Survey of mirp for vehicular ad hoc networks in urban environments

International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
Volume 1 Issue 2, November 2012
www.ijsr.net
Survey of MIRP for Vehicular Ad-Hoc Networks
in Urban Environments
Kavya1
, Dr. Basavaraj Patil S2
, Panduranga Rao M.V 3
1
Dept of Computer Science and Engineering
BTL Institute of Technology
Bangalore, India
kavya1kolar@gmail.com
2
Bangalore, India
csehodbtlit@gmail.com
3
Bangalore, India
raomvp@yahoo.com
Abstract: Vehicular communication is attracting growing attention from both academia and industry, owing to the amount and
importance of the related applications, ranging from road safety to traffic control and up to mobile entertainment. The vehicular ad-hoc
network is an emerging new technology. Many VANET routing protocols use a carry-and-forward mechanism to deal with the
challenge. However, this mechanism introduces large packet delay, which might be unacceptable for some applications. In this proposed
system, we first analyze the unique feature of urban VANET that vehicles have different types, and move like clusters due to the
influence of traffic lights. So the concept of using buses as the mobile infrastructure to improve the network connectivity is proposed.
We also develop a novel routing protocol named as MIRP (Mobile Infrastructure Routing Protocol) which makes full use of buses,
making them a key component in route selecting and packet forwarding.
Keywords: MIRP, VanetMobiSim, MANET, VANET, AODV, DSR
1. Introduction
Vehicular Ad-hoc Networks (VANETs) represent a
rapidly emerging, particularly challenging class of Mobile
Ad Hoc Networks (MANETs), which supports data
communications among nearby vehicles and between
vehicles and nearby fixed infrastructure, and generally
represented as roadside entities.
Figure 1.1 Vehicular Ad-hoc Networks
As shown in Figure 1.1, when multi-hop communication is
implemented, VANET enables a vehicle to communicate
with other vehicles which are out of sight or even out of
radio transmission range.
Some VANET routing protocols use a carry-and-forward
mechanism.. But, this mechanism introduces a large packet
delay, which might not preferable for a lot of applications.
As the frequent network disconnection is caused by the
characteristics of VANET, we first analyse the features of
urban VANET:
1. Vehicle movements are constrained in roads.
2. Traffic lights have great influence on the vehicle
movement that vehicles are moving like clusters.
3. Vehicles have at least two different types which are
ordinary cars as well as buses as analysed in RBM
[10].
Then we propose to improve the connectivity of the
network by taking advantage of buses which have fixed
travel lines and can carry better equipment’s with a larger
transmission range. Based on this idea, we develop a
VANET Routing Protocol based on mobile infrastructure,
which we believe suitable for urban VANETs. This protocol
estimates the density of each road segment based on the bus
line information for road segment selection, and prefers
buses to ordinary nodes as the forwarding node. Our
contribution is folded as two:
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• The concept of using buses as the mobile infrastructure to
improve the network connectivity is proposed by the
analysis of the unique features of urban VANET.
• VANET Routing Protocol based on mobile infrastructure
is developed. Both the transmission quality of each road
segment and different transmission abilities of various
vehicles are considered in the algorithm.
The reminder of this paper is organized as follows: In
Section II, the literature survey is presented. In Section III,
we analyse the features of urban VANET, and propose the
strategy of improving the network connectivity with the help
of buses. The design of MIRP is described in section IV.
Section V presents simulation settings and results. Finally,
conclusions and future works are summarized in section VI.
2. Literature Survey
The routing protocols in VANET are categorized into six
types. Topology based, Position based, Geocast based,
Cluster based, Broadcast based and Infrastructure based.
Topology based routing protocols discover the route and
maintain it in a table before the sender starts transmitting
data. They are further divided into Proactive and Reactive
protocols. These are of two types: Proactive protocols and
Reactive protocols
Position based routing protocols these protocols use
geographic positioning information to select the next
forwarding hops so no global route between source and
destination needs to be created and maintained. In Greedy
Perimeter Stateless Routing (GPSR), each node periodically
broadcasts a beacon message to all its neighbours containing
its id and position.
