wireless devices. Applications can be found in wireless sensor networks
or wearable tech. Energy sources are, among others, sun, wind, or kinetic
energy.
Enhanced Data
rates for GSM
Evolution (EDGE)
This is an enhancement made to 2G GSM networks to improve data transfer
speeds and provides downlink speeds of up to 1 Mbit/s and uplink speeds of
up to 400 kbit/s. It builds on available GSM or GPRS standards and is thus
easily integrated into the existing network.
Enterprise Mobile
Duress (EMD)
Systems designed to detect personnel emergencies within large facilities, such
as hospitals or campuses, where determining the physical location of persons
in distress is a critical issue. EMD systems are a robust extension of a Personal
Emergency Response System (PERS) into the enterprise, focusing on the
protection of people from emergency incidents such as violence.
EPCglobal
A nonprofit organization founded by GS1 (former EAN International) and GS1
US (former UCC). It serves to spread, improve, and standardize the Radio
Frequency Identification (RFDI) technology and to support communication of
gathered data through the Internet.

ESD Electrostatic Discharge.
ESN Electronic Serial Number (in CDMA). Replaced by the MEID.
EtherCAT
A fieldbus system developed by Beckhoff, which allows for real-time Ethernet.
It helps to achieve short data update times, accurate synchronization, and low
hardware costs, so it can be used specifically for automated or control systems.
CAT stands for Controller and Automation Technology.
European Article
Number (EAN)
This is used to mark and identify products. Since 2009, it is also called GTIN
(Global Trade Item Number). The number is usually found beneath barcodes
and consists of up to 13 digits (EAN 13 barcode).
EV-DO Enhanced Voice-Data Only (also Enhanced Voice-Data Optimized).
Facility
A structure designed and constructed for a particular purpose, such as a
medical facility.
FAKRA
Fachnormenausschuss Kraftfahrzeugindustrie. This is a type of SMB connector
used in the automotive industry for connecting coaxial RF connectors which
uses snap on connectors.
Fast Data
This is the application of Big Data analytics to smaller data sets in near-real or
real-time to solve a problem or create business value.
FDMA Frequency-Division Multiple Access.
Firmware
Programming that’s written to the read-only memory (ROM) of a computing
device. Firmware, which is added at the time of manufacturing, is used to run
user programs on the device.
Firmware Over-
the-Air (FOTA)
The process of updating a mobile phone’s operating system and software over
the network, rather than having the consumer come into a service center for
updates.
Fitness Band
A type of activity tracker worn on the wrist, with sensors specifically related to
exercise and activity measuring. In contrast to a smartwatch that may include
fitness/activity tracking features, a “fitness band” is primarily dedicated to
fitness.

Fleet Management
A broad term referencing a range of solutions for vehicle-related applications. A
fleet management solution is typically a vehicle-based system that incorporates
data logging, satellite positioning, and data communication to a back-office
application.
Fog Computing or
Fogging
Also known as fogging, this is a distributed computing infrastructure in which
some application services are handled at the network edge in a smart device
and some application services are handled in a remote data center — in the
cloud.
Form Factor
The physical size, pin-out, and configuration of a component. A family range
of module, for example, may include 2G, 3G, and 4G variants to allow PCB
designers to design in one module but allow for future upgrades through the
product family’s road map.
FOTA Firmware Over-the-Air.
Frequency-
Division Multiple
Access (FDMA)
The division of the frequency band allocated for wireless cellular telephone
communication into 30 channels, each of which can carry a voice conversation
or digital data.
Galileo
Developed by the European Union and Space Agency, Galileo is a global
positioning constellation of satellites which is still in development and will be
made up of 30 satellites (27 operations and thee active spares).
Gateway
A link between two computer systems or programs. This way they can share
information with each other. The router for your home Internet is one type of
gateway.
Gateway GPRS
Support Node
(GGSN)
A main component of a GPRS network that supports the networking between
the GPRS network and external packet-switched networks. See also SGSN.
General Packet
Radio Service
(GPRS)
A wireless communications standard on 2G and 3G cellular networks which
supports a number of bandwidths and provides data rates of 56-114 kbps. As
cellular companies move to more advanced networks, GPRS networks may be
more cost-effective for IoT networks.
Geofence
A virtual border applied to a physical space. For example, geofencing might
be defined around a nursery, and when a mobile device crosses the nursery
boundary, an alert is generated. Geofences may be dynamically created and in
a telematics application can encompass entire neighborhoods or cities.

Geographic
Information
System (GIS)
The combination of hardware, software, and data that captures, manages,
analyzes, and presents many kinds of geographic data. GIS and location
intelligence applications can be the foundation for location-enabled services.
GeoJSON
A dialect of JSON that describes physical places. Features modeled by
GeoJSON are points, line strings, polygons, and multipart groups of these
types (MultiPoint, MultiLineString, MultiPolygon). Numerous mapping and GIS
software packages employ GeoJSON.
Geotagging
The process of tagging a photo, video, or other types of media with
coordinates, thus marking it with a location.
GGSN Gateway GPRS Support Node.
GIS Geographic Information System.
Global Navigation
Satellite System
(GNSS)
Used when talking about different constellations of satellite navigation systems.
Global Positioning
System (GPS)
A system of satellites and radio transmissions that can be used to locate GPS‐
enabled hardware anywhere on the planet to a very good accuracy.
Global System
for Mobile
communication
(GSM)
This is the most widely used digital cellular network and the basis for mobile
communication such as phone calls and the short message service (SMS).
GLONASS
The Russian global navigation satellite system with a constellation made of 24
satellites orbiting Earth. These multi-constellation GPS modules allow users
to access multiple satellite networks, and accessing extra satellites allows for
faster and more accurate positioning as well as offering greater resilience when
satellites are obscured in areas such as cities.
GNSS Global Navigation Satellite System.
GPRS General Packet Radio Service.
GPS Global Positioning System.

Greenfield
In contradiction to brownfield, a greenfield project is a one where no
consideration of previous systems is needed, thus already existing standards
can be ignored.
GSM Global System for Mobile communication.
GSM MAP GSM Mobile Application Part, for control signal messaging on SS7.
HaaS Hadoop as a Service.
Hadoop
A Java-based, distributed programming framework for processing large data
sets. An application can be broken down into numerous small parts, called
fragments or blocks, that can be run on any node in the cluster. Hadoop is free
and part of the Apache project, sponsored by the Apache Software Foundation.
Hadoop as a
Service (Haas)
The running of Hadoop in the Cloud, requiring no local hardware or IT
infrastructure. The service is typically elastic, allowing the adding or removal of
nodes depending on user needs.
Hadoop
Distributed File
System (HDFS)
The primary distributed storage used by Hadoop applications. A HDFS cluster
has a NameNode that manages the file system metadata and DataNodes to
store the actual data.
Handoff
The transfer of a wireless call in progress from one transmission site to another
site without disconnection.
Haptic Technology
or Haptics
Also referred to as Haptics or “touch feedback,” haptic technology applies
tactile sensations to human interactions with machines. The simplest example
is the actuator that vibrates a cell phone, but more advanced haptics can
detect the pressure applied to a sensor, affecting the response.
HDFS Hadoop Distributed File System.
Heating,
Ventilation, and
Air Conditioning
(HVAC)
Sometimes grouped with refrigeration as HVACR, these systems cover both
vehicular and indoor building comfort control.
HEM Home Energy Management.
HEMS Home Energy Management System.

