Recent advancements in electronics and wireless communications
enabled the manufacturing of cheap and small sensor nodes. A Wireless
Sensor Network (WSN) consists of numerous sensor motes which sense the
data, communicate with each other hop by hop and eventually report the
data to the Base Station. Though WSN started for military applications,
gradually it found to have very useful applications in wide range of
areas like Health, Building Monitoring and Factory Automation etc. 
Research in wireless communications is facilitating the development
of Inter-Vehicle Communication systems that will benefit mobility and
safety objectives. For example, an alert message about a traffic
accident or traffic jam can be propagated tens of miles along the road
to help drivers select a better route. Recently, these systems, referred
as Vehicular Ad-hoc Networks (VANET), are gaining significant prominence
from both government agencies and private organizations.  In the last
15 years Intelligent Transportation Systems (ITS) have been researched
and deployed in the US, Europe and Asia to alleviate congestion and
enhance the performance of traffic networks. With the rapid advances in
wireless communication technologies, vehicles in a transportation
network can seamlessly communicate to obtain information about network
conditions, thereby, assisting better decision making.  The data
collected from the sensors on the vehicles can be displayed to the
driver, sent to the Road Side Units (RSU) or even broadcasted to other
vehicles depending on its nature and importance. The RSUs distribute
this data, along with data from road sensors, weather centres, traffic
control centres, etc to the vehicles and also provides commercial
services such as parking space booking, Internet access and gasoline
payment. [4, 5]
VANET (Vehicular Ad Hoc Networks) is a kind of MANET (Mobile Ad Hoc
Networks) with some common characteristics, such as movement and
self-organization of nodes. However there are some differences in some
ways. MANET can contain many nodes that cannot recharge their power and
have un-controlled moving patterns . Although the power is not a
constraint in the vehicles, VANET has some challenges as: 1)
predictable, high mobility that can be exploited for system
optimization; 2) dynamic, rapidly changing topology (due to high
mobility); 3) constrained, largely one-dimensional movement due to
static roadway geometry; 4) potentially large-scale; 5) partitioned ;
6) Vehicles are not completely reliable. 
2. Related Work
In this section we deal with some of the traffic monitoring
techniques right from manual control by traffic policemen to advanced
systems which gain use of GPS Technology.
1) Traffic control by Police Men manually, e.g. there are some
police check posts in distances between cities where they monitor the
traffic flow by human eye or by using some equipment such as sonars.
This method doesn't give satisfactory results. Some of the problems
associated with this are as follows:
* It is difficult to monitor traffic along every road.
* Human errors and low accuracy in monitoring can be considered as
* They cannot continue this work 24 hours of 7 days of the week due
to bad weather or lack of light, etc.
2) Roadside Cameras and Sensors are used to monitor traffic,
collect data .The data is then sent to the police station. Though issues
with the previous method are resolved there are other issues with this
* High cost.
* Low reaction.
* Constant maintenance is required.
* Doesn't cover the road completely.
* Fault Tolerance.
3) Global Positioning System (GPS).
GPS was primarily designed for military applications only, but
after that the US government made it free for the other applications
too. The GPS consists of 24 satellites that were started by Defence
Ministry of the USA (with NavStar as its pseudonym). The first satellite
was sent in 1978 and the others started their work in 1994. Each
satellite can operate only for 10 years and it will be replaced before
its dead time. Satellite's speed is around 7000 m/h, the weight of
each satellite is almost 907 kg and when its wings are open it is around
8.18 meter long. Power consumption by each satellite is around 50 Watt
The GPS satellites send two short and burst signals as L1 and L2.
The personal GPS devices can receive L1 at the UHF band with the 1575.42
MHz frequency. These signals can pass the clouds, gas and plastics, but
not the obstacles as solids, building and mountains.
A GPS signal consists of three data bit:
1) An unreal-random Code: It is simple as an ID code, which is to
identify sender satellite.
2) Temporary Data (for a day): Location of each GPS satellite at
each time can be estimated based on this kind of data.
3) Annually Data: The most important data that each satellite sends
about its status.
Vehicles gain the advantages of GPS system in two ways: Offline
Mode and Online Mode. In both there is a device embedded on vehicles
which can receive the satellites signals and estimate the position of
the vehicle. In offline mode there is one MMC Card required for each
vehicle to save data whereas in the online mode a GMS is used to send
data to the station by the SMS format. The data stored in the MMC Card
can be retrieved via sophisticated software and in online via industrial
mobile hardware the data is readable in the station.
a) GPS's signals are under the effect of the following which
attenuate them :
1. Delay of Troposphere (the lowest portion of Atmosphere) and
Ionosphere: Satellites signals become weak when the pass the atmosphere.
2. Multiple Signals: It occurs when GPS signals before reaching the
receiver reflect by the buildings or rocks.
3. Receiver Periodical Errors: Surely receiver's time is not
working as proper as GPS satellites, therefore it is prone to high
errors about time meters.
