ISSN 2277
2277-2685
IJESR/Sept. 2015/ Vol-5/Issue
5/Issue-9/1248-1253
T. Lakshmi Deepak et.al.,/ International Journal of Engineering & Science Research
WIRELESS SENSOR APPLICATION IN TRAFFIC CONGESTION CONTROL AND
CLASSIFICATION OF VEHICLES
T. Lakshmi Deepak*
eepak*1, D. Hepzibha Rani2, D. Ravi Kumar3
1
2
M. Tech (Embedded Systems), Dept. of ECE, Gudlavalleru Engineering College, Seshadrirao Knowledge
Village, Gudlavalleru (A.P), India.
Asst. Prof, Dept. of ECE, Gudlavalleru Engineering College, Seshadrirao Knowledge Village, Gudlavalleru
(A.P), India.
3
DGM, BEL, Bangalore, India.
ABSTRACT
In today’s
y’s world the vehicular traffic is increasing constantly, especially in case of urban traffic system, existing
traffic management solutions are not efficient enough to meet the requirement. It is being witnessed in our daily
life through persistent traffic jam and high number of accidents. Wireless sensor networks (WSN) based
intelligent transportation systems (ITS) had emerged as a cost effective solution that bear a pivotal potential to
overcome these difficulties. Technology enables a new broad range of smart city applications around urban
areas and these sensing technologies includes traffic safety, traffic congestion control, road state monitoring,
vehicular warning services, and parking management. The main contribution of this paper is to identify the
presence of the vehicles and to classify vehicles based on the collected data using WSNs based ITS.
1. INTRODUCTION
In today’s world, number of traffic jams and accidents in urban and metropolitan areas are constantly increasing
towards more stressful and
nd leads to dramatic consequences on human health, and environment. Intelligent
transportation System (ITS) detects vehicles in predefined positions and these are based on bulky and power
hungry devices which are using wired technologies for communication and power supply. This increases their
installation, maintenance, and reparation cost and subverts the scalability of ITS affecting thus their major
objectives.
Advances in embedded systems and wireless technology give birth to wireless sensor networks (WS
(WSNs) which
are composed of cheap, effective and tiny devices that communicate wirelessly and sense the surrounding
environment. Each device node contains a sensor, a processor, a memory, a radio, and energy source as
depicted. This technology has a great potential
potential to overcome existing difficulties of ITS. Existing solution for
vehicle detection are ultrasonic sensor, induction loop, video camera and infrared sensor. Ultrasonic sensors are
much more expensive than the pulse models and therefore rarely used and
and the main disadvantage of ultrasonic
system is that its performance is affected by temperature change and air turbulence. An inductive loop provides
a high accuracy, but the installation requires road cutting and maintenance is also high. Video camera can
provide good visualization, but it is expensive and requires complicated video process. The infrared sensor is
sensitive to environment noise. On the contrary, the magnetic sensor shows more immune towards environment
noise, such as rain, fog and wind thann any other sensor and in addition the magnetic sensor has the advantage of
easy installation and maintenance.
Fig. 1: Block diagram of simple vehicle detection and monitoring system
*Corresponding Author
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T. Lakshmi Deepak et.al.,/ International Journal of Engineering & Science Research
In this paper, the focus is to build a traffic and road monitoring system for intelligent route planning, road usage
and protection that fulfil the constraints. The system should work under diverse road condition, dense and
shapeless traffic and a large diversity of vehicles. The vehicle detection based on WSN using An isometric
Magnetic Resistive (AMR) sensor. The aim is to provide a price effect, easy to organize, robust, flexible and
less maintenance wireless solution for vehicle detection and classification different classes of vehicles. The main
contributions of this paper are as follows.
a) A WSN based system architecture for identification of the vehicles using the magnetic sensors and collecting
the data.
b) Classification of the vehicles based on the data collected by the sensors.