Table 2.1: Comparison of Various Protocols
Parameters
Protocols
Forwarding Routing Recovery Control
Packet
FSR Multi hop Proactive Multi hop High
OLSR Multi hop Proactive Multi hop High
AODV Multi hop Reactive Store and
Forward
Low
DSR Multi hop Reactive Store and
Forward
Low
GPSR Greedy
Forwarding
Reactive Store and
Forward
Moderate
GyTAR Greedy
Forwarding
Reactive Store and
Forward
Moderate
SADV Store and
Forward
Reactive Multi hop Low
RAR Store and
Forward
Reactive Multi hop Low
Recently, some other routing protocols for VANETs have
been proposed. The Geographical Source Routing [7]
protocol combines position-based routing with topological
knowledge. GyTAR [4] is another protocol, in which the real
time road traffic variation is taken into account. Traditional
wireless ad-hoc networks routing protocols, such as DSR [5]
and AODV [9], are not suitable for VANET.
There are also several VANET routing protocols based on
infrastructure or road side unit (RSU). SADV [2] utilizes
some static nodes at road junctions. With the assistance of
static nodes at junctions, a packet can be stored in the node
for a while and wait until there are vehicles within
communication range along the best delivery path. RAR [8]
is a vehicular hybrid network routing protocol in which
roads are divided into sectors by RSUs, and the route
consists of vehicles and RSUs. The drawback of these
protocols is the requirement and distribution of static node or
RSU.
To deal with the rapidly changing network topology, a new
routing technique based on location information has been
developed. One famous strategy is GPSR [6]. GPSR selects
the node that is the closest to the destination among the
neighboring nodes. To evaluate routing protocols for
VANETs by simulation, various traffic mobility models have
been studied. VanetMobiSim [3] is a well-known and
validated traffic generator, which is developed by Eurecom.
We use this traffic generator in our simulation studies.
3. Features of Urban VANET Analysis
The unique features of urban vanets are: Firstly, vehicle
movements are constrained by roads. Secondly, in urban
VANETs, traffic lights have great influence on the vehicle
movement. Another important feature of urban VANETs is
that vehicles have at least two different types which are
ordinary cars and buses. All these features should be
considered in the routing protocol design.
As vehicle movements are constrained by roads, the simple
greedy forwarding strategy without taking urban
environment characteristics into account in MANET such as
GPSR is not applicable for VANET. Figure 3.1 shows an
example. Assume vehicle S wants to send a packet to D, and
S has two neighbors: A and B. GPSR will choose B to
forward the packet because B is closer to D. But in common
sense, we should choose A as vehicle movements are
constrained in roads. The routing in VANET should be a
sequence of road segments in macros copy, and the decision
to choose which road segment near the junction for
forwarding is critical.
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Figure 3.1: Example of selecting node closest to destination
may not good.
In urban VANET, traffic lights have great influence on the
vehicle movement. Red traffic light at junction stops
vehicles from approaching which is an important factor to
network disconnection. When the traffic light turns green,
stopped vehicles will continue to move and those moving in
the same direction will be close to each other like a cluster.
Suppose the road segment length is L, the period of red
traffic light is T, and the average velocity is V , then the
expectation distance between two clusters is min(T*V;L).
As mentioned above, in urban VANETs, there are at least
two types of vehicles which are ordinary cars and buses. The
number of buses is much less than ordinary cars under
normal conditions. For example, according to Helsinki
Traffic Management Bureau [1], about 80 percent of all
motor vehicles in Helsinki are ordinary cars, and buses only
compose 20 percent. In addition, the buses are larger and
more powerful, so they can carry better wireless equipment
with a larger transmission range than ordinary car hoping to
improve the connectivity of the network by increasing the
transmission range between buses.
Figure 3.2: Uniformly distributed vehicles
Assume that vehicles are uniformly distributed on the road
as figure 3.2. If the average distance between two vehicles is
X, so the average distance between two buses will be
X/20%, because buses only compose 20 percent of all motor
vehicles. To improve the connectivity of the network,
transmission range required between buses should be over
five times those of other vehicles. Since signal reception is
difficult if vehicles are not on the same road due to radio
obstacles such as high-rise buildings, packet transmission is
constrained in one road, or adjacent streets when packet is
near a junction. So even if the buses have a five times
transmission range, they still can hardly communicate with
each other when the distance between them is close to that
maximum transmission range because there are obstacles.
The moving vehicles are affected by traffic lights. When the
light turns green, those moving in same direction will be
close to each other like clusters as shown in figure 3.3.
Figure 3.3: Vehicles move like clusters
Vehicles are close together to each other in one cluster and
the distance between two clusters may be a little longer.
