capitulos 6

6
Deploying Wireless Sensor Devices in
Intelligent Transportation System Applications
Kirusnapillai Selvarajah, Budiman Arief, Alan Tully and Phil Blythe
Newcastle University
United Kingdom
1. Introduction
A recent study by the UK Government’s Office of Science and Innovation, which examined
how future intelligent infrastructure would evolve to support transportation over the next
50 years looked at a range of new technologies, systems and services that may emerge over
that period (UK DfT, 2006). One key class of technology that was identified as having a
significant role in delivering future intelligence to the transport sector was wireless sensor
networks and in particular the fusion of fixed and mobile networks to help deliver a safe,
sustainable and robust future transportation system based on the better collection of data, its
processing and dissemination and the intelligent use of the data in a fully connected
environment. The important innovations in wireless and digital electronics are beginning to
support many applications in the areas of safety, environmental and emissions control,
driving assistance, diagnostics and maintenance in the transport domain. The last few years
have seen the emergence of many new technologies that can potentially have major impacts
on Intelligent Transportation Systems (ITS) (Tully, 2006).
U.S. DOT recently launched a 5 Years ITS strategic research plan to explore the potentially
transformative capabilities of wireless technology to make surface transportation safer,
smarter and greener and ultimately enhance livability for Americans (US DOT, 2011). This
research program formerly known as IntelliDriveSM and now renamed as “Connected
Vehicle Research” program which focus to develop a networked environment supporting
very high speed transactions among vehicles (V2V) and between vehicles and infrastructure
components (V2I) or hand held devices (V2D) to enable numerous safety and mobility
applications (US DOT, 2010).
The European Telecommunications Standards Institute (ETSI) has been creating and
maintaining standards and specifications for Intelligent Transportation Systems (ITS)
include telematics and all types of communications in vehicles, between vehicles (e.g.
vehicle-to-vehicle), and between vehicles and fixed locations (e.g. vehicle-to-infrastructure).
The ETSI not only looking for road transport domain but also the use of information and
communication technologies (ICT) for rail, water and air transport, including navigation
systems (ETSI, 2009).
As future intelligent infrastructure will bring together and connect individuals, vehicles and
infrastructure through wireless communications, it is critical that robust communication
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technologies are developed. Mobile wireless sensor networks are self-organising mobile
networks where nodes exchange data without the need for an underlying infrastructure. In
the road transport domain, schemes which are fully infrastructure-less and those which use
a combination of fixed (infrastructure) devices and mobile devices fitted to vehicles and
other moving objects are of significant interest to the ITS community as they have the
potential to deliver a ‘connected environment’ where individuals, vehicles and
infrastructure can co-exist and cooperate, thus delivering more knowledge about the
transport environment, the state of the network and who indeed is travelling or wishes to
travel. This may offer benefits in terms of real-time management, optimisation of
transportation systems, intelligent design and the use of such systems for innovative road
charging and possibly carbon trading schemes as well as through the CVHS (Cooperative
Vehicle and Highway Systems) for safety and control applications.
Within the vehicle, the devices may provide wireless connection to various information and
communications technologies components in the vehicle and connect with sensors and other
devices within the engine management system. There is growing consumer demand for
wireless communication technologies in transporation applications from point-to-point to
multiplexed communications. Advances in portable devices (Smartphone, Personal Digital
Assistant and Navigation Systems) may exploit the possibility of interconnection using in-vehicle
communications. Also advances in wireless sensor networking techniques which
offer tiny, low power and MEMS (Micro Electro Mechanical Systems) integrated devices for
sensing and networking will exploit the possibility of vehicle to vehicle and vehicle to
infrastructure communications (Blythe, 2006).
Wireless sensor networks offer an attractive choice for low-cost and easy-to-deploy
solutions for intelligent transportation applications. Intelligent parking lot application using
Wireless Sensor Networks is presented in (Lee et al, 2008). In this paper, authors argue the
use of both ultrasonic and magnetic sensors in accurate and reliable detection of vehicles in
parking lots. A wireless sensor networks scenario for Intelligent Transportation Systems
(ITS) is studied in (Chen et al, 2009) and authors also propose new routing protocol to make
the WSN more energy efficient and with less delivery latency.
Wireless Sensor Network based adaptive vehicle navigation in multi-hop relay WiMAX
networks is proposed in (Chang et al, 2008). This paper proposes the WiMAX multi-hop
relay networks as the inter-vehicle communications to increase the reliability and efficiency of
inter-vehicle communications. Real-time traffic information is gathered here from various
types of sensors equipped on vehicles and exchanged among neighbor vehicles. Collaboration-based
hybrid vehicular sensor network architecture is proposed in (Kong & Tan, 2008). In this
paper, authors propose a collaborative hybrid method to deliver desired data to particular
drivers effectively. The road side sensors and vehicular mobile sensors are used to restore data
and exchange data.
As the wireless sensor networks technology is still relatively new and very little is known
about its real application in the transport domain. Our involvement in the transport-related
projects provides us with an opportunity to carry out research and development of wireless
sensor network applications in transport systems. This chapter outlines our experience in
the ASTRA (ASTRA, 2005), TRACKSS (TRACKSS, 2007) and EMMA (EMMA, 2007) projects
and provides an illustration of the important role that the wireless sensor technology can
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play in future ITS. This chapter also presents encouraging results obtained from the
experiments in investigating the feasibility of utilising wireless sensor networks in vehicle
and vehicle to infrastructure communication in real ITS applications.
2. Communication technologies
The use of networks for communications between the electronic control units of a vehicle in
production cars dated from the beginning of the 1990s. The Controller Area Network (CAN)
was first introduced by BOSCH with the clear intent to serve communication systems for
automotive applications and it is still dominant in automotive networks. The CAN is not
fully satisfying requirements such as predictability, performance and dependability which
are mandatory in automotive communications. To overcome the limitations of the CAN
technology, a number of technologies have been developed for designing automotive
networks such as Time-Triggered Protocol (TTP), Time-Triggered CAN (TTCAN), Byteflight
and Flexray (Navet et al, 2005).
