Impact of mobility and maps size on the performances of vanets in urban area

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME
556
IMPACT OF MOBILITY AND MAPS SIZE ON THE PERFORMANCES
OF VANETS IN URBAN AREA
A. Rhattoy1
and A. Zatni2
1
Department of Computer, Modeling Systems and Telecommunications Research
Group/MoulayIsmailUniversity, Higher School of Technology,
B.P. 3103, 50000, Toulal, Meknes, Morocco
2
Department of Computer, MSTI Laboratory/ Ibnou Zohr University, Higher School of
Technology, B. P. 33/S, 80000, Agadir, Morocco
ABSTRACT
Vehicular Ad hoc Networks (VANETs) represent a rapidly emerging research field,
being a particularly challenging class of Mobile Ad Hoc Networks [1], used for
communication and cooperative driving between cars on the road. There are strong
economical interests in this field since vehicle-to-vehicle communication allows to improve
traffic safety, to improve route planning, or to control traffic congestion.The 802.11p is a
draft amendment to the IEEE 802.11 standard for vehicular communications. It has been
adopted by Wireless Access in Vehicular Environments, which defines an architecture to
support Intelligent Transportation Systems (ITS).
For this purpose, we first examine and then display the simulation findings of the
impact of different radio propagation models on the performance of vehicular ad hoc
networks. We have compared the performances of two routing protocols (AODV and OLSR)
for three propagation model (two-Ray ground, Rice and Nakagami). We study those
protocols under varying metrics such as mobility of vehicle and size of the scenario areas.
Our objective is to provide a qualitative assessment of the protocols applicability in different
vehicular scenarios. These two routing protocols are simulated and compared with Network
Simulator-2 under Manhattan Grid Mobility Model.
Keywords: Propagation model, Routing protocols, OLSR, AODV, VANET.
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING
& TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 2, March – April (2013), pp. 556-568
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2013): 6.1302 (Calculated by GISI)
www.jifactor.com
IJCET
© I A E M E

557
1. INTRODUCTION
The development of VANETs is backed by strong economical interests since vehicle-
to-vehicle (V2V) communication allows to share the wireless channel for mobile
applications, to improve route planning, to control traffic congestion, or to improve traffic
safety. Besides, the vehicular communication radio depends on several parameters such as the
emission power, the environment where the waves spread and the utilized frequency also play
a crucial role. The radio propagation waves are controlled by strict rules, mainly when there
are obstacles between the transmitter and the receiver [2], [3]. Among the changes a wave
may undergo, we can cite: reflection, diffraction, diffusion and absorption. This study is
organized as follows. We give three radio propagation models types. Then we discuss of
routing protocols concepts in vehicular ad hoc networks. In addition, we declare the
methodologies of simulation. Finally, we investigate the impact of radio propagation models
on the performances of routing protocols in VANETs and we present our conclusions.
Fig. 1. Model of urban displacement
2. RADIO PROPAGATION MODELS
In a propagation model, we use a set of mathematical models which are supposed to
provide an increasing precision. Propagation radio models are three types: path loss,
shadowing and fading [4]. The first type can be expressed as the power loss during the signal
propagation in the free space. The second type is characterized by fixed obstacles on the path
of the radio signal propagation. The third category is the fading which is composed of
multiple propagation distances, the fast movements of transmitters and receivers units and
finally the reflectors. In this work, we study three propagation models: Two-Ray Ground,
Rice and Nakagami.
2.1 Two-ray ground model
A single line-of-sight path between two mobile nodes is seldom the only means of
propagation. The two-ray ground reflection model considers both the direct path and a ground
reflection path [5]. This model gives more accurate prediction at a long distance than the free
space model. The received power is represented by Eq. 1:

558
2 2
t t r t r
r 4
PG G h h
P (d)
d L
=
(1)
Where, ht and hr are the heights of the transmitter and receiver respectively.
Nonetheless, for short distances, the two-ray model does not give accurate results because of
in oscillation caused by the constructive and destructive combination of the two rays. The
propagation model in the free space is instead, still used where d is small. Hence, in this
model, we calculate dc as a cross-over distance. When d<dc, we use the free space equation,
but when d > dc, the equation (1) is used. Consequently, dc can be calculated as Eq. 2:
t r
c
4 h h
d
π
=
λ
(2)
2.2 Rice model
This fading model depicts the rapid fluctuations of the received signal due to
multipath fading. This fading phenomenon is generated by the interference of at least two
types of transmitted signals to the receiver with slight time intervals [6]. The outcome may
vary according to fluctuations and to different phases in terms of multiple factors such as:
delay between waves, the intensity and the signal band width. Hence, the system performance
may be attenuated by the fading. However, there are several techniques that help stopping
this fading. The signal fading were monitored according to a statistical law wherein the most
frequently used distribution is Raleigh’s [7]. The transmitted signal is, thus, conditioned by
the following phenomena: reflection, scattering and diffusion. Thanks to these three
phenomena, the transmitted power may reach the hidden areas despite the lack of direct
visibility (NLOS) between the transmitter and receiver. Consequently, the amount of the
received signal has a density of Rayleigh Eq. 3:
( )
2
2x x
exp( ), pour 0 x
f x P P
0 , pour x 0