Heterogeneous
Network (HetNet)
Small cell networks using both macro and small cells. HetNets allow mobile
operators to better utilize their data networks’ capacity.
HetNet Heterogeneous Network.
High Speed
Downlink Packet
Access (HSDPA)
This increases the capacity of UMTS/3G bandwidth to allow for faster
download speeds for connected devices.
High Speed Packet
Access (HSPA)
An improvement made to transfer speeds over 3G technology through the
addition of two new protocols; HSDPA and HSUPA. It offers potential downlink
speeds of 14 Mbit/s and downlink of 5.76 Mbit/s.
High Speed Uplink
Packet Access
(HSUPA)
An improvement made to UMTS to enable faster uploading of data from
devices, increasing capacity and throughput while reducing delay.
HLR Home Location Register.
Home Automation
The automation of certain activities within a household. This can include
automated control of lights, doors, and air conditioning, for example.
Home Energy
Management
The process of increasing energy efficiency and savings for residential
customers, ideally within a larger smart-grid environment.
Home Energy
Management
System (HEMS)
Equipment and services that optimize energy use in a residential setting while
maintaining comfort. A HEMS includes smart appliances, home gateways,
smart meters, and information exchange with local utilities via a smart grid.
Home Location
Register (HLR)
The main database of permanent subscriber information for a mobile network.
Host
Computers that provide (or host) certain services or resources within a network
that other participants within the network can then access and use. Hosts are
the hardware basis for servers, as servers are run on hosts. Often times, they
are the central point in a company’s data processing process.
HSDPA High-Speed Downlink Packet Access.
HSPA High-Speed Packet Access.

HSPA+ Enhanced or Evolved High-Speed Packet Access.
HSUPA High-Speed Uplink Packet Access.
HVAC Heating, Ventilation, and Air Conditioning systems.
Hybrid Cloud
A mix of public and private cloud. The distribution of services through private or
public channels is decided upon by the users.
I2C Inter-Integrated Circuit
IaaS Infrastructure as a Service.
iBeacon
A technology introduced by Apple that uses sensors to locate iOS or Android
devices indoors and can send them notifications via Bluetooth Low Energy
(BLE). This can be also used in stores or museums to give further information
about item nearby.
ICCID Integrated Circuit Chip Identifier.
ICT Information and Communication Technologies.
Identifier
Also just ID, this marks objects for clear identification. Identifiers are usually
letters, words, symbols, or numbers that can also be used to create a code that
reveals a definite identity after it is decoded.
Identity
Recognizable attributes that are linked to an object, a person, etc. Those
attributes expose the entity and allow for clear identification. If two things have
the exact same attributes, they usually have the same identity, and they can’t
be distinguished from each other.
Identity of Things
(IDoT)
An area that involves assigning unique identifiers with associated metadata
to devices and objects (things), enabling them to connect and communicate
effectively with other entities over the Internet.
IDoT Identity of Things.
IEEE 802.11
The family of specifications developed by the IEEE for wireless LAN (WLAN)
communications, first adopted in 1997. The addition of a letter, such as
802.11b, indicates a particular specification.

IEEE 802.11ac
Approved in January 2014, this is a wireless standard for high-throughput
wireless local area networks (WLANs) on the 5 GHz band. In contrast to the four
MIMO spatial streams in 802.11n, the 802.11ac standard supports eight.
IEEE 802.11n
Builds on previous 802.11 standards to use multiple antennas to increase
data rates, adding MIMO to the physical layer. The full specification name is
802.11n-2009, which is an amendment to IEEE 802.11-2007.
IEEE 802.11p
Amends wireless access in vehicular environments (WAVE) to the IEEE 802.11
Wi-Fi standard. The amendment defines a way to wirelessly exchange data
without the need to establish a basic service set (BSS), since links to roadside
infrastructure may be available only for a limited amount of time. 802.11p uses
channels of 10 MHz bandwidth in the 5.9 GHz band.
IGES Initial Graphics Exchange Specification.
IGMP Internet Group Management Protocol.
IIoT Industrial Internet of Things.
IMEI International Mobile Equipment Identifier.
IMS Intelligent Maintenance System.
IMSI International Mobile Subscriber Identifier.
IMU Inertial Measurement Unit.
Industrial Control
System (ICS)
Computer hardware and software that monitor and control industrial processes
that exist in the physical world, where operator-driven supervisory commands
can be pushed to remote station devices. Industries such as electrical, water,
oil, and gas are typical ICS users.
Industrial Internet
of Things (IIoT)
A subdiscipline of IoT, encompassing connected large-scale machinery and
industrial systems such as factory-floor monitoring, HVAC, smart lighting, and
security. This is M2M communication where, for example, equipment can send
real-time information to an application so operators can better understand how
efficiently that equipment is running. Also referred to as Industry 4.0, Industrie
4.0, and Industrial IoT.

Industrial,
Scientific, and
Medical (ISM)
Bands
An unlicensed part of the RF spectrum used for general purpose data
communications. In the US, the ISM bands are 915MHz, 2.4 GHz, and 5.5
GHz, whereas 2.4 GHz is the global unlicensed frequency and has increasing
amounts of interference.
Industrie 4.0
Invoking a fourth Industrial Revolution, Industrie 4.0 creates intelligent
manufacturing networks where decentralized smart factories can communicate
and react to each other autonomously. For example, in an Industrie 4.0 factory,
self-predictive systems would trigger maintenance processes autonomously
and automatically adapt logistics to the resulting changes in production. The
term, also known as Industry 4.0, was first used at the Hannover Messe in
2011.
Industry 4.0
Industry 4.0 is a project introduced by the federal government of Germany and
refers to the fourth Industrial Revolution. It is a strategy which aims to make
better use of current and future IT-capacities in traditional industries. Also see
Industrie 4.0.
Inertial
Measurement Unit
(IMU)
A MEMS module which measures angular velocity and linear acceleration using
an accelerometer triad and an angular rate sensor triad. Other IMU sensors may
include magnetometers and pressure sensors.
Information and
Communication
Technologies (ICT)
The ICT Industry provides access to information through telecommunications.
The communications technologies can be things like the Internet, VOIP, wireless
networks, or mobile phones.
Infrastructure as
a Service (IaaS)
An on-demand business model for IT-capacities. Instead of owning IT-
infrastructure or server space, you rent it and pay for it on a per-use basis.
Those capacities are usually owned, maintained, and provided by a cloud
service.
Initial Graphics
Exchange
Specification
(IGES)
This is a vendor-neutral, standardized file format used to transfer information
between computer-aided design programs. The standard was developed to
create a uniform method for exchanging graphical data between the programs.
Insurance
Telematics
Vehicular tracking devices used by automobile insurance companies to alter
rates based on driver behavior. Currently, Progressive (Snapshot), Allstate, and
others typically track braking and mileage. An excessive number of hard-brakes
may indicate risky driving habits, for example.
Integrated Circuit
Chip Identifier
(ICCID)
A unique number used to identify a SIM card. ICCIDs are stored in the SIM
cards and are also engraved or printed on the SIM card.