Wireless communication technologies such as ZigBee (ZigBee Alliance, 2006), Bluetooth
(Bluetooth, 2006 ) and Wi-Fi are also expected to be widely employed in the near future in
automotive communication. It is evident that wireless communications can be used in-vehicle,
inter-vehicle and between vehicle and infrastructure in transportation applications
(Cai & Lin, 2008; Kosch et al, 2009; Heddebaut, 2004). Bluetooth is currently the most widely
used automotive wireless technology for in-vehicle communication and Wi-Fi is used for
vehicle to vehicle communication by several pilot research projects, e.g., the Car2Car
consortium (Car2Car consortium, 2007). Ultra Wide Band (UWB) is an emerging wireless
technology that uses a very large bandwidth (Yang and Giannakis, 2004). It is targeted for
multimedia networking whereas 802.11 networks address data networking. Intelligent
collision avoidance and cruise control systems can be developed using UWB technology as
those systems need high ranging accuracy and target differentiation capabilities. UWB
technology can also be integrated into vehicle entertainment systems by downloading high-rate
data from road-side infrastructure UWB transmitters. Communication Air-interface,
Long and Medium range (CALM) (ISO TC204 Working Group, 2007) has many potential
applications in V2V and V2I communication.
ZigBee will be able to fill the gap left by these other technologies, mainly in the
interconnection of wireless sensor devices with vehicles and infrastructure. The ZigBee
standard has evolved since its original release in 2004 and it is a low cost low power
wireless networking standard for sensors and control devices. ZigBee provides network
speeds of up to 250kbps and is expected to be largely used in typical wireless sensor
network applications where high data rates are not required. Table 1 shows a comparison
between five technologies relating to the most important factors which need to be
considered in the ITS application domain.
ZigBee, Bluetooth, Wi-Fi and UWB have been designed for short-range wireless applications
with low power solutions and can be used at the in-vehicle and vehicle to infrastructure
communication. The ZigBee technology can accommodate larger numbers of devices than
Bluetooth. On the other hand, Bluetooth offers high bandwidth with relatively high
throughput. Many wireless sensor network applications do not require high data rate
communication technology as it is based on data exchange. ZigBee provides 250 kbps data
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rate and is expected to be enough for the many wireless sensor network applications.
Notably, ZigBee uses low overhead data transmission and requires low system resources
which are vitally important factors for embedded sensor networks. Also mesh networking
features in ZigBee protocol allow devices to extend its coverage and optimize its radio
resources. The features show that ZigBee is a suitable communication technology for the
wireless sensor networks based ITS applications.
Table 1. Comparison of Wireless Communication Technologies.
3. Smartdust in transportation applications
Smartdust (or mote) is a new concept for wireless sensor networks which offers tiny, low
power and MEMS integrated devices for sensing and networking and it’s also known as
“motes”. It is not only interesting for the low power sensing technologies but also the low
power communication and networking capability which it has demonstrated (Tully, 2006).
Fundamentally, it provides a convenient and economic means of gathering and
disseminating environmental and other useful information in the transport domain.
Existence of ZigBee based networking capability between Smartdust and other devices will
benefit many low cost and low power applications in the ITS. Smartdust devices also have
sensors attached to them to monitor the physical environment in some way. These sensors
can be built directly onto the Smartdust or can come as daughter-boards which can be
connected in some way to the motes main motherboard. Sensors can measure a wide range
of environmental parameters, such as pollution, noise, temperature, humidity as well as
vehicle speed, vehicle direction and vehicle presence. Initial studies suggest environmental
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monitoring, vehicle to vehicle, vehicle to infrastructure and infrastructure to infrastructure
applications may exist for Smartdust in the transport domain. Indeed the vital application of
the devices is beginning to be tested in the road vehicle environment. Even though a range
of Smartdust platforms are available in the market, Crossbow Mica (Crossbow, 2006) family
motes will be discussed here due to their commercial success in many wireless sensor
network applications.
Mica family motes can be programmed with NesC based TinyOS (TinyOS, 2006) , C based
NanoQplus (NanoQplus, 2008) and many other operating systems which are designed for
embedded systems with very limited resources. Also, MicaZ mote will be the most suitable
platform for the wireless sensor networks based ITS application since it features sensing and
networking capabilities with low power consumption using ZigBee as communication
protocol. Figure 1 shows a Crossbow MicaZ mote. The MicaZ mote is a family of the
Crossbow Mica motes where the radio transceiver uses the Chipcon CC2420 IEEE 802.15.4
(ZigBee) compliant chipset. This will allow the MicaZ motes to communicate with other
ZigBee compliant equipment. The software stack includes a MicaZ mote-specific layer with
ZigBee support and platform device drivers, as well as a network layer for topology
establishment and single / multi-hop routing features. The MicaZ mote platform is built
around the Atmel AtMega128L processor which is capable of running at 7.37 MHz. The
MicaZ motes have 128 Kbytes of program memory, 512 Kbytes of flash data logger memory,
and 4 Kbytes of SRAM. Power is provided by two AA batteries and the devices have a
battery life of roughly 1 year depending on the application (very low duty cycle assumed).
Sensor boards can be attached through a surface mount 51 pin connector, Inter-IC (I2C),
Digital Input Output (DIO), Universal Asynchronous Receiver Transmitter (UART) and a
multiplexed address/data bus.
Fig. 1. Crossbow MicaZ.
Here we present three projects in which Smartdust devices were used in the transport
domain. In the first project, the UK Department for Transport funded ASTRA (ASTRA,
2005) (Applications of Smartdust in Transport) project, a preliminary investigation on
Smartdust’s feasibility was undertaken, with an aim to find out Smartdust’s characteristics
in the transport domain. The other two projects TRACKSS (TRACKSS, 2007) (Technologies
for Road Advanced Cooperative Knowledge Sharing Sensors) and EMMA (EMMA, 2007)
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(Embedded Middleware in Mobility Applications), both were EC FP6 funded) built on the
information gathered from ASTRA project by implementing several ITS applications using
Smartdust. The TRACKSS project focussed on achieving this through collaboration between
Smartdust and other sensing technologies, whereas the EMMA project designed and
implemented a middleware that enables integration of Smartdust with heterogeneous
sensor platform in the transport domain.
3.1 ASTRA project
The ASTRA project investigated the use of mobile wireless sensor networks, and more
specifically, Smartdust for transport applications. The project examined the current state-of-the-
art with Smartdust devices, using MicaZ and Mica2 motes as the technology to be
tested. It also looked at the likely market and technological advances of the Smartdust
technology over the coming decade.
3.1.1 ASTRA project trials
A trial using Smartdust technology was hosted in Newcastle with a pervasive intelligent
corridor established by a network of fixed motes on roads near Newcastle Central Station.
Mobile motes were also placed in several buses. Communication between a static mote and
a moving mote on-board a vehicle was achieved, showing that communication can take
place between road side and vehicles using a network of motes. The architecture consisted
of a number of Fixed motes (F) attached to bus stops along a circular bus route and a
number of Mobile motes (M) carried by buses travelling in both directions along this route.