− ≤ ≤ ∞
= 
 <
(3).
Where, P is the average received power. In case where there is a direct path (LOS)
between the transmitter and receiver, the signal no longer obeys to Rayleigh's law but to
Rice’s. The probability density of Rice is represented by Eq. 4:
( ) 2
0
K 1 x2x(K 1)
exp K I
P P
K(K 1)
f(x) 2x , pour 0 x
P
0 , pour x 0
  ++
− −   
  

 +
= ≤ ≤∞  
 
 <



(4)
Where:K, the ratio of the power received in the direct line and in the path
P, the average power received
I0 (x), the zero-order Bessel function de fined by Eq. 5:

559
2
0 0
1
I (x) exp( x cos )d
2
π
= − θ θ
π ∫ (5)
The density of Rice is reduced to the density of Rayleigh in the case of an absence of
a direct path which means that K = 0 and thus I0 (x) =1.
2.3 Nakagami model
This distribution encompasses several other distributions as particular cases. To
describe Rayleigh distribution, we assumed that the transmitted signals are similar and their
phases are approximate. Nakagami model is more realistic in that it allows similarly to the
signals to be approximate. Since we have used the same labels as in Rayleigh and Rice cases,
we have ∑= ij
ierr θ
. The probability density of Nakagami related to r is represented by Eq. 6:
( )
( )
m 2m 1 2
r m
2m r mr
P r exp , r 0
m
−
 
= − ≥ 
Γ Ω Ω 
(6)
Where, Γ(m) is gamma function, Ω = (r2
) and m = {E (r2
)}2
/var (r2
) with the
constraint m≥1/2. Nakagami model is a general distribution of fading which is reduced to
Rayleigh’s distribution for m = 1 and to unilateral Gaussian model for m = 1/2. Besides, it
represents pretty much rice model and it is closer to certain conditions in the lognormal
distribution.
3. AD HOC ROUTING PROTOCOLS
Vehicular Ad-hoc Networks (VANETs) are characterized by a very high node
mobility and limited degrees of freedom in the mobility patterns. Hence, ad hoc routing
protocols must adapt continuously to these unreliable conditions, whence the growing effort
in the development of communication protocols which are specific to vehicular networks.One
of the critical aspects when evaluating routing protocols for VANETs is the employment of
mobility models that reflect as closely as possible the real behavior of vehicular traffic. In this
paper, we compare the performance of two prominent routing protocols AODV and OLSR in
urban traffic environment.Ad hoc routing protocols are based on fundamental principles of
routing such as: Inundation (flooding), the distance Vector, the routing to the source and the
state of the site. According to the way routes are created and maintained during the data
delivery [8]. Here is a summary of the routing protocols assessed in this study.
3.1 Ad-hoc On-Demand Distance Vector protocol (AODV)
AODV has a way for route request close to that of DSR. However, AODV does not
perform a routing to the source. Every single node on the path refers to a point towards its
neighbour from which it receives a reply. When a transit node needs broadcasts a route
request to a neighbour, it also stores the node identifier in the routing table from which the
first reply is received. To check the links state, AODV uses control messages (Hello) between
direct neighbours. Besides, AODV utilizes a sequence number to avoid a round trip and to
ensure using the most recent routes [9].