Ordinary cars in different clusters may not be able to
communicate, but buses can as they have larger transmission
range. Additionally, buses are dispatched periodically, so the
distribution of buses in the network is relatively dispersive.
In this case, without having a five times transmission range,
connectivity can be improved if the communication range
between buses is wider than the distance between adjacent
clusters, which is min(T*V;L). This means 2-3 times
transmission ranges between buses may produce a much
better connectivity.
The road segments in the real area showed in figure 3.4 have
0 to 12 bus lines and 7 lines on average. And the departure
time interval of each bus line is about 15 minutes.
Figure 3.4: A real map from Helsinki
As there are two directions in each road, the average time
between two adjacent buses is 15/7*2. Moreover, bus takes a
large proportion of time on bus stop and traffic lights, and
has a lower speed than ordinary car. Though the average
distance between buses is hard to estimate, but it is not very
long that buses can form a mobile backbone.
To improve the connectivity, assume two wireless interfaces
on each bus, which works on different channels, while
ordinary cars have only one interface. The transmission
range between cars and between a car and a bus is R1 on
channel one, and that between buses is R2 (R2 > R1) on a
different channel. So buses constitute a mobile backbone to
enhance the multi-hop communication. Now, a new routing
protocol is proposed. This protocol makes full use of the
buses, making them a key component in route selection and
packet forwarding.
4. Design of MIRP
MIRP is a based on location reactive routing protocol. Here
we assume that each vehicle knows its location through
GPS, and has a digital street map including bus line
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information. We also provide the availability of a location
service, so the source node can get the destination
information. In addition, each bus has two wireless
interfaces working on different channels, while ordinary car
has only one interface.
The transmission range between cars and between a car and
a bus is R1 on channel one, and the transmission range
between buses is R2 (R2 > R1) on a different channel.
The MIRP protocol consists of two essential parts:
 Selecting an optimal route which consists of a sequence of
road segments with the best estimated transmission
quality.
 Efficiently forwarding packets hop-by-hop through each
road segment in the selected route.
4.1 Routing
In the road segments are chosen one by one, considering the
transmission quality of each road segment: when selecting
the next forwarding road segment, a node (the sending
vehicle or an intermediate vehicle near a junction) checks the
route table and chooses the best neighbouring road segment
with a min estimated hop count to the destination. To select
an optimal route with the min estimated hop count, we have
first to estimate the hop count of each road segment.
Generally speaking, the more buses on the road the more
vehicles on it, because the road with many buses is often a
prosperous area. So we can estimate the hop count of each
road segment by the expectation bus density and road length.
For convenient, we define the following notations:
• R1: the transmission range of ordinary cars and buses on
channel one.
• R2: the transmission range of buses on channel two.
• Xi: the total route length of bus i
• Lj : the length of road segment j
• Nj : the expected number of buses on road segment j
• Cj : the estimated hop count on for road segment j
Suppose the route length of bus i is Xi, and the route of bus i
contains road segment j. So the probability that bus i is on
road segment j is Lj/Xi .Although the bus movement is
affected by many other factors such as bus stops and traffic
lights, we can estimate the probability by Lj/Xi because the
longer a road segment is the more bus stops and traffic lights
there will be. Then the expected number of buses Nj on road
segment j is:
As every bus departs periodically, they will scattered
distribute in the network, the average distance between buses
on road segment j is Lj/Nj.
Suppose when the density of buses on the road segment is
high enough to reach an average distance D (a constant
number, which we choose D = R2/2 in our simulation)
between buses, packet forwarding on road segment j can be
completed by buses and without any help of ordinary cars.
The hop count Cj on this street can be estimated by:
Otherwise, buses and ordinary cars are both needed for
packet forwarding. As the network connectivity is strongly
and positively related to road segment density, we can
estimate the hop count on road segment j by the formula:
Where Nj/Lj is the density of road segment j, 1/D is the road
density if the average distance between buses is D, so
Lj/D*Nj is the density proportionate to reach an average
distance D between buses. This formula is not absolutely
accurate, but it is simple and can be used as an estimation of
the hop count. A large hop count number will appear when
the road segment is not connected. Routing algorithm prefers
not to choose them for the optimal route. Now that we know
the expected hop count for each road segment, the total hop
count for a certain route can be calculated. Afterwards, the
Dijkstra algorithm can be used to select a shortest route with
the minimal expected hop count. Two implementations can
be used. One is that the best route consisting of a sequence
of road segments is computed by the source and put into the
packet header. The disadvantage of this method is the
increase of packet size and network bandwidth cost. The
other is calculating the route of each junction pairs at the
network beginning. The next road segment and estimated
hop count are both recorded in a route table. And the next
road segment is chosen when packet is near a junction. This
scheme requires less bandwidth. Our protocol uses the
second implementation.