One special fixed mote was placed in an interface board connected to a Data Server PC. The
PC was connected to other Client PCs via the Internet. The range of the motes was such that
it was unlikely that fixed nodes would always be able to communicate directly. They
communicated with mobile nodes as buses passed bus stops. The buses communicated with
each other as they passed. Together, the fixed and mobile nodes formed an ad-hoc network.
M
M
M
F
F
F
M
F
Client
Browser
Data
Server
Fig. 2. ASTRA Experimental Setup.
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The motes were concentrated in an area of Newcastle close to the Central Station, as shown
in Figure 3 below. The red dots represent the bus stops which carried the motes. The buses
travelled along Grainger Street and Neville Street before crossing the River Tyne to
Gateshead (as seen in the map). The PC which acted as a Data Server was located in the
Centre for Life, in the lower left-hand corner of Figure 3.
3.1.2 Experimental results
In an urban road environment radio wave reflections from surrounding buildings and
objects can have a significant impact on the performance of the radio. In some situations the
performance of the radio is enhanced and in other situations the performance is reduced.
Reducing the motes radio transmitter power is likely to improve the performance of the
radio in some cases due to a corresponding reduction in the number of radio wave
reflections. Possible future research includes developing a way for motes to adjust their
radio power to obtain optimal radio performance at a given location.
Fig. 3. Wireless sensor networks intelligent corridor.
The map is presented above is the Central Station area which shows the quality of each link.
The quality of a link is based on the percentage of packets received, the higher the
percentage the higher the link quality. This is indicated on the map by varying the width of
the line between points where larger widths indicates higher quality links. A dashed line
indicates total packet loss. The results also show that the distance between nodes is not the
only factor in determining link quality. Radio reflections and interference seem to have
played a significant role in determining the link quality during this experiment. Many of the
shorter links did not show any connection whereas some longer links showed a good
quality connection.
Experiments also showed that the MicaZ motes outperform the Mica2 motes in rural and
urban environment due to their improved radio performance. Deploying MicaZ motes as
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opposed to Mica2 motes may therefore require fewer motes to cover the same geographical
area. Matching the correct motes for the environment in which they are used will clearly be
important. Although not tested within the trial, it is reasonable to conclude that the use of
Wi-Fi or Bluetooth would result in a worse performance than using ZigBee as the former
two technologies carry a greater overhead for new nodes joining a network.
Fig. 4. Packets lost MicaZ vs Mica2.
Several experiments showed that it is possible to create connections between moving
vehicles and a roadside infrastructure in an urban environment. It was also possible
vehicles travelling at high speed (70mph). The time available for data transfer during a
passage is a function of the radio range, the speed of the vehicle and the overheads
involved in having the mobile node join the network. The ASTRA trial findings are
summarized in Table 2.
Experiments were carried out to investigate the effect of mote packaging, height, antennae
position and position inside the vehicle (Bus/Car) in radio range, received signal strength
(RSSI) and packets loss. The experiment results showed these parameters have a
significant effect on radio range and packets loss but can be overcome transmitting
packets with high power level or using many devices to form a network (multi-hop
technology). Experience gained from the ASTRA project trials provided the opportunity
to use Smartdust devices in a range of transport prototype applications in the TRACKSS
and the EMMA project. It seems that the opportunities of using Smartdust as part of a
connected world (collaboration with other sensors/platform) for future intelligent
transportation system are high.
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Range (m) Max Power Signal (dBm) % Lost Packets
Weather
Range is greater at
lower temperatures
(9.0ºC - 100m, 17.1ºC
– 85m).
No difference.
No difference except
towards the limits of
range where packet
loss rates increase as
expected.
Packaging
The thicker the
material of the
packaging the shorter
the range (waterproof
box: 65m vs.
trunking: 100m).
Thicker materials absorb
more radio waves
resulting in lower signal
strength (The thicker
waterproof box displays an
average signal strength
reduction of 15 dBm when
compared with the
trunking).
No difference except
towards the limits of
range where packet
loss rates increase as
expected.
Urban
environment
Interference to the
radio (caused by
buildings, cars,
people, etc.) has an
effect on range.
Interference to the radio
(caused by buildings, cars,
people, etc.) has an effect
on signal strength.
Interference to the
radio (caused by
buildings, cars,
people, etc.) has an
effect on packet loss.
Position
(height)
The height of the
nodes has an effect
on range. The higher
the node the greater
the range (At a height
of 0m the range is
between 5m and 10m,
at heights of 1m and
2m the range is at
least 25m).
The height of the nodes
has an effect on signal
strength. The higher the
node the higher the signal
strength ( at a distance of
25m the signal strength at
heights 0m/1m/2m was
NONE/-98.5/-94.1)
The height of the
nodes has an effect on
packet loss. The
higher the node the
lower the number of
packets lost (at a
distance of 25m the
packet loss at heights
0m/1m/2m was
100%/37.7%/11.4%)
Position
(antennae)
The orientation of the
antennae has an effect on
the received signal
strength. The best
orientation of the antenna
is north and the worst
orientation is south (North:
-63.1 dBm, South: -80.4
dBm)
The orientation of the
antennae does have
an effect on packet
loss. The best
orientation of the
antenna is north and
the worst orientation
is east (North: 0%,
East: -2.4%)
Effect of
chosen radio
frequency
Choice of radio frequency
has an effect on the received
signal strength. In general
lower frequencies result in a
higher received signal
strength over the same
distance (868MHz: -57.9
dBm, 902MHz: -66.4 dBm,
928MHz: -72.7 dBm).
Choice of frequency in
general does not affect
packet loss. However
interference from
other devices using
the same frequency
can affect packet loss.
Table 2. ASTRA trial summary.
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3.2 TRACKSS project
The focus of the TRACKSS (Technologies for Road Advanced Cooperative Knowledge
Sharing Sensors) project was to research advanced communications concepts, open
interoperable and scalable system architectures that allow easy upgrading, advanced sensor
infrastructure, dependable software, robust positioning technologies and their integration
into intelligent co-operative systems to support a range of core functions in the areas of road
and vehicle safety and traffic management and control (TRACKSS, 2007). The overall aim
was to develop new systems for cooperative sensing and predict flows, infrastructure and
environmental conditions surrounding traffic, with a view to improving road transport
safety and efficiency. Figure 5 shows how TRACKSS can be used in improving
collaborations among various ITS sensors.
Fig. 5. How TRACKSS enabled sensor collaborations.
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One of the major results of the TRACKSS project is the framework that provides support
that allows communication between various sensors to be implemented readily and in a
straightforward manner. This framework was designed to enable collaborations among
infrastructure sensors, vehicle sensors, as well as vehicle and infrastructure sensors.
