Survey of Wireless Sensor Network Application

IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 09, 2014 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 294
Survey of Wireless Sensor Network Application
Bali Kumar Patel1
Benupani Gupta2
Ankit Naik
1,2
Student 3
Faculty
1,2,3
Department of Computer Science & Engineering
1,2,3
Kirodimal Institute of Technology, Raigarh,( C.G.)Raigarh, Chhattisgarh, India
Abstract— Sensor networks offer a powerful combination of
distributed sensing, computing and communication. They
lend themselves to countless applications and, at the same
time, offer numerous challenges due to their peculiarities,
primarily the stringent energy constraints to which sensing
nodes are typically subjected. The distinguishing traits of
sensor networks have a direct impact on the hardware design
of the nodes at least four levels: power source, processor,
communication hardware, and sensors. Various hardware
platforms have already been designed to test the many ideas
spawned by the re-search community and to implement
applications to virtually all fields of science and technology.
We are convinced that CAS will be able to provide a
substantial contribution to the development of this exciting
field. A wireless sensor network (WSN) has important
applications such as remote environmental monitoring and
target tracking. This has been enabled by the availability,
particularly in recent years, of sensors that are smaller,
cheaper, and intelligent. These sensors are equipped with
wireless interfaces with which they can communicate with
one another to form a network. The design of a WSN
depends significantly on the application, and it must
consider factors such as the environment, the application’s
design objectives, cost, hardware, and system constraints.
The goal of our survey is to present a comprehensive review
of the recent literature since the publication of [I.F.
Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A
survey on sensor networks, IEEE Communications
Magazine, 2002]. Following a top-down approach, we give
an overview of several new applications and then review the
literature on various aspects of WSNs. We classify the
problems into three different categories: (1) Internal
platform and underlying operating system, (2)
Communication protocol stack, and (3) Network services,
provisioning, and deployment. We review the major
development in these three categories and outline new
challenges.
Keywords: CAS, IEEE, WSN
I. INTRODUCTION
The increasing interest in wireless sensor networks can be
promptly understood simply by thinking about what they
essentially are: a large number of small sensing self-
powered nodes which gather information or detect special
events and communicate in a wireless fashion, with the end
goal of handing their processed data to a base station.
Sensing, processing and communication are three key
elements whose combination in one tiny device gives rise to
a vast number of applications [1], [2]. Sensor networks
provide endless opportunities, but at the same time pose
formidable challenges, such as the fact that energy is a
scarce and usually non-renewable resource. However, recent
advances in low power VLSI, embedded computing,
communication hardware, and in general, the convergence
of computing and communications, are making this
emerging technology a reality [3]. Likewise, advances in
nanotechnology and Micro Electro-Mechanical Systems
(MEMS) are pushing toward networks of tiny distributed
sensors and actuators.
II. OVERVIEW OF KEY ISSUES
Current state-of-the-art sensor technology provides a
solution to design and develop many types of wireless
sensor applications. Available sensors in the market include
generic (multi-purpose) nodes and gateway (bridge) nodes.
A generic (multi-purpose) sensor node’s task is to take
measurements from the monitored environment. It may be
equipped with a variety of devices which can measure
various physical attributes such as light, temperature,
humidity, barometric pressure, velocity, acceleration,
acoustics, magnetic field, etc. Gateway (bridge) nodes
gather data from generic sensors and relay them to the base
station. Gateway nodes have higher processing capability,
battery power, and transmission (radio) range. A
combination of generic and gateway nodes is typically
deployed to form a WSN.
To enable wireless sensor applications using sensor
technologies, the range of tasks can be broadly classified
into three groups as shown in Fig. 1. The first group is the
system. Each sensor node is an individual system. In order
to support different application software on a sensor system,
development of new platforms, operating systems, and
storage schemes are needed. The second group is
communication protocols, which enable communication
between the application and sensors. They also enable
communication between the sensor nodes. The last group is
services which are developed to enhance the application and
to improve system performance and network efficiency.
From application requirements and network
management perspectives, it is important that sensor nodes
are capable of self-organizing themselves. That is, the
sensor nodes can organize themselves into a network and
subsequently are able to control and manage themselves
efficiently. As sensor nodes are limited in power, processing
capacity, and storage, new communication protocols and
management services are needed to fulfill these
requirements.
The communication protocol consists of five
standard protocol layers for packet switching: application
layer, transport layer, network layer, data-link layer, and
physical layer. In this survey, we study how protocols at
different layers address network dynamics and energy
efficiency. Functions such as localization, coverage, storage,
synchronization, security, and data aggregation and
compression are explored as sensor network services.
Implementation of protocols at different layers in
the protocol stack can significantly affect energy

(IJSRD/Vol. 2/Issue 09/2014/067)
consumption, end-to-end delay, and system efficiency. It is
important to optimize communication and minimize energy
usage.