Figure 4.1: Route Selection
Figure 4.1 shows an example of how the next road segment
is selected. Once vehicle A which is near a junction receives
a packet, it checks the route table. Considering the distance
and expected number of buses, the road segment with
shadow will be chosen as the next road segment.
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4.2 Forwarding
Once the next road segment is determined, the “bus first”
strategy introduced below is used to forward packets on the
road segment. Each vehicle periodically sends beacon
packets which contain its location and vehicle type (bus or
not). They also maintain a table of its neighbours’
information and select one neighbour for packet forwarding.
Traditional algorithms select the node which is closest to the
destination or closest to the next junction to be the next hop.
As the transmission range between buses is larger, prefer
choosing a bus to be the next hop showed in figure 4.2. But
this does not mean that buses always have higher priorities
than ordinary cars, there still have some other factors to be
traded off.
Figure 4.2: Buses have higher priorities
Traditional algorithms select the node which is closest to the
destination or closest to the next junction to be the next hop.
As the transmission range between buses is larger, prefer
choosing a bus to be the next hop showed in figure 4.2. But
this does not mean that buses always have higher priorities
than ordinary cars, there still have some other factors to be
traded off.
Firstly, as the route is consisted by a sequence of road
segments, the decision near the junction is critical. We’d
better send the packet to the node on the next road segment
when it is near the junction. That means the nodes on the
next road segment always have higher priorities than the
nodes on the same road.
Figure 4.3: Buses don’t have higher priorities in some
situation
Secondly, let’s consider the situation showed in figure 4.3.
Assume that bus1 currently keeps the packet, and it has two
neighbours (bus2 and car1) which are both closer to the next
junction. The distance between bus1 and bus2 is d which is
very small, and the distance between bus1 and car1 is much
larger. As, bus1 knows that there is no other bus except bus2
on the next R2 meters of road segment, selecting bus2 as the
next hop gains benefit only if there are any buses in the
shadow area of figure 7. This probability is rather low, as the
length of the shadow is about d which is very small. In this
situation, choose car1 as the next hop is much better. Similar
situation is not considered for ordinary cars, because cars
only know that there is no other bus on the next R1 meters of
road segment. So the forwarding neighbour is selected
according to the strategy below which we called “bus first”:
• If the neighbour table contains any buses on the next road
segment, choose the one which is closest to the junction
after the next junction. Otherwise choose an ordinary
car which is closest to the junction after next.
• If the neighbour table contains no vehicles on the next
road segment, and packet is now on a bus: choose a bus
which is closest to the next junction and the distance
between them must be larger than d (a constant number
much smaller than both R1 and R2), else choose a
vehicle which is closest to the next junction.
• If the neighbour table contains no vehicles on the next
road segment, and packet is now on a car: choose a bus
which is closest to the next junction. If not available, we
should choose a vehicle which is closest to the next
junction.
• If there are no better suitable forwarding nodes, drop the
packet.
5. Simulation Results
5.1 Simulation Model
In our experiments we use version 2.33 of the ns-2
simulator. The simulated area is based on a real map from
Southern Beijing with a 1700m*1000m size.
Table 1: Simulation Parameters
Parameter Value
R₁ 150m
R₂ 300m
Bandwidth for both
channel
2Mb
Beacon interval 1.0
Vehicle Velocity 0-30m/s
Number of nodes 100-250
The bus percent 20%
TTL 32
Packet Size 512bytes
CBR data rate 128bytes/s
The vehicle movement trace is generated by VanetMobiSim,
which is a well known and validated traffic generator .Other
simulation parameters are listed in TABLE 1. We simulated
the MIRP protocol and compared its delivery ratio and
throughput with GPSR. Additionally, we evaluated two
variants of MIRP called one-Channel and two-Channel
respectively. In one-Channel algorithm, the buses have only
one wireless interface with the transmission range R1 same
as ordinary cars, but the route selection is the same as MIRP.