Multiple scenarios have been designed, describing how the various sensors might
collaborate through infrastructure, vehicle and mixed applications. There are two important
concepts defined and implemented in TRACKSS:
Knowledge Sharing Model (KSM)
The KSM serves as the core of the framework, defining the XML format of the data to be
exchanged among sensors, and providing a common API for communication. Data are
classified into several pre-defined types of knowledge, and they are passed around using
“publish and subscribe” mechanism supported by the KSM. The KSM platform is
implemented through two executables: ServerSAM (which acts as the coordinating server,
through which all communication is processed) and TrackssRouter (which handles the
communication related to each sensor).
Knowledge Sharing Sensor (KSS)
In TRACKSS, each sensor is turned into a KSS by integrating the common communication
protocols into its corresponding software. Each KSS can publish and/or subscribe to certain
types of knowledge; the communication details are handled automatically by the KSM
platform. Figure 6 presents the simplified version of the communication mechanism between
two KSSs in TRACKSS. More details on TRACKSS KSM and KSS can be found in [24].
Fig. 6. TRACKSS KSM and its support for KSS.
In this paper, one of the TRACKSS collaborations involving Smartdust and Near-Infrared
Optical Identification sensor is presented.
The Near-Infrared Optical Identification sensor was developed by LIVIC-LCPC (France) and
its purpose is to detect, identify objects (i.e. vehicles or roadside objects) and localize them in
the road scene. It is composed of two parts: an Emitter part and a Receiver part. The Emitter,
which is the active part, is a near-infrared LED-based lamp, which, thanks to an embedded
controller, codes an identifier (a number) using a defined frame protocol. The signal is thus
time-coded (blinking light) and not space-coded. This enables important improvements with
regard to detection ranges and robustness when compared to traditional spatial-pattern-based
identification systems. The Receiver is a high-frame-rate, low-resolution CCD camera,
equipped with an IR-bandpass filter, plus a decoding algorithm. By attaching an Emitter
(with its unique identifier) to an object (such as a road sign), the Receiver can detect,
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identify, and locate it on the road scene. Full details of the Near-Infrared Optical
Identification sensor can be found in (von Arnim, 2007 and 2008)
This collaboration was implemented using the support provided by the KSM and its
executable platform, turning the Smartdust and the Near-Infrared Optical Identification
sensors into two collaborating KSSs. Among others, this collaboration enables a more robust
Road Sign Recognition Application to be implemented, where one sensor’s capabilities can
complement the other’s weaknesses. This is based on the fact that no individual sensor is
perfect: each has its own features and limitations. In this case, the Near-Infrared Optical
Identification sensor has a very good range and localization features, but it relies on visual
detection. This makes it prone to errors when objects may obstruct the view, or in inclement
weather conditions. On the other hand, the Smartdust sensor cannot easily determine the
location of a target (since it relies on radio signal, which does not necessarily carry the
information on the detection direction), but the radio signal is free from visual impairment.
By combining the positive characteristics of these two sensors, we have developed a new
system that provides a more robust detection and identification of objects on the road.
3.2.1 Cooperative road sign detection application
By attaching the Smartdust transmitter and the Near-Infrared Optical Identification sensor
Emitter to a road sign, suitably equipped vehicles can detect, identify, and localize the road
sign ahead with sufficient time and even in poor visibility. For this application, we equip a
car with a Near-Infrared Optical Identification Receiver (camera), a Smartdust device (sitting
on its base station), and a computer connected to these sensors, on which the applications
controlling these sensors are running. We also place infrared Emitters and Smartdust devices
next to the sign on the side of the road; they act as the broadcaster of signal for representing
the road signs. Figure 7 provides a diagrammatical representation of this set-up.
Fig. 7. The set-up of the I2V equipments.
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On the on-board computer, both sensors communicate using TRACKSS’ KSM. Essentially,
the KSM serves a middleware that encapsulates a common communication protocol of these
two sensors, enabling them to exchange information in a straightforward and uniform
manner. This collaboration combines the long detection-range capabilities of the Near-
Infrared camera with the visual-independence feature of the Smartdust in order to develop a
more robust system for road sign detection.
In most cases, the Near-Infrared camera picks up the signal from the infrared Emitter prior
to the Smartdust base station picking the signal from the Smartdust device on the side of the
road. In these cases, the latter provides a confirmation of the detected road sign to the
former, hence increasing the confidence of the result. In other cases (for example, around the
bend or if there is some visual impairment), the Smartdust picks up the road sign signal
before the camera. The system will then warn the driver that a road sign is detected nearby,
but we do not have 100% certainty until this is confirmed by the camera.
Two examples of graphical displays can be seen on Figure 8. The left one shows a symbolic
representation of the road sign and the right one show an “augmented reality” view, where
the road sign is drawn at its real position on the image. One can easily understand the
benefit of this application for night-time driver assistance. Further details of this
collaboration – along with details of several other collaborations – can be found in (Arief &
von Arnim, 2008, von Arnim et al, 2008).
Fig. 8. Graphical display for road sign recognition.
The first major experimental result of this application is given by the fusion of identification
information from two different sensors with different technologies. When the fusion
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strategy is a confirmation of one sensor by the other, then the limitations of each sensor can
be compensated.
Concerning the detection range, with our confirmation strategy (where the target must be
within range to be simultaneously detected by both sensors), the final range is highly
influenced by the range of either sensor, depending on weather conditions. The Near-
Infrared Optical Identification sensor has a much higher range than the Smartdust sensor in
its best-case scenario (low-luminosity). On the contrary, in the worst-case scenario for Near-
Infrared Optical Identification sensor (sunny weather), the Smartdust usually has greater
range. Another interesting observation is that the Near-Infrared Optical Identification sensor
performs better when the vehicle is travelling at a high speed whereas Smartdust is better at
low speed. So to sum up, both sensors complement each other in their capabilities. Our
applications do not need a range over hundreds of meters. The fused sensors need to be able
to detect a road sign sufficiently early to warn the driver or start an action, at any speed up
to 130km/h. The ranges measured in both scenarios are sufficient. It is also important to
note that the range of the Smartdust sensor can be easily increased by chaining multiple
Smartdust sensors in an ad-hoc network ahead of the road sign. It is therefore possible for
both sensors to have a comparable detection range.
From these detection range data, we can also derive information about the “distance-to-be-covered
and available-time before passing the road sign”. This value is very much affected
by the speed of the vehicle; Table 3 shows the values at two different speeds: 50 km/h and
130 km/h. The road sign ID relates to the identification number of the road signs – in this
case: 7 (representing a danger sign), 9 (representing an end of 50 km/h speed limit) and 20
(representing a traffic light).