560
3.2 Optimized Link State Routing Protocol (OLSR)
OLSR [10], [11] is proactive routing protocol or table driven protocol. Initially nodes
have routing tables and they update their routing tables time to time. It is based on the link-
state algorithm. Each node maintains the topology information of network and sending this
information from time to time to neighbors. The uniqueness of OLSR is that it minimizes the
size of control messages and rebroadcasting by using the MRP (Multipoint Relaying). The
basic concept of MPR is to reduce the loops of retransmissions of the packets. Only MPR
nodes broadcast route packets. The nodes within the network maintain a list of MPR nodes.
MPR nodes are selected within the environs of the source node. The selection of MPR is
done by the neighbor nodes in the network, with the help of HELLO messages.
4. METHODOLOGY
In this study, on one hand we study the impact of different propagation models in
order to analyze the environment effect on the VANETs' performance. On the other hand, we
compare two routing protocols performances (AODV and OLSR) according to three
propagation models. The assessment is twofold: First, we diversified the nodes’ speed.
Second, we altered the size of the scenario areas. The propagation models under study are:
the two-Ray ground, Rice’s and Nakagami’s models. The simulation span is of 200 sec. The
data packet size is 512 octets.Since the Random Waypoint Model is considered unrealistic
[12] and [13], a mobility model clearly affects the simulation results. This mobility model do
not consider vehicles’ specific patterns, they cannot be applied to simulation of vehicular
networks in urban Area. Accordingly, we have chosen Manhattan Grid Mobility Model [14],
this Model is similar to City Section Mobility Model, and he uses a grid road topology, as
shown Figure. 1. This model is implemented in the BonnMotion framework [15]. This model
adds traffic density like in a real town, where traffic is not uniformly distributed; so, there are
zones with a higher vehicle density. These zones are usually in the downtown, and vehicles
must move more slowly. The evaluation is done in two scenarios, in the first scenario we
have varied the nodes speed and in a second we have varied the size of the scenario areas.
4.1 Scenario 1
So as to analyze the routing protocols’ behaviour, we selected traffic sources with a
constant output (CBR) related to UDP protocol. The packet emission rate is settled at 8
packets per second with a maximal speed variation of nodes. Ten speed values were
considered: 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 m/sec. The assessed protocols are: AODV and
OLSR. These two are available in 2.34 of ns-2. At the moment, we limit the number of
sources in 10 and we analyze the impact of the nodes’ speed.
4.2 Scenario 2
In this section we show the simulation results when we varying the size of the area,
maintaining unaltered the number of nodes and the rest of parameters. We selected scenario
areas of 1400*700m, 1600*800m, 1800*900m, 2000*1000m and 2200*1100m. The number
of nodes is set to 40 vehicles. Let’s limit the nodes’ maximal speed at 10 m/s while the other
parameters are similar to those in the first case.

561
4.3 Performance indicators
Because of the length chosen in this study, we have selected just three performance
indicators in order to study the routing protocols performances. They are outlined as follows:
Packet delivery fraction, end average to end delay and the throughput.
a. Packet Delivery Fraction (PDF)
This is the ratio of total number of CBR packets successfully received by the
destination nodes to the number of CBR packets sent by the source nodes throughout the
simulation:
n
recv
10
0 n
sent
1
CBR
Pkt _ Delivery 100
CBR
= ×
∑
∑
This estimation gives us an idea of how successful the protocol is in delivering
packets to the application layer. A high value of PDF indicates that most of the packets are
being delivered to the higher layers and it is a good indicator of the protocol performance.
b. Average End-To-End Delay (AE2E Delay)
This is defined as the average delay in transmission of a packet between two nodes
and is calculated as follows:
( )
n
sent _ Time recv _ Time
1
n
recv
1
CBR CBR
Avg_ End _ to_End_delay
CBR
−
=
∑
∑
c. Throughput
The throughput data reflects the effective network capacity. It is computed by
dividing the message size with the time it took to arrive at its destination. It is measured
considering the hops performed by each packet.
5. RESULTS AND DISCUSSION
In this part, we display the study findings about the impact of the nodes’ maximal
speed and the size of the scenario areas, on the routing protocols; according to the three
aforementioned performance indicators: packets Delivery fraction, Throughput and average
end to end delay.
5.1 Scenario 1
The results corresponding to the PDF, AE2E Delay and throughput are shown in
figure 2-4 respectively.
5.1.1 Packet delivery fraction
In figure 2, we notice the packet delivery fraction decrease according to the speed
increase. Consequently, the links are weaker with speed; the main reason for the packet loss
is mobility, congestion and the wireless channel characteristics.

562
Fig. 2: (a) AODV- PDF versus Speed
Fig. 2: (b) OLSR- PDF versus Speed
Meanwhile, we notice that the two-ray ground deliver more packets than Rice and
Nakagami, the bad performance of these two last models is due to the low intensity of the
signal caused by the obstacles. This results in the packet loss on weak links, displays wrongly
the links disconnection and leads to the interruption and thus the dire need to set up a new
itinerary.The Rice and Nakagami Models are most appropriate to simulate urban scenarios.
OLSR present the bad delivery rate of data packets, OLSR uses wrong routes to send data.
5.1.2 Average end-to-end delay
Similarly to PDF, we notice that the two-ray ground endure less delay than the two
other models. The nodes’ mobility has an influence on every metric; in other words, it
influences mainly the end-to-end delay.