44

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In two-Channel algorithm, buses have two wireless
interfaces, and route selection the same to MIRP too. In both
one-Channel and two-Channel, the strategy of next hop
selection on a certain road segment is based on greedy
forwarding rather than “bus first”.
5.2 Simulation Result
As shown in figure 5.1, MIRP achieves the highest packet
delivery ratio. GPSR incurs a highest data loss rate, because
simple greedy forwarding strategy without taking urban
environment characteristics into account is not suitable for
VANET. After considering some urban environment
characteristics, the performance of one-Channel algorithm is
much better than GPSR. Two-Channel algorithm performs
better than one-Channel for the following reason: as vehicles
move like clusters, increasing the transmission range of a
small percent of vehicles can notably improve the network
connectivity. MIRP achieves the highest packet delivery
ratio, because the difference of bus and ordinary car is taken
into account and buses are given higher priority to become
the next hop in some situation.
Figure 5.1: The data delivery ratio in different network
density
Figure 5.2: Throughput of networks with 200 nodes
To compare the throughput, we randomly select 50
communication pairs. Each source node generates CBR
traffic for a period of 600 seconds with the sending rate from
5kb/s to 30kb/s. As shown in figure 5.2, the throughput of
MIRP outperforms all the other protocols. The throughput of
two- Channel algorithm is about twice that of one-Channel,
because the buses have another channel for transmission.
The performance of MIRP is much better, because channel 2
is less congested as buses only compose 20 percent of all
vehicles, and MIRP prefer choosing a bus as the next hop. In
addition, the packet control overhead of MIRP is small,
because only beacon packets are used as control packet.
6. Conclusion
In this work, we have analyzed the unique features of urban
VANETs and proposed the idea of improving network
connectivity by increasing the transmission range of buses.
Then we presented our new routing protocol MIRP which
takes advantage of the buses as a mobile backbone. The
proposed protocol is a geographical routing using the map
topology and the bus line information to facilitate route
selection. In addition, the “bus first” forwarding strategy is
used because we assume that the transmission range between
buses is larger. Additionally, the algorithmic complexity of
MIRP is low, and the deployment is easy because no static
nodes or RSU are needed in MIRP.
As the future work, we will incorporate more realistic factors
into our routing protocol, such as the velocity, direction and
even the history information of vehicle traffic.
References
[1] http://www.bjjtgl.gov.cn.
[2] Y. Ding, C.Wang, and L. Xiao. A static-node assisted
adaptive routing protocol in vehicular networks.
VANET’07: Proceedings of the fourth ACM international
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September 2007.
[3] J. Harri, F. Filali, C. Bonnet, and M. Fiore.
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[4] M. Jerbi, S.-M. Senouci, R. Meraihi, and Y. Ghamri-
Doudane. An improved vehicular ad hoc routing protocol for
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[5] D. B. Johnson and D. A. M. Y.-C. Hu. The dynamic
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[7] C. Lochert, H. Hartenstein, J. Tian, H. F¨ußler, D.
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[8] Y. Peng, Z. Abichar, and J. M. Chang. Roadside-aided
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[9] C. E. Perkins and E. M. Royer. Ad hoc on-demand
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Kavya received her B.E. degree in Computer Science and
Engineering from Reva Institute of Engineering and
Technology, Bangalore. At present she is pursuing the
Master of Technology in Computer Science and Engineering
Department at BTL institute of Technology, Bangalore.
Dr. S Basavaraj Patil is the Founder & Principal
Consultant; Predictive Research He received the PhD in,
Computer Science & Engineering from Kuvempu
Vishwavidyanilaya and M.Tech, Bio-Medical
Instrumentation from S J College of Engineering, Mysore.
He worked as Assistant Vice President at CIBM Research,
and HSBC Consultant at Manthan Systems and Technical
Architect at Aris Global. He is presently working as head of
the department at BTL institute of technology.
Panduranga Rao M.V is a Researcher at NITK, India. He
received the M.Tech degree in computer Science from
Visvesvaraya Technological University and B.E. degree in
Electronics and Communication from Kuvempu University,
Karnataka. He was Research Associate at JNNCE, during
the period from August 1989 – April 2005. He received an
award at Okinawa, Japan. . He is presently working as
professor at BTL institute of technology.
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Survey of mirp for vehicular ad hoc networks in urban environments

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