Speed Road sign Distance (m) Available time
50 7 141.7 10.2
50 9 133.3 9.6
50 20 125.0 9.0
130 7 89.5 2.4
130 9 135.2 3.7
130 20 95.5 2.6
Table 3. Distance and Time Range of Road Sign Detection.
These values compare favourably to the standard driver’s reaction time of 1 second. Even at
130 km/h, we have at least 2.4 seconds anticipation time before the road sign.
3.3 EMMA project
The EMMA (Embedded Middleware in Mobility Applications) project (EMMA, 2007) had
an overarching goal of utilising new embedded middleware to support the underlying logic
and communications required for future cooperating wireless objects and the applications
they may support in the automotive and road transport domains. The EMMA project was
committed to deliver a middleware platform and a development environment which
facilitates the design and implementation of embedded software for cooperative sensing
objects. The ultimate aim of the project was to hide the complexity of the underlying
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infrastructure whilst providing open interfaces to 3rd parties enabling the faster, cost-efficient
development of new cooperative sensing applications.
Fig. 9. EMMA hierarchical approach.
The EMMA network architecture can be considered at three levels (Figure 9): Within an
automotive subsystem, at a vehicle level and at the supra-vehicle level. Recently, many
wireless micro sensor network applications are being developed for a variety of applications
including transport monitoring and control. However, there are still numerous challenges to
be overcome if wireless sensor devices are to communicate with each other in an intelligent,
cost effective and reliable way. The EMMA project selected the principle of hierarchical
Wireless Co-operating objects (WICOs). The number and type of objects present in any
given WICO hierarchy may vary depending on the prevailing conditions and their
availability; however the handling of this can be abstracted away from the application
developer by the middleware wherever possible.
It was necessary to find suitable technologies to support and coordinate WICOs at all three
levels. Given this wide variety of sensor technology available and in common use, a popular
approach to manage the growing complexity of a typical development is to create a
malleable interface often called termed middleware. Middleware has been around for many
years in different forms. “Middleware” in the loosest terms covers a wide variety of system
integration schemes.
Evaluation of many middleware technologies has often reached the conclusion that
middleware is a large and complex system, requiring careful coupling to, and tuning for, the
specified application domain (EMMA, 2007). As a result there is a need to identify and
develop a middleware system optimised for the challenges present in the intelligent
transportation domain. A differentiating factor between current middleware models and the
proposed EMMA system is the need to operate on multiple different platforms, with
substantially different resources available.
Current middleware technology is typified by systems designed to run in non-real time,
resource rich environments, such as large business information systems. A typical traffic
infrastructure or automotive application runs in a real-time environment with strictly
limited resources, distributed across many control units, themselves incorporating multiple
dissimilar sensors (Hadim and Mohamed, 2006, Katramados et al, 2008).
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EMMA middleware is capable of integrating systems for transportation application using
large powerful fully featured PowerPC based FPGA technology (Xilinx ML403) which uses
Qplus operating systems, as well as compact, battery-powered Smartdust devices which
uses NanoQplus operating systems. An additional capability of the EMMA middleware is
the ability to handle dynamic, ad-hoc networks. These are a natural result of
communications between moving vehicle-based environments and infrastructure systems
with finite static coverage. It must be possible to handle the potential changes to the network
composition whilst insulating the application developer from the technical challenges that
this involves.
Fig. 10. EMMA Middleware.
The core part of the middleware is independent from the operating system and platform it is
running on. This part provides several functions to the application through the middleware
API. Since the different platforms vary strongly in resources like memory size, energy or
CPU speed, not all functions will be provided on all platforms. The OS independent part,
therefore, has to be modular. The adapters also include conversions for the APIs of different
communication technologies, e.g. ZigBee or CAN. Each communication technology uses, for
example, a different addressing scheme that has to be hidden from the EMMA applications.
Therefore, the middleware defines an independent addressing.
The EMMA middleware is designed in a modular fashion. The communication adapters
form the interface between the communication module and the actual hardware drivers. The
communication module has a generic part and two specialised parts for message and data-centric
communication (Yang & Wang, 2007). The detailed description of each of the
modules is provided below.
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Fig. 11. Modular Components of EMMA Middleware.
Security is also an integral part of any ITS application which can be achieved with the
combination of three core components, namely authentication, encryption and mechanisms
for access control. The EMMA middleware provided both conceptual support for pre-distributed
symmetric keys and conceptual support for symmetric encryption but no
support for access control mechanism. Another FP7 funded PECES (PECES, 2009) project (a
follow-up project of the EMMA project) is currently developing a complete security suite for
pervasive computing applications including transport domain. The PECES project security
suite make use of existing encryption protocols and standards to ensure the authenticity of
devices, to exchange encryption key between communicating devices and to provide
appropriate access control mechanisms to limit information sharing in and between devices.
More detail information about PECES project security concepts can be found in PECES
project Secure Middleware Specification deliverable (Handte et al, 2010).
The security module in the EMMA middleware provides management of keys and
encryption and decryption function. The actual functions that perform encryption and
decryption have to be provided by the application developers but the EMMA middleware
provide mechanisms for calling them at the right time. Before a received message is
delivered to the application, the security module is called to decrypt the message using the
provided key and decryption function. Conversely, it is also called before handing over the
message to the communication layer for sending.
In the road infrastructure domain, the scalability of the EMMA middleware allows it to be
embedded in a variety of platforms from tiny Smartdust sensors to traffic control systems.
By developing these devices using a common middleware based platform, the individual
Smartdust devices can be configured as Wireless Cooperating Objects (WICOs). This WICO
based sensor network allows the system to be flexibly configured to take advantage of
changing conditions, for example with transitory sensors joining or leaving the fixed
network as appropriate. The EMMA middleware provides a whole new range of
opportunities for exchanging data amongst different vehicle sensors, subsystems and the
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engine. Numerous applications are continuously being developed aiming to improve the
safety and enhance the driving experience. However, often such systems are isolated from
the rest of the vehicle and their sensor information cannot be reused. As a solution, the
EMMA middleware transforms such sensors to cooperating objects, giving the opportunity
to develop new data fusion applications.
3.3.1 Vehicle to infrastructure application
Applications were developed in order to demonstrate the benefits of the middleware for
giving priority to emergency vehicles. For the infrastructure level application, an emergency
vehicle (ambulance, fire engine or police car, etc) are equipped with a Smartdust WICO (or a
MicaZ mote installed with the EMMA middleware) which broadcast a beacon message if it
is on an emergency situation. For example, in a busy intersection controlled by traffic lights,
emergency vehicles are detected and given priority by regulating the state of the traffic
lights (Figure 12).