Fig. 1: Broad classification of varios issues in a WSN
Traditional networking protocols do not work well
in a WSN since they are not designed to meet these
requirements. Hence, new energy-efficient protocols have
been proposed for all layers of the protocol stack. These
protocols employ cross-layer optimization by supporting
interactions across the protocol layers. Specifically, protocol
state information at a particular layer is shared across all the
layers to meet the specific requirements of the WSN.
As sensor nodes operate on limited battery power,
energy usage is a very important concern in a WSN; and
there has been significant research focus that revolves
around harvesting and minimizing energy. When a sensor
node is depleted of energy, it will die and disconnect from
the network which can significantly impact the performance
of the application. Sensor network lifetime depends on the
number of active nodes and connectivity of the network, so
energy must be used efficiently in order to maximize the
network lifetime.
Energy harvesting involves nodes replenishing its
energy from an energy source. Potential energy sources
include solar cells [4,5], vibration [6], fuel cells, acoustic
noise, and a mobile supplier [7]. In terms of harvesting
energy from the environment [8], solar cell is the current
mature technique that harvest energy from light. There is
also work in using a mobile energy supplier such as a robot
to replenish energy. The robots would be responsible in
charging themselves with energy and then delivering energy
to the nodes. Energy conservation in a WSN maximizes
network lifetime and is addressed through efficient reliable
wireless communication, intelligent sensor placement to
achieve adequate coverage, security and efficient storage
management, and through data aggregation and data
compression. The above approaches aim to satisfy both the
energy constraint and provide quality of service (QoS)2
for
the application. For reliable communication, services such
as congestion control, active buffer monitoring,
acknowledgements, and packet-loss recovery are necessary
to guarantee reliable packet delivery. Communication
strength is dependent on the placement of sensor nodes.
Sparse sensor placement may result in long-range
transmission and higher energy usage while dense sensor
placement may result in short-range transmission and less
energy consumption. Coverage is interrelated to sensor
placement. The total number of sensors in the network and
their placement determine the degree of network coverage.
Depending on the application, a higher degree of coverage
may be required to increase the accuracy of the sensed data.
In this survey, we review new protocols and algorithms
developed in these areas.
III. APPLICATION
WSN applications can be classified into two categories:
monitoring and tracking (see Fig. 2). Monitoring
applications include indoor/outdoor environmental
monitoring, health and wellness monitoring, power
monitoring, inventory location monitoring, factory and
process automation, and seismic and structural monitoring.
Tracking applications include tracking objects, animals,
humans, and vehicles. While there are many different
applications, below we describe a few example applications
that have been deployed and tested in the real environment.
PinPtr [2] is an experimental counter-sniper system
developed to detect and locate shooters. The system utilizes
a dense deployment of sensors to detect and measure the
time of arrival of muzzle blasts and shock waves from a
shot. Sensors route their measurements to a base station
(e.g., a laptop or PDA) to compute the shooter’s location.
Sensors in the PinPtr system are second-generation
Mica2 motes connected to a multi-purpose acoustic. sensor
board. Each multi-purpose acoustic sensor board is designed
with three acoustic channels and a Xilinx Spartan II FPGA.
Mica2 motes run on a TinyOS [10] operating system
platform that handles task scheduling, radio communication,
time, I/O processing, etc. Middleware services developed on
TinyOS that are exploited in this application include time
synchronization, message routing with data aggregation, and
localization.
Microscope of redwood [11] is a case study of a
WSN that monitors and records the redwood trees in
Sonoma, California. Each sensor node measures air
temperature, relative humidity, and photo-synthetically-
active solar radiation. Sensor nodes are placed at different
heights of the tree. Plant biologists track changes of spatial
gradients in the microclimate around a redwood tree and
validate their biological theories.

Fig. 2: Overview of sensor network
Semiconductor plants and oil tanker application
reported in [12] focus on preventive equipment maintenance
using vibration signatures gathered by sensors to predict
equipment failure. Based on application requirements and
site survey, the architecture of the network is developed to
meet application data needs. Two experiments were carried
out: the first was in a semiconductor fabrication plant and
the second on an onboard oil tanker in the North Sea. The
goal was to reliably validate the requirements for industrial
environments and evaluate the effect of the sensor network
architecture. The study also analyzed the impact of platform
characteristics on the architecture and performance of real
deployment.
Underwater monitoring study in [13] developed a
platform for underwater sensor networks to be used for long
term monitoring of coral reefs and fisheries. The sensor
network consists of static and mobile underwater sensor
nodes. The nodes communicate via point-to-point links
using high speed optical communications. Nodes broadcast
using an acoustic protocol integrated in the TinyOS protocol
stack. They have a variety of sensing devices, including
temperature and pressure sensing devices and cameras.