Intelligent Device
Any type of equipment, instrument, or machine that has its own computing
capability. As computing technology becomes more advanced and less
expensive, it can be built into an increasing number of devices of all kinds. In
addition to personal and handheld computers, the almost infinite list of possible
intelligent devices includes cars, medical instruments, geological equipment,
and home appliances.
Intelligent
Maintenance
System (IMS)
A method that uses the collected data from machinery to predict and prevent
potential failures in that machinery.
Intelligent
Transportation
System (ITS)
The application of advanced information and communications technology
to surface transportation for enhanced safety and mobility while reducing
environmental impacts. EU Directive 2010/40/EU defines ITS in the context of
road transport.
Inter-Integrated
Circuit (I2C)
I2C, pronounced I-squared-C, is a serial bus that provides communication
between sensors and microcontrollers such as the Arduino. In contrast to
the full-duplex SPI specification, I2C has a slower data rate, and data can
only travel in one direction at a time. Arduino uses 7-bit values to reference
I2C addresses, and devices using I2C must use a common ground to
communicate.
International
Mobile Equipment
Identifier (IMEI)
The unique number used in GSM to identify mobile devices on their individual
operator networks.
International
Mobile Subscriber
Identifier (IMSI)
The unique number used in GSM and CDMA to identify SIM cards on their
individual operator networks.
International
Telecommunications
Union (ITU)
A specialized agency of the United Nations responsible for issues concerning
information and communication technologies.
Internet Group
Management
Protocol (IGMP)
This communication protocol is based on the IP protocol and is used to
support group communication. IGMP allows for IP-multicasting that enables the
transmission of IP packages to many receivers with one transmission, and this
is a requirement for technologies such as Internet television.
Internet of
Everything (IoE)
A term being promoted by Cisco as a variation or extension of IoT. IoE subtly
distinguishes itself by emphasizing the connection of people to things.

Internet of Things
(IoT)
Internet-connected physical devices, in many cases everyday objects
(things), that can communicate their status, respond to events, or even act
autonomously. This enables communication among those things, closing the
gap between the real and the virtual world and creating smarter processes and
structures that can support us without needing our attention. IoT has evolved
from the convergence of wireless technologies, micro-electromechanical
systems (MEMS), and the Internet.
Internet Protocol
Security (IPSEC)
A set of protocols that provide authentication and encryption to Internet
Protocol (IP) packets, adding an extra layer of security on IP communications.
Interoperability
The ability of two or more systems or components to work together and
exchange and use information effectively.
In-Vehicle
Infotainment (IVI)
Systems integrated into automobiles that deliver both entertainment and
information content. Typical IVI applications include managing audio, listening
to or sending SMS, making voice calls, navigating, and using rear-seat
entertainment such as games, movies, games, and social networking. IVI
also includes interfacing with smartphone-enabled content such as traffic
conditions, sports scores, and weather forecasts.
IoE Internet of Everything.
IoT Internet of Things.
IoT Botnet
Internet of Things botnet. A group of hacked computers, smart appliances, and
Internet-connected devices that have been co-opted for illicit purposes.
IoT Healthcare
IoT healthcare is also called “connected health,” this encompasses all
advancements in the medical industry that relate to M2M communication and
remote sensing.
IoT Privacy
Internet of Things privacy. The special considerations required to protect the
information of individuals from exposure in the IoT environment, where almost
any physical or logical entity or object can be given a unique identifier and the
ability to communicate autonomously over the Internet or similar network.
IoT Security
Internet of Things security. The area concerned with safeguarding connected
devices and networks in the Internet of things.
IP Devices All devices within a network which are labeled with an IP address.

IPSEC Internet Protocol Security.
IPv6
IP addresses serve to identify devices on the Internet. IPv6 is the newest
Internet protocol, which provides more addresses than the IPv4 protocol.
IPv6 Address
A 128-bit alphanumeric string that identifies an endpoint device in the Internet
Protocol Version 6 (IPv6) addressing scheme.
IRIDIUM
A satellite communication constellation that provides global voice and data
coverage through its satellite network, operating on the 1618.85 to 1626.5 MHz
frequencies.
IS-136 Interim Standard 136 (standard for TDMA cellular).
IS-95 Interim Standard 95 (standard for CDMA cellular).
ISM Bands Industrial, Scientific, and Medical Bands.
ITS Intelligent Transportation System.
ITU International Telecommunications Union.
IVI In-Vehicle Infotainment.
JavaScript Object
Notation (JSON)
Used as a lightweight alternative to XML for organizing data, JSON is text-
based and human-readable. The format uses “name : object” pairs to organize
the data.
JSON JavaScript Object Notation.
Kevin Ashton
The man who first coined the phrase “Internet of Things” in 1999. Mr. Ashton
cofounded MIT’s Auto-ID Center which created a global standard system for
RFID.
L2TP Layer 2 Tunneling Protocol.
LAN Local Area Network.

Layer 2 Tunneling
Protocol (L2TP)
This is a tunneling protocol used to support virtual private networks (VPNs) or
as part of the delivery of services by ISPs. It does not provide any encryption or
confidentiality by itself, relying on an encryption protocol that it passes within
the tunnel to provide privacy.
LED Light-Emitting Diode.
Light-Emitting
Diode (LED)
A semiconductor that generates light via electroluminescence. Infrared LEDs
can be used for the remote control units of many consumer electronics.
Link Budget
An accounting of all of the losses in a wireless communication system. In order
to “close the link,” enough RF energy has to make it from the transmitter to the
receiver. (Losses include antennas, structural attenuation, propagation loss,
etc.)
LLN Low power and Lossy Networks.
Local Area
Network (LAN)
A network of devices in relatively close proximity, prior to the point of
transmission over leased telecommunication lines. The two most common
communications technologies used in LANs are Ethernet and Wi-Fi.
Long Term
Evolution (LTE)
/ 4G
LTE, often referred to as 4G, is the latest cellular network type, offering superior
data transfer speeds than its predecessor, 3G, and it’s part of the GSM
upgrade path. Portable devices can now access data at high-speed broadband
speeds through LTE. Depending on where in the world you are, LTE may be
implemented using different frequency bands. For example, European LTE uses
700/800/900/1800/2600 MHz bands, where North America uses 700/750/800/8
50/1900/1700/2100(AWS)/2500/2600 MHz.
Low power and
Lossy Networks
(LLN)
These networks are comprised of embedded devices with limited power,
memory, and processing resources. LLNs are typically optimized for energy
efficiency, may use IEEE 802.15.4, and can be applied to industrial monitoring,
building automation, connected homes, healthcare, environmental monitoring,
urban sensor networks, asset tracking, and more.
Low Power Wide
Area (LPWA)
These networks are built specifically for M2M communications and offer
long-range, low-power consumption. They solve cost and battery-life issues
that cellular technology cannot, and LPWA networks solve range issues that
technologies like Bluetooth or BLE struggle with.