Fig. 12. Giving priorities for Emergency Vehicle.
In the Vehicle to Infrastructure application demonstration, a Smartdust WICO connected to
a traffic regulator that acted directly by providing information to the regulator about an
emergency situation. Smartdust WICOs were placed in the infrastructure to relay this
message to the traffic light regulator. It was placed so far as needed in order to give time to
the traffic regulator to change its status taking into account the time lost due to
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communication mechanisms (publish/subscribe) and other time periods the traffic regulator
needs in order to guarantee safety first. The traffic regulator has been programmed to attend
to the trigger signal provided by the mote activating an emergency control sequence. The
demonstration has successfully carried out real road environment in Valencia, Spain.
An inter-hierarchical application, integrating all three levels that developed in the project:
the automotive subsystem, the car level and the supra-car level, was also developed to
demonstrate how heterogeneity issues can be solved in wireless sensor networks developing
EMMA middleware. In this application demonstration, inter-hierarchical collaboration of
the WICOs developed on the project consisted in transforming the information provided by
the vehicles at both engine level (cylinder pressure and oil pressure sensors are based on
TelosB platform) and vehicle level (GPS, Vehicle dynamic and Radar sensors are based on
Xilinx ML 403 platform (Katramados et al, 2008) ) into specific traffic control actions at
infrastructure (to Smartdust WICO) level. In the infrastructure, a Smartdust WICO was
connected to a portable VMS to display the information sent by the engine level and vehicle
level sensors. The inter-hierarchical application scenario is explained in Figure 13 with the
EMMA middleware components.
Fig. 13. Inter-hierarchical application.
The above application scenarios were implemented and tested with the EMMA middleware.
This was used as a test bed to check the correct operation of EMMA approach for wireless
sensor networks. It is worth mentioning that wireless pervasive computing applications for
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ITS can be developed using the EMMA middleware but validation of such scenarios would
require an extensive use of cooperative transport systems which is out of scope of the EMMA
project. The EMMA middleware aims to provide an efficient platform to develop embedded
applications for future intelligent transportation systems. The big advantage is that the EMMA
approach is completely open to any systems and easily adapt to different applications.
Several set of experiments have been carried out with the EMMA middleware to evaluate
possible use of Smartdust WICO in the supra-vehicle level as well as engine level and
vehicle level application. These experiments intend to evaluate the use of wireless sensors
with the EMMA Middleware for the proposed application scenarios at three different
hierarchical levels. The engine level and vehicle level sensors are static networks and
experiments showed satisfactory results for the application. The following section provides
some experiments of the vehicle to infrastructure scenario. Two Smartdust WICOs were
used and the first WICO was programmed with the EMMA Send message component and
this WICO transmitted a packet for every, 500ms and 1000ms. The second WICO was
programmed with the EMMA receive message component and connected to the MIB520
programming board which in turn was connected to a Laptop computer.
Open space experiment
This experiment was carried-out on the Exhibition Park, a large grassy area to the north of
the Newcastle University. Each scenario, 100 packets were sent and those packets were
received. Both WICOs were placed at 1m above the ground. The WICOs power level was set
to maximum level. Each scenario repeated three times and calculations were performed
offline to determine how many messages were lost at each distance and average values
reported in Figure 14.
Fig. 14. Open space packets lost.
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Urban environment experiment
This experiment was carried-out on the Claremont Road, a busy road near to the Newcastle
University. Each scenario, 100 packets were sent and those packets were received. Both
WICOs were placed at 1m above the ground. The WICOs power level was set to maximum
level. Each scenario repeated three times and calculations were performed offline to
determine how many messages were lost at each distance and average values reported in
Figure 15.
Fig. 15. Urban environment packets lost.
Mobile environment experiment
This experiment was carried-out on the Claremont Road up to 40mph and at the Motorway
near to the Newcastle airport for higher speeds. The first Smartdust WICO was placed on a
road side stand 1m from the ground and the second Smartdust WICO was placed on the
middle of the dashboard of a vehicle. The Smartdust WICO at the vehicle sent message
periodically which was received by the Smartdust at the road side. Each scenario repeated
three times and calculations were performed offline to determine how many messages were
lost at each distance and average values reported in Figure 16.
The open space experiment and urban environment experiment show that packets can be
received without any packet lost up to 45m distance. The percentage of packets lost
increases above 45m distance in both cases. In the mobile environment experiment, the
received packets decrease with the speed increases as the WICO is in range for a shorter
period of time. This means that communication time window decreases with the vehicle
speed. At the 70mph speed, The WICO in the roadside received 5 and 11 packets for sending
intervals 1000ms, 500ms respectively. And interestingly, there was no packets lost were seen
between the first packet and the last packet received. The mobile environment experiment
demonstrated that Smartdust WICO can be used with the EMMA middleware
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communication methods between a fixed infrastructure WICO and also fast moving
vehicle-based WICO applications. This is an important finding which proves that the
ZigBee Smartdust WICO do not suffer from any Doppler effects at normal motorway
(70mph) speeds. It shows that the EMMA middleware communication functionality
works well even the vehicle travelling at 70mph.
Fig. 16. Mobile environment packets lost.
4. Discussion
This chapter has summarised the research undertaken at Newcastle University to
investigate the use of Smartdust devices for Intelligent Transportation System Applications.
The costs of building and maintaining the infrastructure could be amortised over many such
services delivered by Smartdust based wireless sensor networks. Research to deliver this
concept of connected mobile devices and infrastructure leads to the opportunity to consider
realistically for the first time a fully connected Intelligent Transport System for the future.
Research is currently focused on filling in the knowledge and technology gaps in pervasive,
mobile ad-hoc wireless systems for a range of transport applications. Mobile wireless
systems are beginning to be proven as a future tool that will enable the joining up of
vehicles, individuals and infrastructure into a single ‘connected’ intelligent infrastructure
system. Embedding this technology in infrastructure – such as environmental sensors in
lampposts, embedded in vehicles and infrastructure, in goods, and even connecting
individuals through their Smartphones, or even bespoke wearable wireless interfaces – offer
potential for a more all-seeing, all knowing ITS infrastructure. If for example, vehicles are
continually in wireless communication with the infrastructure, new paradigms for traffic
monitoring and control could be considered, road space allocated more efficiently and
incidents dealt with in an optimum way. If vulnerable users have such wireless devices, the
infrastructure could warn vehicles to slow down and the drivers to be more vigilant –
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indeed wireless devices attached to children could for example warn drivers that children
are playing out on the street, just around the corner and to reduce speed now. Such devices
could help with security and safety of individuals, be used on airline boarding cards and
other tickets, and even be used to verify HOV (high occupancy vehicles) or blue badge
entitlement. When such a system is also connected to say, a vehicle’s CAN-bus, then
information on driving style, strange driving behaviour (say where there is a badly
maintained stretch of road or object in the road, could be detected from the CAN data –
allowing mitigating and maintenance actions to be automatically triggered).