2. OVER VIEW OF DETECTION SYSTEM AND CLASSIFICATION
A Wireless Sensor Network (WSN) is a network of small sensor nodes (SN) communicating among themselves
using wireless communication, to sense the physical world. The WSN composed of three kinds of nodes
including detection nodes, routing nodes and a base station node and these nodes can be freely and dynamically
organized into an ad-hoc wireless network. In the traffic application, magnetic sensors are placed at known
locations either on the pavement or on the road and it obtain the magnetic signature of vehicles travelling over
the sensors. The signals are processed through a vehicle detection algorithm by the sensor nodes, detection
events are then generated and transmitted to the access point and having collected the event data from these
synchronized sensor nodes, the access point can then calculate the count of different types of vehicles,
occupancy of the road and speed of the monitored traffic. Finally, this real time traffic information is passed to
the Traffic Management Center (TMC) or to the local control unit for applications like traffic monitoring and
signal control.
Fig. 2: WSN for vehicle detection and monitoring
Almost all vehicles have significant amounts of ferrous metals in their body (iron, steel, nickel, cobalt, etc.), the
magnetic field disturbance created by a vehicle is sufficient to be detected by a magnetic sensor, which makes it
a good enough for detecting vehicles. Potential vehicle detection applications that could use these magnetic
sensors include traffic surveillance, railroad crossing control, parking lot space monitoring and automatic gate
opening.
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T. Lakshmi Deepak et.al.,/ International Journal of Engineering & Science Research
Fig. 3: The disturbance of Earth’s magnetic flux lines by a vehicle
The above picture gives a pictorial representation of the disturbance of the magnetic flux lines when the Earth's
magnetic field penetrates a vehicle. The earth’s magnetic field strength is roughly equal to a half-gauss in
magnetic flux density, so a low field and high sensitivity magnetic sensor is needed to measure the disturbance.
The flux lines prefer to pass through the ferrous material of the vehicle. As the vehicle moves along, it is always
accompany by a concentration of the flux lines known as its “magnetic shadow” There is condensed flux to the
sides of the vehicle and amplified flux above and below it. An AMR sensor installed within the pavement
detects the amplified flux below the vehicle. One type of these low field magnetic sensors is the magnetoresistive sensor, which is very suitable for use in a sensor node because of its small size. Magneto-resistive
sensor can be further classified into Anisotropic Magneto-Resistive (AMR) and Giant Magneto-Resistive
(GMR) types. An AMR sensor is directional, which means it only provides an amplitude response to the
magnetic field along its sensitive axis, whereas a GMR sensor has little directionality.
2.1 AMR Sensor
Anisotropic Magneto-Resistive (AMR) sensors as an upgrade from older and simpler vehicle detection systems.
With the small size and simplicity of these wheat stone bridge based sensors, many applications are now able to
deploy many of these sensors cost-effectively, and gain more information on nearby vehicles. Appealing to the
fact that almost all road vehicles have significant amounts of ferrous metals in their chassis (iron, steel, nickel,
cobalt, etc.), magnetic sensors are a good candidate for detecting vehicles. Today, most magnetic sensor
technologies are fairly miniature in size, and thanks to solid state technology, both the size and the electrical
interfacing have improved to make integration easier. In traffic application, it is essential to isolate one vehicle’s
magnetic signal from the other vehicle signals from vehicles in different lanes and travel direction. So an AMR
sensor is a much better choice for use in the sensor node. Basically, the AMR sensor is a Wheatstone bridge
device as shown in Fig. It is made out of a nickel-iron (Perm alloy) thin-film deposited on a silicon wafer and
patterned as a resistive strip element. In the presence of a magnetic field, a change in the bridge resistive
elements causes a corresponding change in voltage across the bridge outputs. These resistive elements are
aligned together to have a common sensitive axis that will provide positive voltage change with magnetic fields
increasing in the sensitive direction.
Fig. 4: AMR Sensor Bridge and vehicle detection system
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T. Lakshmi Deepak et.al.,/ International Journal of Engineering & Science Research
For AMR sensors, the sensor resistive elements are oriented as a resistive “wheat stone bridge” that varies
resistance slightly as the magnetic field changes upon each element. The resistive elements are made of perm
alloy thin films and have around 1000 ohms of resistance, but each element is precision matched to within an
ohm of each other when no magnetic fields are present. Each bridge has four resistive elements with opposite
elements being identical. For example, if the bridge receives a positive magnetic field or lines of magnetic flux
in the sensitive axis, the Vb to Out+ and Out- to GND elements will slightly decrease in resistance while the
other two elements will increase in resistance. The result will be that the voltage at Out+ increases above Vb/2
and the voltage at Out- decreases from Vb/2. The vehicle detection system consists of the two types of modules
and they are master module and slave module. The slave module contains the sensor and transceiver for
communicating with the master, buried along the length of the road with a distance between them and the master
module contains transceiver for receiving data from the slave module. This master module and slave module
communicate with each other using the radio channel.