4. Orbit Error: Temporary data might not report the exact location
of the satellite.
5. Obstacles: Some other satellites, buildings, trains, electronic
obstacles, crowded trees can prevent signals.
6. Satellites Geometry: Satellites geometry is pointed to the
proportional location of satellites. When the satellites are on the same
way or they are in the small groups, some geometry errors happen.
7. Satellite's signal intentional corruption: This was made by
Defence Organization to prevent using of robust signals of GPS
satellites by unauthorized people.
b) Hardware constraints:
1. The necessity of additional hardware as GPS receivers, MMC Card,
SIM CARD, ...
2. Less accuracy (up to 15 meters in positioning and 0.5 km/h for
3. Dependency on GPRS system in online mode.
4. Failure of MMC Card in the offline mode. Thus GPS cannot be a
3. Proposed Fixed Topology
In this section we see the topology that we propose for a network
where we route the data safely to the Base Station (BS) without the use
of GPS. We assume that there are two types of sensors in the network.
The first kinds of sensors are fixed sensors which are deployed on
predetermined distances on both sides of the road (Road Side Sensors).
The second type of sensors is the sensors that are attached to every car
and which communicate with the fixed roadside sensors (Vehicles'
Sensors). Road Side Sensors (RSS) act as Cluster Head (CH) to forward
collected data from Vehicles' Sensor (VS) to the Base Station (BS)
e.g. Police Station or Rescue Team Station.
As we are trying to save cost of the network, the number of RSS
nodes is an important factor which is depend on their communication
coverage range and distances from each other.
Suppose each RSS node can cover up to 500 m, as the Figure 2
depicted, each node can communicate with 3 other nodes. Therefore for
each one kilometer we need 7 road side sensor nodes.
4. Routing Algorithm for the Proposed Topology
In this algorithm we take that every vehicle will have a sensor
called Vehicle Sensor (VS) which has some predetermined attributes. We
have sensors called Road Side Sensors (RSS) on both sides of the road.
The sensor in the vehicle will constantly send a status message for
every fixed time interval. The status message contains the following
1) Vehicle ID.
2) Driver ID.
3) Speed of the vehicle.
4) Emergency status.
The Vehicle ID will contain the unique ID that is given to every
vehicle. Driver ID is the licence number of the owner of the vehicle
provided by the governing authorities. The sensor in the vehicle (VS)
senses the current speed of the car and records it in the status
message. When the vehicle meets with an accident, the emergency status
attribute becomes active. The status message sent by the vehicle will be
received by the RSS. When the emergency status is active or the speed of
the vehicle exceeded the base speed limit, the RSS forwards the packet
to the next RSS which is in its range of communication. As shown in
Figure 2, the RSS0 can send data to the RSS1 and waiting for an
acknowledgment, if it didn't received the acknowledgment packet in
an interval of time, then it sends the data packet to RSS2 and it fails
again, it will tries RSS3.
[FIGURE 1 OMITTED]
[FIGURE 2 OMITTED]
Meanwhile, the sender adds the information of the nodes which did
not responded to the data packet, as failure nodes, so that the
authorities investigate out of service nodes and replace them if
As a vehicle is equipped with its own battery whose capacity is
much greater than that of a wireless sensor node requires, there are no
energy restrictions on the sensor in the vehicle. But the RSS have no
such power source so we need to use their power efficiently. As we have
said, a primary concern of wireless sensor networks is power
consumption. It is desirable to place the network devices in a low-power
sleep mode as much as possible, to minimize average power consumption.
The protocol in which the network devices monitor the channel constantly
would be a poor choice for wireless sensor networks, since their
receivers would have to be constantly active and drawing current (Due to
their low transmitter output power, the receivers of many wireless
sensor network devices dissipate more power than their transmitters,
exacerbating this situation.). Any energy expended monitoring a silent
channel, or listening to a network device that does not have a message
to send, is wasted energy that could better be used for actual
So in our algorithm we keep some of our nodes in the network sleep
mode to save power. The decision whether to stay awake or in sleep mode
is decided by a probability which is decided by the following factors:
1) Remaining energy in the sensor.
2) Generated random number.
3) Previous sate of the node.
4) Importance of the message.
Remaining energy in the node will contribute in deciding the state
of the node. When the node has more remaining energy then it can have
high chances of being awake and participate in the transmission.
P (rem ) = 1 - (Remaining Energy / Total Energy) (1)
A random number is generated and if the random number is above a
threshold level then the node will have chances of being awake.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)
Previous state of the node also affects the present state of the
node if the previous state is active then the node tries to change its
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)
Importance of the message also decides the state of the node. If
the node receives an important message like emergency active attribute
messages then it have higher chances of being awake.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)
The total probability of a node to go to sleep mode is P = 0.25 x
[p(imp) + p(state) + p(rand) + p(rem)] (5)
5. Applications of Proposed Work
5.1. Velocity Monitoring
As the RSS will know its position, the speed limit in that
particular location will be decided by the authorities and fed in the
sensor. Every vehicle moving continuously sends status data packets and
whenever an RSS detects the velocity of a vehicle exceeding the velocity
limit, the data is being forwarded to the next node and eventually to
the Base Station. The data specifies the approximate location of the
vehicle, vehicle's ID and other information which is useful for the
5.2. Positioning Information
When there is a need to know the location of a vehicle --to find a
stolen vehicle--we send a request query giving the vehicle ID. This is
circulated throughout the network and when an RSS gets a data packet
from the matching vehicle ID then its position and related information
is sent to the Base Station.