Low Power
Wireless Sensor
Network
A group of spatially distributed, independent devices that collect data
by measuring physical or environmental conditions with minimal power
consumption.
LPWA Low Power Wide Area.
LTE Long Term Evolution.
M2M Machine-to-Machine.
M2P Machine-to-Person.
MAC Media Access Control.
Machine
Authentication
The authorization of an automated human-to-machine or machine-to-machine
(M2M) communication through verification of a digital certificate or digital
credentials. Unlike user authentication, the process does not involve any action
on the part of a human.
Machine Data
Also known as machine-generated data, this is digital information created
by the activity of computers, mobile phones, embedded systems, and other
networked devices.
Machine-to-
Machine (M2M)
A broad term describing technology that allows for one connected device
to communicate and exchange information with another connected device,
without the assistance of a human.
Machine-to-
Person (M2P)
Describes the analytics for big data in a human readable form (e.g.,
dashboards).
MapReduce
A parallel processing model for handling extremely large data sets. First, a
Map process runs to reduce a data set to key value pairs (in tuples), and then a
second Reduce process combines those pairs into a smaller set of tuples. First
introduced by Google, MapReduce is a concept central to Hadoop.
MCU MicroController Unit.
MDN Mobile Directory Number.

Mechatronics
A combination of mechanical and electronics, mechatronics brings together
electrical engineering, control engineering, computer engineering, and
mechanical engineering disciplines. A warehouse inventory robot would be a
mechatronic device, whereas an IoT-enabled sensor device, such as a weather
station, could be better classified as a Cyber-Physical System (CPS).
Media Access
Control (MAC)
The “layer 2” in a network that allows the physical medium (radio waves or wire
signals) to be organized to pass data back and forth. For low-rate data wireless
applications, the MAC has many implications on performance.
MEID Mobile Equipment Identifier.
MEMS Micro-Electro-Mechanical Systems.
Mesh Networking
or Mesh Network
Topology
An ad-hoc, local area network infrastructure where the nodes communicate
directly with each other without the need to pass through a central structure
such as an ISP. The only way to shut down a mesh network is to eliminate
every node. One of the most dramatic demonstrations of mesh technology
was during the Hong Kong protests in October 2014 where the direct
communication between protestors’ devices confounded the government’s
ability to block communication. The adaptivity of mesh networks makes them
ideal for IoT applications.
Message Broker
A middleware program that translates a message from the messaging protocol
of the sender into the messaging protocol of the receiver. This way a message
broker makes it easier for two applications to communicate.
Message Queue
Telemetry
Transport (MQTT)
An open, lightweight M2M communications protocol for the transfer of
telemetry messages.
Message-Oriented
Middleware (MOM)
Middleware that allows for synchronous as well as asynchronous (queue)
messaging between distributed systems.
mHealth
Mobile Health. This is the practice of medicine using mobile devices,
particularly physiological sensors. Sensors may be enabled to communicate
with a user’s mobile phone in a Body Area Network configuration. Related to
e-Health.
MicroController
Unit (MCU)
A full computer on a single chip. The chip contains a CPU, a clock, non-volatile
memory for the program (ROM or flash), volatile memory for input and output
(RAM), and an I/O control unit.

Micro-Electro-
Mechanical
Systems (MEMS)
Miniaturized mechanical and electro-mechanical elements, typically used for
measurements, such as accelerometers and gyroscopes. Systems-on-a-chip
(SoC) technology is used to embed mechanical devices such as fluid sensors,
mirrors, actuators, pressure and temperature sensors, and vibration sensors on
to semiconductor chips.
MIMO Multiple Input, Multiple Output (in the context of antennas).
MMS Multimedia Messaging Service.
MNO Mobile Network Operator.
Mobile Directory
Number (MDN)
The number a user would dial to reach a specific mobile phone. Used in CDMA
— conceptually similar to the MSISDN in GSM.
Mobile Equipment
Identifier (MEID)
Unique identification numeral for mobile devices used in CDMA.
Mobile Network
Operator (MNO)
Companies that operate traditional mobile communications networks.
Mobile Station
(MS)
A cellular radio handset or cellular M2M device.
Mobile Station
International
Subscriber
Directory Number
(MSISDN)
The telephone number to the SIM card in a mobile phone. Used in GSM —
conceptually similar to the MDN in CDMA.
Mobile Switching
Center (MSC)
The center of a network switching subsystem, associated with communications
switching functions, routing SMS messages, and interfacing with other
networks.
Mobile Virtual
Network Operator
(MVNO)
A wireless communications provider that leases the infrastructure over which it
proves services.
Modbus
A communication protocol mainly used to connect electronic devices. The
Modbus Master (for example, a computer) requests information from the
Modbus Slaves (for example, electronic thermometers). Up to 247 Slaves can
transmit their information to one Master.

MOM Message-Oriented Middleware.
Mote
Short for Remote. A mote is a wireless transceiver that also acts as a remote
sensor.
MQTT Message Queue Telemetry Transport.
MS Mobile Station.
MSC Mobile Switching Center.
MSISDN Mobile Station ISDN.
Multimedia
Messaging Service
(MMS)
A feature of mobile devices that allows transmission of images, video, or audio
in addition to short text messages via standardized communications protocols.
See also Short Messaging Service (SMS).
Multiple DoF
Sensing
A MEMS concept referring to the detection of the combined input along
multiple axes using multiple sensing types, such as acceleration and rotation.
Typical applications include antenna stabilization, robotics and dead-reckoning.
Multiple-Input and
Multiple-Output
(MIMO)
A radio technology using multiple antennas at both the transmitter and receiver
to improve communication performance. MIMO is an important part of wireless
communication standards such as IEEE 802.11n (Wi-Fi).
Nagios
Software that monitors IT infrastructures. It includes, for example, immediate
problem detection.
Near Field
Communication
(NFC)
Short-range wireless communication between devices, used in applications
such as contactless mobile payments, transport ticketing, and phone-as-key.
Using NFC, consumers can pay for retail items simply by bringing their mobile
phones into the range of the sensor and confirming the transaction. NFC has
been overshadowed in IoT applications by other protocols such as BLE.
Nearables
Coined for the similarity to “wearables,” this describes items with nearby
tracking devices, or beacons, attached to them. Nearables can communicate
with smart devices, such as smartphones, to let the user interact with objects in
their vicinity.
NFC Near Field Communication.

OBE On-Board Equipment.
On-Board
Equipment (OBE)
Components of a Vehicle-to-Infrastructure (V2I) implementation located in a
moving vehicle, communicating wirelessly with roadside equipment (RSE). OBE
applications may interface with other vehicle systems via the CAN Bus.
Open Source
A type of software where the source code is available and can be modified and
freely redistributed. Open source is the opposite of closed, proprietary systems.
Many developers insist that IoT must have open standards to reach its full
potential.
Open VPN
An open-source software application that implements virtual private network
(VPN) techniques for creating secure point-to-point or site-to-site connections
in routed or bridged configurations and remote access facilities. This is a
security method which can be implemented on devices such as cellular routers.
Operational
Technology (OT)
As opposed to Information Technology (IT), this refers to technologies
associated with control and automation. If IT helps run business processes, OT
helps execute the physical interactions that control value creation.
Operator A company that provides telecommunication services. See also Carrier.
PaaS Platform as a Service.
PAN Personal Area Network.
Part 90 Bands
Small parts of the RF spectrum that are made available in small areas to
businesses for data or voice communications. Many smart grid providers use
part 90 licenses for their wireless data.
Passive Sensor
A device that detects and responds to some type of input from the physical
environment.
PCB Printed Circuit Board.
PDR Pedestrian Dead Reckoning.
PDU Power Distribution Unit.