2.2 Communication
Ability of wireless communication is one of fundamental properties of the sensor node. Communication
capability of WNS nodes are in most practical cases limited by communication range and data rate. It is
important to remember that increasing of communication range requires increasing of transmit power/receiver
sensitivity, i.e. energy consumption is increased as well.
The communication protocol can be summarized as follows:
1. All the sensor and repeater nodes are configured with a pre-assigned radio channel and a communication
time slot before installation.
2. A synchronization message is send by the AP periodically with a random back off time, so all the reachable
nodes can catch this message eventually.
3. The radios of all the synchronized sensor nodes will wake up during its assigned time slot for two-way
communication, and quickly switch back to sleep mode afterward.
4. The repeater nodes are configured with the time slots of a multiple number of sensor nodes, so that they can
wake up at the right time to collect data from these sensor nodes and transmit the data back to the AP at their
own time slots.
5. After data processing is done at the AP, useful traffic data are transmitted to the Traffic Management Center
(TMC) for further traffic controls.
The exclusion of multi-hop communication between sensor nodes greatly simplifies the protocol. The drawback
is that the network coverage is limited by the maximum communication range between the AP and repeater
nodes (e.g. 1000). For the implementation of a traffic surveillance station, this coverage is more than enough in
most cases. It is claimed that a system lifetime of 10 years can be achieved for a typical vehicle detection
application.
Fig. 5: Communication links structure
2.3 Classification
Vehicle classification refers to the process and methodology to classify a vehicle based on the data collected in a
specific format into a pre-defined vehicle class. It is an important source of information for transportation design
and management that can be used for many purposes. The distribution of vehicle types also provides valuable
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T. Lakshmi Deepak et.al.,/ International Journal of Engineering & Science Research
data analysis input to the prediction of highways capacity, assessment of the effectiveness of traffic legislation,
automatic toll collection, weight enforcement strategies and environmental impact studies. Vision-based
classification can achieve a correct classification rate. The major limitation of vision-based classification is that
the system’s performance is greatly affected by the environmental and lighting conditions. On the other hand,
vehicle classification by wireless sensor networks provides a much more flexible deployment configuration,
making the system portable and, once again, scalable for large scale deployment. The magnetic sensor measures
the Earth’s magnetic field, which has different inclination angles in different geographical locations. Since the
Earth’s magnetic field is uniform over the surface in the scale of kilometre, vehicle signatures of the same
vehicle measured at adjacent locations in the same travelling direction are expected to be the same. Since the
transfer of information is very energy intensive, we try to minimize the volume of transmitted data during the
WSN design stage. It is therefore important to realize essential part of the data processing directly at the point of
its origin - in a sensor node. Now the data is collected at the Traffic Management Center (TMC), based on the
essential part of the data collected by the sensors the vehicles are classified into different classes and the count
of the each vehicle class is done based on the data i.e. each vehicle class has a specific sequence of numbers for
identification. The data will be collected from the database and a graph is plotted in the Mat lab taking the x-axis
as the type of vehicle class and the y-axis as the number of vehicles of each class.
Fig. 6: Graph plotted from data collected by the sensor
3. CONCLUSION AND FUTURE PLAN
The experiments done suggest that two magnetic sensors achieve good vehicle estimates. Further, a tri-axis
magnetometer can classify classes of vehicles. The low-cost, ease of deployment and maintenance, and more
detail information provided by these sensor networks, can give us a base for an accurate, extensive, and dense
traffic system. The interesting thing about the wireless network is that the same communication and node
architecture can be used to process and communicate measurements from different sensors, hence these are
known as modular architecture.
Conducting the experiment with more no of sensors and estimating the traffic and classifying different classes of
vehicles and getting vehicle data from different sensors, storing the data received from different sensors and
using that data for real-time traffic management.
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