Many of these devices can carry payloads such as sensors, and the idea of monitoring
pollution with these devices in a pervasive way is beginning to be researched by Newcastle
University (with pervasive wireless environmental sensors being attached to lampposts).
However if these devices become small and cheap enough (as is the future vision for
Smartdust) then one could image that we each carry our personal exposure meter. Moreover
with ‘extreme’ sensor design, wireless pollution sensors could be fitted in engine manifolds
and exhaust pipes to allow the actual pollution generated by a vehicle to be measured and
maybe adjustments to driving style or engine management systems can be advised or made
to mitigate some of the pollution effects (early prototypes are being developed at the
university at the moment). If future ‘carbon allowances’ are to be considered in the
connected car, the pollution the car generates will also need to be measured and monitored
– as proposed in the Smart Market Protocols project where auction and trading-based
carbon allowances have been considered.
Significant research is required to fully realise the potential of such wireless systems, not just
on the transport application side, but challenges to reduce the size of these devices from
‘smart-lumps’ to ‘Smartdust’ is critical as size, cost and power consumption of these devices
will dictate whether the devices will become pervasive in the transport domain. This
requires detailed work on antennae design, an investigation as to which is the most
appropriate communications frequency, 802.11x, the influence of CALM, Wi-Fi and
probably the most important challenge being battery power requirements (using power
scavenging or other techniques).
The final key area of research which is still embryonic is in low-cost and robust sensor design –
much work is on-going but uncoordinated in the transport domain. The robustness and
dependability of mobile sensor devices, suitable communications protocols and e-science
techniques to deal with the data are crucial. Also important is the issue of privacy and data
protection in a potentially all-seeing, all-knowing connected world, which rise the question of
how much information we want, need and what level of intrusion are we willing to bear.
5. Conclusion
It is clear that the next generation of vehicles will be required to have increased safety, lower
emissions and more entertainment with higher performance than those of today. The
innovations in wireless sensor devices and digital electronics will enable novel automotive
applications which will become very common in future ITS. The challenges such as
integrating heterogeneous devices for specific ITS application can be achieved by
developing technologies such as in the TRACKSS and the EMMA projects.
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This chapter has presented research projects that has been undertaken to investigate the
suitability of using Smartdust and ZigBee technology for the intelligent systems applications.
A selection of the applications and experiments carried out to systematically characterise the
Smartdust technology in the road domain were presented here. The ability to communicate
between vehicle and roadside illustrates that Smartdust will enable efficient and discrete
communications between vehicle and roadside infrastructure – as the unit cost of Smartdust
devices will continue to go down – this is a significant contribution to the ITS domain.
6. Acknowledgment
We would like to thank Axel von Arnim for his contribution to the Road Sign Recognition
application during his time at LIVIC-LCPC. We also would like to thank Alberto Zambrano
Galbis (ETRA R+D, Spain) and Carl Shooter (TRW Conekt, UK) for their contributions to the
EMMA project vehicle to infrastructure level application. The work presented here was
sponsored by various projects, including UK DfT-funded ASTRA project, EU-funded
TRACKSS and EMMA projects.
7. References
Arief, B. & von Arnim, A. (2008) TRACKSS Approach to Improving Road Safety through
Sensors Collaboration on Vehicle and in Infrastructure, Proceedings of the 2nd IEEE
International Symposium on Wireless Vehicular Communications (WiVeC 2008),
Calgary, Canada, 21-22 Sep. 2008
Arief, B.; Blythe, P.T.; Fairchild, R.; Selvarajah, K. & Tully, A. (2007). Integrating Smartdust
into Intelligent Transportation Systems, Technical Report CS-TR 1062, School of
Computing Science, Newcastle University, Dec 2007
ASTRA(2005). “ASTRA Project” Project, last accessed 24 April 2011, Available from:
http://research.cs.ncl.ac.uk/astra/
Bluetooth (2006). “Bluetooth (2006) Technology website”, last accessed 24 April 2011,
Available from: http://www.bluetooth.com/Bluetooth/Technology/
Blythe, P.T. (2006). Intelligent Infrastructure: A Smart Future with Smartdust and Smart
Markets, Proceedings of IFAC Conference on Transport automation and Control, Delft,
August 2006
Cai, H. & Lin, Y. A Roadside ITS Data Bus Prototype for Intelligent Highways, IEEE
Transaction on Intelligent Transportation Systems, vol. 9, no. 2 , pp.344 – 348, June 2008.
Car2Car Consortium (2007). “Car2Car Consortium website”, last accessed 30 April 2011,
Available from: http://car-2-car.org/,
Chang, B. J; Huang, B. J. & Liang, Y. H. (2008). Wireless Sensor Network based adaptive
vehicle navigation in multi- hop Relay WiMAX networks, Proceeding of the 22nd
International Conference on Advanced Information Networking and Applications, March
2008.
Chen, Y.; Cheng, L.; Chen, C. & Ma, J. (2009). Wireless Sensor Network for Data Sensing in
Intelligent Transportation System, Proceeding of the 69th IEEE Vehicular Technology
Conference, 2009
Cook, J. A .; Kolmanovsky, I.V.; McNamara, D.; Nelson, E.C. & Prasad, K.V. (2007). Control,
Computing and Communications: Technologies for the Twenty-First Century
Model T, Proceedings of the IEEE, vol. 95, no. 2, pp. 334 – 355, Feb. 2007.
www.intechopen.com

167
Crossbow (2006). “MPR/MIB User’s Manual”, last accessed 21 April 2011, Available from:
http://www.memsic.com/products/wireless-sensor-networks/wireless-modules.
html,
EMMA (2007). “EMMA Project”, last accessed 24 April 2011, Available from:
http://www.emmaproject.eu/
ETSI (2009). “ETSI website”, last accessed 10 July 2011, Available from:
http://www.etsi.org/WebSite/Technologies/IntelligentTransportSystems.aspx
Hadim, S. & Mohamed, N. (2006). Middleware: middleware challenges and approaches for
wireless sensor networks, IEEE Distributed Systems online, volume 7, no. 3, March
2006.