Pedestrian Dead
Reckoning (PDR)
A method of indoor positioning that uses a last known waypoint, distance, and
direction of travel to calculate the current location of a moving person. PDR
may be used to supplement other positioning methods such as GPS. Dead
reckoning is subject to cumulative errors.
Pen Testing or
Pentest
Penetration Testing.
Penetration
Testing
A method of evaluating the security of a network or system from internal
or external threats. Also called pentests, this is part of a full security audit
and typically exploits a combination of weaknesses to gain access and then
evaluates the capability of the network’s defenders to detect and respond to
the penetration.
PERS Personal Emergency Response System.
Personal Area
Network (PAN)
Interconnected devices operating in the range of a single person, typically 10
meters. PANS are mostly or exclusively wireless, making the term basically
indistinguishable from Wireless PANs (WPAN). WPAN is based on the IEEE
802.15 standard and does not necessarily require an uplink to the Internet. The
PAN concept was first developed by Thomas Zimmerman and others at the
M.I.T. Media Lab.
Personal
Emergency
Response System
(PERS)
A mobile duress panic alarm component of a monitoring system, typically for
the residential market. Modern PERS devices go beyond their origins as a mere
push button to include MEMS and various other sensors.
Personal
Protection Drone
(PPD)
A type of drone, or drone swarm, dedicated to an individual’s security. PPDs are
non-lethal and may be primarily used to record an encounter or raise an alarm.
A use case for a swarm of PPDs may be to hinder the approach of an assailant
long enough to facilitate the protected person’s withdrawal.
Pervasive
Computing
Another term for ubiquitous computing.
Photoplethys-
mogram (PPG)
A optically obtained plethysmogram using an LED that measures the output
volume of an organ, such as the heart. A photodiode measures the amount
of light reflected from the LED, which in a heart monitoring application can be
translated into a waveform. Respiration can induce variations in the amplitude
of the PPG waveform.

Physical Web, The
Google’s open standard to allow IoT devices to communicate via web
addresses. By using HTTP, users can walk up and access any smart device
(such as parking meters and vending machines) without the overhead of
dedicated mobile apps.
Platform as a
Service (PaaS)
A platform that provides web developers with the infrastructure they need to
develop and run an application.
PoE Power over Ethernet.
Point-to-Point
Tunneling
Protocol (PPTP)
This is a method for implementing virtual private networks (VPNs).
Power Distribution
Unit (PDU)
A physical device with multiple outlets that connects electrical power to
recipient devices. PDUs can be simple, such as a mounted power strip, or
more complex by having power filtering, UPS, load balancing, or intelligent
monitoring incorporated in the device.
Power over
Ethernet (PoE)
The capability to deliver enough power to operate a device over an Ethernet
connection. PoE is useful in certain low-voltage applications, such as passive
IP cameras.
PPD Personal Protection Drone.
PPG Photoplethysmogram.
PPTP Point-to-Point Tunneling Protocol.
Preboot Execution
Environment
(PXE)
The ability to manage power over a network connection. A PXE-enabled device
can be shut down or restarted via a network connection, allowing for power-
hungry devices to be managed remotely.
Preferred
Roaming List
(PRL)
A database (especially in a CDMA-based wireless device) that tells it how to
find and connect to locally available wireless network(s). The function of the
PRL is most important when a device is outside its home network and must
seek out an alternate network.
Printed Circuit
Board (PCB)
A plastic board made for connecting electronic components together and used
in most computers and electronics.

Private Cloud
Information technology services supplied via the cloud but only within a single
organization, for example, one company.
PRL Preferred Roaming List.
Public Cloud
Information technology services supplied via the cloud that are public and
made available for everyone.
Pulse Oximeter
A sensor that measures oxygen saturation in the blood. Saturation of peripheral
oxygen, or SpO2, is a measure of hemoglobin saturation and can be measured
non-invasively, for example, with a clip on the finger or ear. The sensor typically
employs a pair of small LEDs facing a photodiode that measures the amount of
light passing through the skin.
PXE Preboot Execution Environment.
Python
A widely used open-source programming language that can be implemented
in variety of ways, including in embedded applications. There is a large library
base which can be used by Python applications, helping minimize code and
speed up development time.
Python Script
Interpreter
A tool that lets you run Python code, something which is now being seen
embedded directly into devices such as cellular modules.
QoS Quality of Service.
Quality of Service
(QoS)
Different services that regulate data transfer priorities to identify and control
the quality with which a service can be accessed by users. This is especially
important if a certain quality (for example, bandwidth) has to be guaranteed to
ensure the functionality of a service.
Quantified Self
A movement that started in 2007 that uses modern technical advances to gain
more insight into one’s own life by collecting data relating to, among other
things, health and emotions. This data is then used to improve a person’s
lifestyle and state of mind.
Quantum Sensor
A sensor that takes advantage of quantum correlations to produce
measurements beyond what are possible with traditional sensors. Taking
advantage of unique behavior of systems at the atomic scale, quantum sensors
use wonder materials such as graphene and quantum dots.

Radio
Fingerprinting
An electronic process that identifies each individual wireless handset by
examining its unique radio transmission characteristics. Fingerprinting is used
to reduce fraud since an illegal device can not duplicate a legal device's radio-
frequency fingerprint.
Radio Frequency
(RF)
Radio waves. This term generally means “wireless communication” when
referred to in IoT discussions.
Radio Frequency
Identification
(RFID)
Generally speaking, this is the use of strong radio waves to “excite” enough
current in a small tag to send a radio transmission back. It works over short
range and only for small amounts of data.
RADIUS Remote Authentication Dial-In User Service.
Remote
Authentication
Dial-In User
Service (RADIUS)
A type of server responsible for receiving user connection requests,
authenticating the user, and returning all configuration information necessary for
the client to deliver service.
Remote
Monitoring and
Control
The increasingly automated monitoring and control of devices, technologies, or
processes. Wireless devices which send information gathered directly to control
centers are often used to achieve this.
Remote Sensing
The use of various technologies to make observations and measurements at a
target that is usually at a distance or on a scale beyond those observable to the
naked eye.
Representational
State Transfer
(REST)
An architecture for web standards, especially for the HTTP protocol. It is
supposed to simplify the design of network applications compared to, for
example, SOAP.
REST Representational State Transfer.
RESTful Web
Services
Web services that are realized within the REST architecture are called RESTful
Web Services. Also see REST.
RF Radio Frequency.
RF Geolocation
A general term that applies to “finding” a radio transceiver with another. GPS
is a good example. A good rule to remember is that to do RF geolocation well,
you need a large RF bandwidth.