Mobile nodes can locate and move above the static nodes to
collect data and perform network maintenance functions for
deployment, re-location, and recovery. The challenges of
deploying sensors in an underwater environment were some
key lessons from this study.
MAX [14] is a system for human-centric search of
the physical world. MAX allows people to search and locate
physical objects when they are needed. It provides location
information reference to identifiable landmarks rather than
precise coordinates. MAX was designed with the objectives
of privacy, efficient search of a tagged object, and human-
centric operation. MAX uses a hierarchical architecture that
requires objects to be tagged, sub-stations as landmarks, and
base-station computers to locate the object. Tags on objects
can be marked as private or public which is searchable by
the public or owner only. MAX is designed for low energy
and minimal-delay queries. The implementation of MAX
was demonstrated using Crossbow motes where trials were
conducted in a room of physical objects.
Connection-less sensor-based tracking system
using witness (CenWits) [15] is a search-and-rescue system
designed, implemented, and evaluated using Berkeley
Mica2 sensor motes. The system uses several small radio
frequencies (RF)-based sensors and a small number of
storage and processing devices. CenWits is not a
continuously-connected network. It is designed for
intermittent network connectivity. It is comprised of mobile
sensors worn by subjects (people), access points that collect
information from these sensors and GPS receivers, and
location points to provide location information to the
sensors. A subject will use the GPS receivers and location
points to determine its current location. The key concept is
the use of witnesses to convey a subject’s movement and
location information to the outside world. The goal of
CenWits is to determine an approximate small area where
search-and-rescue efforts can be concentrated.
Cyclops [16] is a small camera device that bridges
the gap between computationally-constrained sensor nodes
and complimentary metal-oxide semiconductor (CMOS)
imagers. This work provides sensor technology with CMOS
imaging. With CMOS imaging, humans can (1) exploit a
different perspective of the physical world which cannot be
seen by human vision, and (2) identify their importance.
Cyclops attempts to interface between a camera module and
a lightweight sensor node. Cyclops contains programmable
logic and memory circuits with high speed data transfer. It
contains a micro-controller to interface with the outside
world. Cyclops is useful in a number of applications that
require high speed processing or high resolution images.
WSN in a petroleum facility [17] can reduce cost
and improve efficiency. The design of this network is
focused on the data rate and latency requirement of the
plant. The network consists of four sensor node and an
actuator node. The sensor nodes are based on T-mote sky
devices [18]. Two AGN1200 pre-802.11N Series MIMO
access points [19] are used to create an 802.11b 2.4 GHz
wireless local area network. In this multi-hop WSN, the T-
mote sky devices send their radio packets to the base station
which is forwarded to a crossbow star gate gateway. The
crossbow star gate gateway translates the radio packets and
sends it along the Ethernet MIMO to a single board TS-3300
computer [20]. The single board TS-3300 computer outputs
the sensor data to the distributed control system. The
distributed control system can also submit changes to the
actuator. In this study, results of network performance, RSSI
and LQI measurement and noise were gathered. Results
show that the effect of latency and environmental noise can
significantly affect the performance of a WSN placed in an
industrial environment.
Volcanic monitoring [21] with WSN can help
accelerate the deployment, installation, and maintenance
process. WSN equipments are smaller, lighter, and consume

less power. The challenges of a WSN application for
volcanic data collection include reliable event detection,
efficient data collection, high data rates, and sparse
deployment of nodes. Given these challenges, a network
consists of 16 sensor nodes was deployed on Volcàn
Reventador in northern Ecuador. Each sensor node is a T-
mote sky device [18] equipped with an external omni
directional antenna, a seismometer, a microphone, and a
custom hardware interface board. Of the 16 sensor nodes, 14
sensor nodes are equipped with a single axis Geospace
Industrial GS-11 Geophone with corner frequency of 4.5 Hz
while the other two sensor nodes carried triaxial Geospace
Industries GS-1 seismometers with corner frequencies of 1
Hz. The custom hardware interface board was designed with
four Texas Instruments AD7710 analog-to-digital converters
to integrate with the T-mote sky devices. Each sensor node
draws power from a pair of alkaline D cell batteries. Sensor
nodes are placed approximately 200–400 m apart from each
other. Nodes relay data via multi-hop routing to a gateway
node. The gateway node connected to a long-distance Free-
Wave radio modem transmits the collected data to the base
station. During network operation, each sensor node samples
two or four channels of seismic acoustic data at 100 Hz. The
data is stored in local flash memory. When an interesting
event occurs, the node will route a message to the base
station. If multiple nodes report the same event, then data is
collected from the nodes in a round-robin fashion. When
data collection is completed, the nodes return to sampling
and storing sensor data locally. In the 19 days of
deployment, the network observed 230 eruptions and other
volcanic events. About 61% of the data was retrieved from
the network due to short outages in the network from
software component failure and power outage. Overall, the
system performed well in this study.