Handte, M.; Haroon, M.; Apolinarski, W.; Rak, Z.; Zambrano, A.; Selvarajah, K. (2010). D4.1
Secure Middleware Specification, Available from: http://www.ict-peces.eu/
Heddebaut, M.; Deniau, V. & Adouane, K. (2004) . In-vehicle WLAN radio-frequency
communication characterization, IEEE Transactions on Intelligent Transportation
Systems, volume 5, issue 2, page(s):114 – 121, June 2004
ISO TC204 Working Group (2008). “CALM Concept”, last accessed 20 May 2011, Available
from: http://www.isotc204wg16.org/concept
Katramados, I.; Barlow, A.; Selvarajah, K.; Shooter, C.; Tully, A. & Blythe, P. T. (2008).
Heterogeneous sensor integration for intelligent transport systems, Proceeding of
the IET Road Transport Information and Control – RTIC 2008 and ITS United Kingdom
Members Conference, Manchester, UK, 20-22 May 2008.
Kong, F. & Tan, J. (2008). A collaboration based hybrid vehicular sensor network
architecture, Proceeding of the International Conference on Information on Information
and Automation, 2008
Kosch, T. ; Kulp, I.; Bechler, M.; Strassberger, M.; Weyl, B. & Lasowski, R. (2009).
Communication architecture for cooperative systems in Europe - [Automotive
networking series], IEEE Communications Magazine, vol. 47, no 5, pp.116 – 125,
May2009.
Lee, S.; Yoon, D. & Ghosh, A. (2008). Intelligent parking lot application using wireless
sensor networks, Proceeding International Symposium on Collaborative Technologies and
Systems, 2008
Marqués, A. (2007). Cooperative Sensors Making use of a Common Knowledge Sharing
Model, Proceeding of the ITS World Congress, Beijing, China, October 2007
NanoQplus and Qplus (2008). “NanoQplus and Qplus website”, last accessed 21 April 2011,
Available: http://www.qplus.or.kr
Navet, N.; Song, Y.; Simonot-Lion, F. & Wilwert, C. (2005). Trends in Automotive
Communication Systems”, IEEE Proceedings, vol. 93, no.6, pp.1204 – 1223, June 2005
PECES (2009). “PECES Project”, last accessed 3 October 2011, Available from:
http://www.ict-peces.eu/
Selvarajah, K.; Tully, A. & Blythe, P.T. (2008). Integrating Smartdust into the embedded
middleware in mobility application (EMMA) project, Proceedings of the IET Road
Transport Information and Control – RTIC 2008 and ITS United Kingdom Members
Conference, Manchester, UK, 20-22 May 2008.
TinyOS (2006). “TinyOS website” last accessed 21 April 2011, Available from:
http://www.tinyos.net/
TRACKSS (2007). “TRACKSS Project” last accessed 24 April 2011, Available from :
http://www.trackss.net/
www.intechopen.com

168
Tully, A. (2006). Pervasive tagging, sensors and data collection: a science and technology
review for the foresight project on intelligent infrastructure systems, IET Intelligent
Transport Systems, vol. 153, pp.129 – 146.
UK DfT(2007). “Intelligent Transport Systems (ITS) – The policy framework for the road
sector”, last accessed on 20 May 2011, Available from:
http://webarchive.nationalarchives.gov.uk/+/http:/www.dft.gov.uk/about/strat
egy/transportstrategy/eddingtonstudy/
US DOT (2010). “IntelliDrive whitepaper”, last accessed on 20 June 2011 . Available from:
http://www.its.dot.gov/press/2010/vii2intellidrive.htm
US DOT (2011). “US Connected Vehicle Research program”, last accessed on 20 June 2011,
Available from: http://mobilesynergetics.com/
von Arnim, A. ; Perrollaz, M. & Chevreau, J-M. (2007) Système Optique d’Identification d’un
Véhicule”, patent #fr-0752663, 15 Jan 2007
von Arnim, A.; Arief, B. & Fusée, A. (2008). Cooperative Road Sign and Traffic Light Using
Near Infrared Identification and ZigBee Smartdust Technologies, Proceedings of the
15th World Congress on Intelligent Transport Systems (ITS 2008), New York, USA, 16-
20 Nov 2008
von Arnim, A.; Perrollaz, M.; Betrand, A. & Ehrlich, J. (2007). Vehicle Identification Using
Near Infrared Vision and Applications to Cooperative Perception, Proceedings of the
IEEE Intelligent Vehicles, Istanbul, 13 Jun 2007
Warneke, B.; Last, M. & Pister, S. J. (2001). Smartdust: communication with a cubic-millimeter
computer”, IEEE computer, vol. 34, no. 1, pp. 44 – 51, Jan 2001.
Yang, L. & Giannakis, G. B. (2004) Ultra-wideband communications: an idea whose time has
come, IEEE Signal Processing Magazine, vol. 21, no. 6, pp.26 – 45, Nov. 2004
Yang, L. & Wang, F-Y. (2007). Driving into Intelligent Spaces with Pervasive
Communications, IEEE Intelligent Systems, vol. 22, no. 1, pp:12 – 15, Jan.-Feb. 2007
ZigBee Alliance (2006). “ZigBee Alliance website”, last accessed 24 April 2011, Available
from: http://zigbee.org/,
www.intechopen.com

Edited by Dr. Ahmed Abdel-Rahim
ISBN 978-953-51-0347-9
Hard cover, 206 pages
Publisher InTech
Published online 16, March, 2012
Published in print edition March, 2012
Intelligent Transportation Systems (ITS) have transformed surface transportation networks through the
integration of advanced communications and computing technologies into the transportation infrastructure. ITS
technologies have improved the safety and mobility of the transportation network through advanced
applications such as electronic toll collection, in-vehicle navigation systems, collision avoidance systems, and
advanced traffic management systems, and advanced traveler information systems. In this book that focuses
on different ITS technologies and applications, authors from several countries have contributed chapters
covering different ITS technologies, applications, and management practices with the expectation that the
open exchange of scientific results and ideas presented in this book will lead to improved understanding of ITS
technologies and their applications.
How to reference
In order to correctly reference this scholarly work, feel free to copy and paste the following:
Kirusnapillai Selvarajah, Budiman Arief, Alan Tully and Phil Blythe (2012). Deploying Wireless Sensor Devices
in Intelligent Transportation System Applications, Intelligent Transportation Systems, Dr. Ahmed Abdel-Rahim
(Ed.), ISBN: 978-953-51-0347-9, InTech, Available from: http://www.intechopen.com/books/intelligent-transportation-
systems/deploying-wireless-sensor-devices-in-intelligent-transportation-system-applications
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