RF Sensitivity
The minimum magnitude of input signal you need based on a specified signal-
to-noise ratio to achieve at minimum error rate.
RFID Radio Frequency Identification.
RFID Tagging
A system using small radio frequency identification devices for tracking
purposes. An RFID tagging system includes the tag itself, a read/write device,
and a host application for data collection, processing, and transmission.
Roaming
Using a wireless device in an area outside its home coverage area. There is
often an additional charge for roaming.
SaaS Software as a Service.
SBC Single Board Computer.
SCADA Supervisory Control and Data Acquisition.
SDN Software-Defined Networking.
SDO Standards Development Organization.
Sensor
A device used to measure a specific characteristic of the surrounding
environment, such as temperature. The use of sensors and actuators to
connect things to the physical world is a key component of IoT. A properly
implemented sensor ideally should be sensitive only to the characteristic
being measured and should not interfere with what’s being measured nor be
influenced by other characteristics.
Sensor Analytics Statistical analysis of data that is created by wired or wireless sensors.
Sensor Fusion
The process of combining and processing the raw data coming out of multiple
sensors to generate usable information. For example, because of the quantity
of sensors, a NASA un-crewed vehicle on Mars requires sensor fusion to detect
if there has been a failure.
Sensor Hub
A technology that connects sensor data and processes them. This way the hub
does part of a processors data-processing job.

Serial Peripheral
Interface (SPI)
A specification developed by Motorola for use in short distance communication
between sensors and microcontrollers such as Arduino. In contrast to the I2C
specification, the full-duplex SPI runs at a higher data rate and is appropriate
for applications such as Ethernet and memory cards.
Serial Port Profile
(SPP)
A hardware profile used with Bluetooth applications that includes custom AT
commands and functionality dedicated to wireless data connections and serial
cable replacement.
SGSN Serving GPRS Support Node (see also GGSN).
Shock Sensing
A MEMS concept referring to the detection of sudden impacts at a
predetermined level. Typical applications include shut-off sensing, condition
monitoring, and tap detection for data entry.
Short Message
Service Center
(SMSC)
Also called Short Message Center (SMC), this is the network element in a
mobile telephone network that stores, forwards, converts, and delivers SMS
messages.
Short Messaging
System (SMS)
A feature of mobile devices that allows transmission of short text messages via
standardized communications protocols.
SIGFOX
A low-bandwidth, wireless protocol that offers excellent range and obstacle
penetration for short messages, giving a new low-powered and cost-effective
wireless transmission medium for IoT and M2M technologies.
Signal Phase and
Timing (SPaT)
Refers to communications associated with the operations of signalized
intersections. The major components associated with a SPaT application are
roadside equipment (RSE) and onboard equipment (OBE). A SPaT-formatted
message can be used to convey the current status of a signal at an intersection.
SIM Subscriber Identity Module.
Simple (or
Streaming) Text
Oriented Message
Protocol (STOMP)
A protocol designed for working with message-oriented middleware, similar to
HTTP, and it allows clients to communicate with most of the message brokers,
making it language-agnostic.
Simple Object
Access Protocol
(SOAP)
A protocol specification for exchanging structured information in the
implementation of web services in computer networks.

Single Board
Computer (SBC)
A complete, functioning computer with all functions (I/O, processor, memory)
located on one board. Popularized by the Raspberry Pi system, SBCs are
constructed in direct contrast to traditional motherboards with plug-in cards for
functions like graphics and Ethernet.
SMA SubMiniature version A.
Smart Buildings
Buildings that try to minimize costs and environmental impact. This is achieved
by connected systems and efficient use of energy through new, automated
technology that intelligently responds to certain circumstances (available solar
energy, temperature inside the building, etc.).
Smart Car
An automobile that uses technology to support the driver and create a safer
traffic environment. Different systems (inside and outside of the car) are
connected and communicate with each other to allow intelligent intervention in
dangerous situations and more fluid traffic.
Smart Cities
A concept that tries to create a more intelligent city infrastructure by using
modern information and communication technologies. Smart cities propose
a more flexible adaptation to certain circumstances, more efficient use of
resources, higher quality of life, more fluid transportation, and more. This may
be achieved through networking and integrated information exchange between
humans and things.
Smart Grid
A general term referring to the application of networking capabilities and
computer systems to the electric grid. A smart grid would include smart meters
at the point of delivery, allowing for real time monitoring of usage and the
adjustment of power settings on some appliances.
Smart Home
The networking of household devices and systems through information
and communication technology. This way, processes within a home can
be monitored and controlled automatically to optimize quality of life, costs,
security, and environmental impact. Related to Connected Home.
Smart Label
A type of identification tag that contains more advanced technologies than
conventional barcode data. Some common types of smart labels are QR codes,
Electronic Article Surveillance (EAS) tags, and RFID tags.
Smart Meter
An electronic device that measures and displays resource consumption (of
water, gas, electricity, etc.) and communicates this information to the resource
distributors and managers (such as utilities and municipalities) and even to
consumers. This allows for a more efficient distribution, usage, pricing, and
control of these resources.

Smartwatch
A wristwatch, generally with a display, that interacts with the wearer and can
communicate with a network wirelessly (the device may have a USB connection
for charging and other functions). Many smartwatches have MEMS and
physiological sensors, such as ECG and skin temperature thermometers.
SMS Short Message Service.
SMSC Short Message Service Center.
SOAP Simple Object Access Protocol.
SoC System on a Chip.
Social Web of
Things
The socialization of the Internet of Things. This is the integration of connected
things into our social life. An example would be a TV that not only informs you
that your favorite TV show is on in an hour, but also lets you know which of your
friends like the show too so you can meet up and watch together.
Software as a
Service (Saas)
A subscription-based model where a monthly fee is charged for using
software, rather than an upfront purchase. SaaS (also spelled SAAS) and
cloud computing can give cash-strapped enterprises and startups access to
applications such as email and lead management that might otherwise be too
expensive to purchase outright.
Software-Defined
Network (SDN)
An approach to networking that decouples control of information flow from the
hardware and gives it to a software controller. This allows for less data to travel
wirelessly, making it a potential strategy for IoT networks.
Spaced Repetition
A quantified self-concept designed to increase the brain’s retention of
knowledge. Available via apps and cloud-based technologies, spaced repetition
operates on the theory that there is an optimum time between memorization
drills to maximize retention.
SPaT Signal Phase and Timing.
SPI Serial Peripheral Interface.
SS7 Signaling System 7.

Standards
Development
Organization
(SDO)
An organization whose primary activities are developing, coordinating, revising,
amending, interpreting, or otherwise producing technical standards.
Steel Collar
Things in the workplace that replace or augment human labor. A “steel-collar
workforce” is capable of tirelessly and efficiently performing repetitive tasks
or monitoring. Playing off of the terms “blue collar” and “white collar,” the
phrase was first coined in the early 1980s referring to a robotic threat to US
manufacturing jobs.
STOMP Simple (or Streaming) Text Oriented Message Protocol.
Structure
Attenuation
The loss in intensity of radio waves through a medium (like radio waves through
a brick wall).
SubMiniature
version A. (SMA)
A type of connector commonly used with antenna, giving you male and female
coaxial cable connectors that connect with a screw head.
Subscriber
Identity Module
(SIM)
A piece of hardware (the “smart card”) containing account information for a
user on a GSM network. The SIM is inserted into a SIM holder in GSM cellular
devices.
Subscriber
Identity Module
(SIM)
Provided by the Mobile Network Operator, a SIM contains the International
Mobile Subscriber Identity (IMSI) and the security parameters to authenticate
access to the network.
Supervisory
Control and
Data Acquisition
(SCADA)
An industrial control system typically used for geographically dispersed assets,
often scattered over large distances. SCADA is often applied to electrical
utilities to monitor substations, transformers, and other electrical assets.
SX1272
First-generation long-range wireless transceiver from Semtech, which
introduced a new type of PHY layer modulation. This technology dramatically
increases the range of sub-GHz RF communications.
SX1276
Follow on to SX1272 from Semtech, and this part includes frequency coverage
for more unlicensed bands worldwide and several modes that increase receive
sensitivity.