5.3. Incident and Accident Reporting
As we have discussed in the routing protocol when a car remains
stationary for longer periods of time or when a car sends a panic
message, it is immediately routed to the Base Station--police station
and rescue team. Since the reported data contains all the important data
like vehicle ID and approximate position it is easy for the officials to
proceed forward accordingly.
6. Simulation Results
To simulate the topology in MATLAB, we generate random traffic at
different times all along the road. Then using these results we plot the
graph. The initial energy in the network is assumed to be 10,000 units.
Figure 3 shows the remaining energy in the network vs. the number
of events occurred. This plot is given for different transmission
probabilities. Figure 4 shows the
[FIGURE 3 OMITTED]
[FIGURE 4 OMITTED]
Packet loss vs. the number of events occurred. This is also plotted
for different transmission probabilities. So, we should make a
compromise for the energy consumption and avoid packet loss.
From the above given tables we see that if the transmission
probabilities are decreased then energy remaining in the network at any
time is increased. But at the same time we also need to consider the
packet loss occurring due to low transmission probability. Table 1 and
Table 2 show the values of Energy remaining in the network for first 100
events and 200 events respectively.
In this paper we discussed various aspects of communication in
Vehicular Networks. We also saw an optimal energy utilization algorithm
for vehicular networks. The proposed algorithm is free from GPS and it
doesn't require any costly topology to find the location
doi: 10.4236/wsn.2010.212110 Published Online December 2010
Received October 31, 2010; revised November 1, 2010; accepted
November 8, 2010
 I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci,
"A Survey on Sensor Networks," IEEE Communications Magazine,
Vol 40, No. 8, 2002, pp. 102-114.
 V. T. Arasan and R. Z. Koshy, "Methodology for Modeling
Highly Heterogeneous Traffic Flow," Journal of Trans-Portation
Engineering, Vol. 131, No. 7, 2005, pp. 544-551.
 A. Benslimane, "Localization in Vehicular Ad Hoc
Networks," 2005 Systems Communications (ICW05, ICHSN05, ICMCS05,
SENET05), Monteral, 14-17 August, 2005, pp.19-25.
 P. Papadimitratos, L. Buttyan, J.-P. Hubaux, F. Kargl, A. Kung
and M. Raya, "Architecture for Secure and Private Vehicular
Communications," Proceedings of the 7th International Conference on
ITS Telecommunications, Sophia Antipolis, 6-8 June 2007, pp. 1-6.
 H. Hartenstein and K. P. Laberteaux, "A Tutorial Survey on
Vehicular Ad Hoc Networks," IEEE Communications Magazine, Vol. 46,
No. 6, 2008, pp. 164-171.
 P. Mohapatra and S. Krishnamurthy, "Ad Hoc Networks:
Technologies and Protocols," Springer Science and Business Media,
Inc., USA, 2004.
 O. Dousse, P. Thiran and M. Hasler, "Connectivity in
Ad-Hoc and Hybrid Networks," IEEE Infocom 2002, New York, 23-27
 H. Wu, R. Fujimoto, R. Guensler and M. Hunter, "A
Mobility-Centric Data Dissemination Algorithm for Vehicular
Networks," VANET '04 Proceedings of the 1st ACM International
Workshop on Vehicular Ad Hoc Networks, October 2004, pp. 47-56.
 P. L. N. Raju, "Fundamentals of GPS," Satellite
Remote Sensing and GIS Application in Agricultural Meteorology,
Proceedings of a Training Workshop, Dehra Dun, 7-11 July 2003, pp.
 Garmin International Ltd. Company website:
Mohammad Jalil Piran (1), Garimella Rama Murthy (2)
(1) Departement of Computer Science and Engineering, Master by
Research Information Technology, Jawaharlal Nehru Technological
University, Hyderabad, India
(2) Communication Research Centre, International Institute of
Information Technology, Hyderabad, India E-mail: email@example.com,
Table 1. Value of energy remaining for the first 100 events.
Probability of transmission Remaining Energy Packet
/ parameter in the network loss
100% 5200 0
80% 8080 0
60% 8560 4
40% 8500 4
20% 9000 6
Table 2. Value of energy remaining for the first 200 events.
Probability of transmission Remaining energy Packet
/ parameter in the network loss
100% 400 0
80% 1540 5
60% 2800 4
40% 5680 9
20% 6160 15