Health monitoring applications [9] using WSN can
improve the existing health care and patient monitoring.
Five prototype designs have been developed for applications
such as infant monitoring, alerting the deaf, blood pressure
monitoring and tracking, and fire-fighter vital sign
monitoring. The prototypes used two types of motes: T-mote
sky devices [18] and SHIMMER (Intel Digital Health
Group’s Sensing Health with Intelligence, Modularity,
Mobility, and Experimental Re-usability).
Because many infant die from sudden infant death
syndrome (SIDS) each year, Sleep Safe is designed for
monitoring an infant while they sleep. It detects the sleeping
position of an infant and alerts the parent when the infant is
lying on its stomach. Sleep Safe consists of two sensor
motes. One SHIMMER mote is attached to an infant’s
clothing while a T-mote is connected to base station
computer. The SHIMMER node has a three-axis
accelerometer for sensing the infant’s position relative to
gravity. The SHIMMER node periodically sends packets to
the base station for processing. Based on the size of the
sensing window and the threshold set by the user, the data is
processed to determine if the infant is on their back.
Baby Glove prototype is designed to monitor vitals.
Baby Glove is a swaddling baby wrap with sensors that can
monitor an infant’s temperature, hydration, and pulse rate. A
SHIMMER mote is connected to the swaddling wrap to
transmit the data to the T-mote connected to the base station.
Like Sleep Safe, an alert is sent to the parent if the analyzed
data exceeds the health settings. Fire Line is a wireless heart
rate sensing system. It is used to monitor a fire fighter’s
heart rate in real-time to detect any abnormality and stress.
Fire Line consist of a Tmote, a custom made heart rate
sensor board, and three re-usable electrodes. All these
components are embedded into a shirt that a fire fighter will
wear underneath all his protective gears. The readings are
taken from the T-mote is then transfer to another T-mote
connected to the base station. If the fire fighter’s heart rate is
increasing too high, an alert is sent.
Heart@Home is a wireless blood pressure monitor
and tracking system. Heart@Home uses a SHIMMER mote
located inside a wrist cuff which is connected to a pressure
sensor. A user’s blood pressure and heart rate is computed
using the oscillometric method. The SHIMMER mote
records the reading and sends it to the T-mote connected to
the user’s computer. A software application processes the
data and provides a graph of the user’s blood pressure and
heart rate over time.
LISTSENse enables the hearing impaired to be
informed of the audible information in their environment. A
user carries the base station T-mote with him. The base
station T-mote consists of a vibrator and LEDs. Transmitter
motes are place near objects (e.g., smoke alarm and
doorbell) that can be heard. Transmitter motes consist of an
omni-directional condenser microphone. They periodically
sample the microphone signal at a rate of 20 Hz. If the
signal is greater than the reference signal, an encrypted
activation message is sent to the user. The base station T-
mote receiving the message actives the vibrator and its LED
lights to warn the user. The user must press the acknowledge
button to deactivate the alert.
ZebraNet [5] system is a mobile wireless sensor
network used to track animal migrations. ZebraNet is
composed of sensor nodes built into the zebra’s collar. The
node consists of a 16-bit TI microcontroller, 4 Mbits off-
chip flash memory, a 900 MHz radio, and a GPS unit.
Positional readings are taking using the GPS and sent multi-
hop across zebras to the base station. The goal is to
accurately log each zebra’s position and use them for
analysis. A total of 6–10 zebra collars were deployed at the
Sweet waters game reserve in central Kenya to study the
effects and reliability of the collar and to collect movement
data. After deployment, the biologists observed that the
collared zebras were affected by the collars. They observed
additional head shakes from those zebra in the first week.
After the first week, the collared zebra show no difference
than the uncolored zebra. A set of movement data was also
collected during this study. From the data, the biologists can
better understand the zebra movements during the day and
night.
IV. CONCLUSION
Sensor networks offer countless challenges, but their
versatility and their broad range of applications are eliciting
more and more interest from the research community as well
as from industry. Sensor networks have the potential of
triggering the next revolution in information technology.
The challenges in terms of circuits and systems are
numerous: the development of low-power communication

hardware, low-power microcontrollers, MEMS based
sensors and actuators, efficient AD conversion, and energy
scavenging devices is necessary to enhance the potential and
the performance of sensor networks. System integration is
another major challenge that sensor networks offer to the
circuits and systems research community. We believe that
CAS can and should have a significant impact in this
emerging, exciting area.
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