System on a Chip
(SoC)
A single integrated-circuit technology that contains all the necessary circuits
and parts for a complete system. A single microchip in a wearable device, for
example, could contain an analog-to-digital converter, memory, logic control,
I/O, etc.
TaaS Things as a Service.
TCP/IP Transmission Control Protocol/Internet Protocol.
TDMA Time Division Multiple Access.
Telematics
An IT concept regarding the long-distance transmission of data. In vehicles on
the move, telematics refers to the integrated use of telecommunications and
informatics, such as dashboard screens that show the vehicles current position
on a map or in centralized tracking applications.
Terrestrial
Trunked Radio
(TETRA)
This operates as a two-way transceiver and is popularly used by the emergency
services as well as on transport such as rail and on marine vessels. It operates
on low frequencies split over 4 channels (ranging between 380 and 400 MHz
for emergency services and higher for civilian use). The use of low frequencies
allows for far greater transmission distances but lower data transfer rates.
TETRA Terrestrial Trunked Radio.
Thing, in the
Internet of Things
An entity or physical object that has a unique identifier, an embedded system,
and the ability to transfer data over a network.
Thingbot
Something with an embedded system and an Internet connection that has been
co-opted by a hacker to become part of a botnet of networked things.
ThingManager A platform for managing real-world Things and their digital representations.
Things as a
Service (TaaS)
The concept of delivering IoT functionality without the end user having to
operate or maintain extensive hardware. For example, services such as Hadoop
can be delivered in the cloud to receive and process the data generated by IoT-
enabled sensor networks.
THNGMNGR ThingManager.

Thread
A simplified IPv6-based mesh networking protocol geared to the smart home
vertical. Developed on low-cost 802.15.4 chipsets, Thread is designed for
extremely low power consumption.
Tilt Sensing
A MEMS concept referring to the measurement of the inclination or angle of
change with respect to gravity. Typical applications include industrial equipment
platform stabilization and landscape/portrait detection on handheld devices.
Time Division
Multiple Access
(TDMA)
A channel access method for shared medium networks.
Transceiver
Short for transmitter-receiver. A transceiver both transmits and receives analog
or digital signals. A transceiver is normally built into a network interface card.
Transmission
Control Protocol/
Internet Protocol
(TCP/IP)
The core standard protocol for Internet-based communications. Some wireless
systems “break” TCP/IP in order to lower the overhead of the on-air signals.
Transparent
Computing
A characteristic of ubiquitous computing where smart devices respond to
users’ needs in the background. The devices are invisible (“transparent”) in the
sense that they operate without the conscious thought or interaction of the user
who is benefiting from the object or Thing.
Transponder
A wireless communications device that picks up and automatically responds
to an incoming signal. The term is a combination of the words transmitter and
responder. Transponders can be either passive or active.
TV Whitespace
A new FCC program that makes unused TV station bands available for
temporary and controlled use in a small geographic area. This is used mostly by
rural Internet service providers and wireless microphone providers.
UART Universal Asynchronous Receiver/Transmitter.
UBI Usage-Based Insurance.
Ubiquitous
Computing
The concept of embedding microprocessors in everyday things so they can
communicate information continuously. Ubiquitous devices are expected to
be constantly connected. Utility smart meters are an example of ubiquitous
computing, replacing manual meter-readers with devices that can report usage
and modify power settings on ubiquitous appliances.

UL or U/L Uplink.
Ultra-Wide Band
A “spark gap” transmitter that emits a very weak, very wide (in frequency)
pulse of RF energy. This signal is used mostly for localizing signals. Wide signal
bandwidths are good for measuring distance.
UMTS Universal Mobile Telecommunications System.
Uniform Resource
Identifier (URI)
The unique identifier that makes content addressable on the Internet by
uniquely targeting items, such as text, video, images, and applications.
Uniform Resource
Locator (URL)
A particular type of URI that targets web pages so that when a browser
requests them, they can be found and served to users.
Universal
Asynchronous
Receiver/
Transmitter
(UART)
A microchip controlling a computer’s interface to serial devices, converting the
bytes it receives from the computer along parallel circuits into a single serial bit
stream. A 16550 UART has a 16-byte buffer.
Universal
Authentication
A network identity-verification method that allows users to move from site to
site securely without having to enter identifying information multiple times.
Universal Mobile
Telecommunications
System (UMTS)
Also referred to as 3G cellular technology, this is the third iteration of the GSM.
It achieves improved data transfer speeds over 2G by adding additional higher
frequency bands (2100MHz).
Uplink (UL or U/L)
This is the process of sending data from your device/computer to a server or
target address. In a cellular network, this would be seen as data being sent
from a mobile handset to a cellular base station.
URI Uniform Resource Identifier.
URL Uniform Resource Locator.
Usage-Based
Insurance (UBI)
Also called Pay as You Drive (PAYD), UBI bases the insurance rate on pre-
defined variables including distance, behavior, time, and place. The data
gathering and telematics can be provided by a “black box” in the vehicle, a
dongle-type device, or even a smartphone.

V2I Vehicle-to-Infrastructure.
V2V Vehicle-to-Vehicle.
V2X
A shorthand to combine Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I),
and Vehicle-to-Anything.
Vehicle-to-
Infrastructure
(V2I)
The communication of smart cars and commercial vehicles with surrounding
sensors, such as signal phase and timing (SPaT) information.
Vehicle-to-Vehicle
(V2V)
Using a region of the 5.9 GHz band, V2V systems allow vehicles to
communicate with each other and with roadside stations. Networks of vehicles
can help avoid congestion, find better routes, and aid law enforcement.
Vehicle-to-Vehicle
Communication
(V2V
Communication)
The wireless transmission of data between motor vehicles.
Vibration Sensing
A MEMS concept referring to the detection of periodic acceleration and
deceleration. Typical applications include structural health monitoring, acoustic
event triggering, and seismic equipment.
Video Motion
Detection (VMD)
A technology that analyzes image data and the differences in a series of
images. VMD makes event-driven video surveillance possible, but the potential
for false positives creates challenges in storage and alarm verification.
Video Surveillance
as a Service
(VSaaS)
A managed data service that transfers the monitoring and storage of video to
the cloud. VCaaS streamlines security operations by centralizing IT and requires
no capital investment in servers but has heavy bandwidth requirements.
Virtual Power
Plant (VPP)
In a virtual power plant, different, decentralized power generating plants are
connected and are monitored and controlled from a single control center. This
way, virtual power plants can integrate smaller energy providers — for example
solar or wind parks — into the energy infrastructure. VPPs are also able to
flexibly react to changes in demand.
Virtual Private
Network (VPN)
A secure system for users to send and receive data across shared or public
networks. This is accomplished through encryption or protocols that act as if
the user’s devices were directly connected to the private network.