563
Fig. 3: (a) AODV-AE2E Delay versus Speed
Fig. 3: (b) OLSR-AE2E Delay versus Speed
The AODV protocol has an end-to-end delay considerably higher than OLSR. Hence,
the transmitted data packets will be deleted once they reach their broken links. In addition,
the data packets undergo extra delays during the communication interfaces’ waiting because
of the frequent retransmissions. This latency causes the packets death (their deletion).
5.1.3 Throughput
As we expected, the throughput decreases slightly when the speed increases because it
has to find the path for more routing traffic delivery. Therefore, the channel will be less used
for the data transfer to as to reduce the useful throughput. We notice that the Two-Ray Grand
model is more efficient than Rice and Nakagami models; the bad performance of these two
last models is due to the low intensity of the signal caused by the obstacles.

564
Fig. 4: (a) AODV-Throughput versus speed
Fig. 4: (b) OLSR-Throughput versus speed
5.2 Scenario 2: Varying the scenario size
The results corresponding to the PDF, AE2E Delay and Throughput are shown in
Figure 5-7 respectively.
5.2.1 Packet delivery fraction
When there are increases in the size of the scenario, the density nodes decreases. The
total number of packets received decreases. By increasing the size of the simulated scenario
increases the block size, this prevents direct communication through the blocks and then
limits the spread and increases the radio losses of data packets which resulted to a decrease of
useful throughput and increase the number of nodes blind.By increasing the size of the
simulated scenario increases the block size, this prevents direct communication through the
blocks and then limits the spread and increases the radio losses of data packets which resulted
to a decrease of useful throughput and increase the number of nodes blind. The block sizes in
the topology play an important role in determining the performance of VANETs. With large
block sizes, vehicles spend more time in traversing between intersections; thus, nodes are
mobile more often. This increased mobility leads to a weakened connectivity in the network,
and a corresponding drop in the delivery ratio.

565
Fig. 5: (a) AODV- PDF versus size of the area
Fig. 5: (b) OLSR- PDF versus size of the area
5.2.2 Average end-to-end delay
Figure 6, depicts the Average end-to-end delay. As can be seen, when the area
increases, the system needs more time to inform the vehicles. As can be observed in figure,
the percentage of blind nodes highly depends on this factor. When the area is very small, the
percentage of blind nodes is also very small.
Fig. 6: (a) AODV-AE2E Delay versus size of the area

566
Fig. 6: (b) OLSR-AE2E Delay versus size of the area
When the size of the area increases, the number of blind nodes also increases.
Neverthe-less, the number of packets received per node decreases. We note that, if the size of
the urban area decreases (the density of nodes increases), and the number of link nodes
increases, which reduces the end to end delay, as well, the percentage of mobile blind
decreases. AODV protocol has a delay significantly higher than OLSR.
5.2.3 Throughput
Figure 7, illustrate the variation of throughput as a function of the scenario size. As
expected, the Two-Ray Grand model offers the best values of Throughput than Rice and
Nakagami models. The percentage of vehicles blind depends strongly on the size of the area.
OLSR has a throughput slightly higher than AODV.
Fig. 7: (b) AODV-Throughput versus size of the area

567
Fig. 7: (b) OLSR-Throughput versus size of the area
6. CONCLUSIONS AND PERSPECTIVES
In this article, we have study the impact of different radio propagation models on the
performance of vehicular ad hoc networks. According to the simulation findings, we may
state that the choice of the propagation models has a great impact on the routing protocol’s
performance. The latter decreases rapidly when the fading models, mainly Ricean and
Nakagami have been taken into consideration. The main reasons of their deterioration are the
outcome of the big variation in the received intensity signal.
In this paper, we have evaluated the performance of AODV and OLSR for vehicular
ad hoc networks in urban environments. We have tested OLSR and AODV against mobility
of vehicle and size of the scenario areas. Globally, for most of the metrics we have used in
this paper, OLSR has better performance that AODV. Indeed, OLSR has smaller routing
overhead and end-to-end delay. For the PDR, where OLSR may be outperformed by AODV.
We have also illustrated in this paper, that the average velocity was not a valid parameter to
evaluate routing protocols in VANET. Accordingly, one should rather evaluate ad hoc
protocols against new metrics, such as acceleration/ deceleration, or the length of street
segments instead of simple average mobility. We can also say that, the propagation delay is
lower when node density increases. Besides, the percentage of blind nodes highly depends on
this factor. When the area increases, the system needs more time to inform the rest of the
vehicles and the percentage of blind nodes highly depends on this factor, too. When the area
is very small, the percentage of blind nodes is also very small. When the area increases, the
number of blind nodes also increases. Nevertheless, the total number of packets received per
node decreases.
In the forthcoming studies, we plan to include geographical forwarding protocols in
future performance evaluation as they are more suited to dense networks; we will look at the
routing protocols’ behaviors in the multi-channel environment and/or multi-networks in order
to determine the key parameters that have an impact on the protocols’ choice.

568
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