Virtual Sensor
These sensors use data to gather information that would not be measurable by
a single device. This way they can attain information that can’t be measured
directly.
Visited Location
Register (VLR)
The database containing information about a subscriber’s roaming within a
mobile switching center’s location area.
VLR Visited Location Register.
VMD Video Motion Detection.
VPN Virtual Private Network.
VPP Virtual Power Plant.
VSaaS Video Surveillance as a Service.
WAN Wide Area Network.
WAP Wireless Application Protocol.
WAVE Wireless Access in Vehicular Environments.
Wearables
or Wearable
Technology
These are technologies or computers integrated into articles of clothing or
accessories that can be worn. Often, the wearble tech is used to quantify
a physical process (such as heartbeat monitoring) or to augment human
capabilities. Wearables may also be used to control external things, for
example, with gestures. Because of the impracticality of wires to transmit
sensor data, wearables are almost universally wireless, using a variety of
communication protocols such as BLE. Examples include smartwatches,
fitness bands, and Google Glasses.
Wide Area
Network (WAN)
A telecommunications network or computer network that extends over a large
geographical distance.
Wi-Fi Wireless Fidelity.
Wireless Access
in Vehicular
Environments
(WAVE)
The IEEE 802.11p standard required to support Intelligent Transportation
Systems (ITS) applications. ITS applications include data exchange between
moving vehicles and between vehicle and ITS-enabled roadside infrastructure.

Wireless
Application
Protocol (WAP)
A protocol for wireless devices allowing the user to view and interact with data
services. Often used to support Internet access and Web browsing on mobile
phones.
Wireless Fidelity
(Wi-Fi)
This is a common form of local area network which operates on the 2.4 GHz
band. Its popularity has led to a wide variety of devices to become Wi-Fi
enabled, including smartphones, cameras, vehicles, and household appliances.
Wi-Fi can be embedded into a device through designing in a Wi-Fi module.
Wristop
A contraction of wristband and desktop, a wristop computer refers to a
wearable that goes on the wrist, such as a smartwatch.
ZigBee
Small range wireless networking protocol that primarily operates on the 2.4 GHz
frequency spectrum. ZigBee devices connect in a mesh topology, forwarding
messages from controlling nodes to slaves, which repeat commands to other
connected nodes. Due to its low power consumption and low data rate, ZigBee
has been used in applications such as traffic management, wireless light
switches, and industrial device monitoring.
Z-Wave
Wireless communication technology used in security systems and also
business and home automation.

Visualizing the IoT
158 VISUALIZING THE IOT
The Definitive Guide | The Internet of Things for Business, 2nd Edition. Visualizing the IoT | 157

IoT Application
An application provides the IoT solution’s intelligence. It acts upon
the device to manifest functionality in real-time. It also orchestrates
data flows and invokes analytics to/from the device.
COMPONENTS OF AN IOT SOLUTION
Collaboration Processes
(incl. integration with people,
business processes systems)
Applications
Applications Management
(data access, visualization,
runtime library)
Data Analytics
(rules, data analysis, business logic)
Data Ingestion, Storage Transformation
Connectivity Device Management
(multiple technology management for
remote administration)
Network
(Cellular, Wi-Fi, Bluetooth, LPWA, etc.)
Machines, Devices Sensors
IoT Application
An application provides the IoT solution’s intelligence. It acts upon
the device to manifest functionality in real-time. It also orchestrates
data flows and invokes analytics to/from the device.
COMPONENTS OF AN IOT SOLUTION
Collaboration Processes
(incl. integration with people,
business processes, systems)
Applications
Applications Management
(data access, visualization,
runtime library)
Data Analytics
(rules, data analysis, business logic)
Data Ingestion, Storage, Transformation
Connectivity Device Management
(multiple technology management for
remote administration)
Network
(Cellular, Wi-Fi, Bluetooth, LPWA, etc.)
Machines, Devices, Sensors

Air, rail, trucks, marine,
automobiles, traffic management,
navigation, vehicle diagnostics
Toll booths, automobiles, planes,
ships, truck fleets, self-driving
vehicles
Remote/home patient monitoring,
medical implants, hospital/
clinic/doctor office, mobile POC,
diagnostic, labs
Telemedicine, chronic disease
monitoring, cardiac implants, sleep
therapy machines, medication
compliance
Materials manufacturing, asset
tracking, factory resource
automation, process automation,
equipment maintenance
Motors, pipelines, machines, bins,
tanks, fabrication, valves, pumps
Home monitoring, security, energy
management, entertainment,
children elderly protection,
appliance monitoring control
Thermostats, appliances, video
surveillance, home electronics,
alarms, game consoles
Power generation, energy manage-
ment, alternative energygeneration
management, oil/gas process
efficiency
Smart meters, windmills, drills,
generators, fuel cells, turbines
Retail inventory management,
retail sales advertising, hospitality
industry, restaurant industry
POS terminals, vending machines,
digital signage, inventory tags,
NFC payment
Building monitoring, waste man-
agement, equipment personnel,
traffic congestion, public service
enhancement
Street lights, environmental
monitors, police, fire, parking,
water treatment plants
Monitoring management for
offices, retail, hospitality, airports,
stadiums
HVAC, lights, security, fire safety
Personal comfort, safety,
health/fitness, entertainment
Personal fitness, smart watches,
augmented reality, virtual reality
INDUSTRIES/VERTICALS USE CASES EXAMPLE DEVICES
Transportation
Healthcare
Industrial
Consumers Smart Homes
Retail
Cities
Smart Enterprise
Fitness Wearables
Energy Utilities
| |

Anatomy of an IoT Device
RF MODULE SENSOR MODULE
POWER MANAGEMENT
CIRCUIT
POWER CIRCUIT
MANAGEMENT
SIGNAL
PROCESSING
ULP
WI–FI
MODULE
CELLULAR ZIGBEE
BLUE-
TOOTH
RADIO
TRANCEIVER
MODULE
TEMP
SENSOR
IMAGE
SENSOR
PRESSURE
SENSOR
GYRO
MAGNET-
OMETER
ACCELER-
OMETER
LIGHT
SENSOR
ENERGY HARVESTER
BATTERY
Bluetooth LE
Cellular (GSM,
CDMA, LTE)
LoRaWAN
Z-Wave
6LoWPAN
Signal Processing
Unit
Wi–Fi
NFC-Near Field
Communication
Neul
Sigfox
Thread
Radio Transceiver
Duplexer
Humidity Sensors
Light Sensors
Magnetometers
Micro Flow Sensors
Position Angle Sen-
sors
Proximity /Presence
Sensors
Speed Sensors
Acoustic Vibration
Sensors
Auto Transportation
Sensors
Displacement Sensors
Distance Sensors
Force Sensors
Gas RFID Sensors
Heat Thermal Sensors
Ultra–Capacitors
Microbatteries
Power Management Unit
Li–Ion Battery
AAA/AA Batteries
Energy Harvesting
Boost Converters
POWER MANAGEMENT MODULEENERGY STORAGE MODULE

Fleet Example
Point of Sale Example
Users /Client
Users / Client
Mobile phone
users
Fleet
Fleet Management
Server
Internet
Internet
Vending Management
Server
Vending machine
Delivery van
Warehouse / Logistics
GPS/GPRS
Network
GPS Satellite

A Guide To The Internet Of Things

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