Int’l J. of Communications, Network and System Sciences, 2011, 4, 395-402
doi:10.4236/ijcns.2011.46047 Published Online June 2011 (http://www.SciRP.org/journal/ijcns)
Impact of Mobility on Delay-Throughput Performance in
Multi-Service Mobile Ad-Hoc Networks
Mohamed Amnai1, Youssef Fakhri1,2, J aafar Abouchabaka1
1Laboratoire de Recherche en Informatique et Télécommunications (LARIT), Equipe Réseaux et Télécommunications,
Faculté des Sciences, Kenitra, Maroc
2LRIT Unité Associée au CNRST, Faculté des Sciences de Rabat, Rabat, Maroc
Received March 22 , 20 1 1; revised April 2, 2011; accepted April 11, 2011
Abstract
Mobile Ad-Hoc network is a collection of mobile nodes in communication without using infrastructure. De-
spite the importance of type of the exchanged data between the knots on the QoS of the MANETs, the mul-
tiservice data were not treated by the larger number of previous researches. In this paper we propose an
adaptive method which gives the best performances in terms of delay and throughput. We have studied the
impact, respectively, of mobility models and the density of nodes on the performances (End-to-End Delay,
Throughput and Packet Delivery Ratio) of routing protocol (On-Demand Distance Vector) AODV by using
in the first a multiservice VBR (MPEG-4) and secondly the Constant Bit Rate (CBR) traffic. Finally we
compare the performance on both cases. Experimentally, we considered the three mobility models as follows
Random Waypoint, Random Direction and Mobgen Steady-State. The experimental results illustrate that the
behavior of AODV change according to the model and the used traffics.
Keywords: Mobility Models, AODV, VBR, CBR, QoS, MANET
1. Introduction
Mobile Ad-Hoc Network (MANET) is a self-configuring
network of mobile nodes connected using wireless links,
forming a random topology. The nodes move freely and
randomly. The network’s wireless topology may be un-
predictable. The minimal configuration, the quick de-
ployment and the absence of a central governing author-
ity make ad hoc networks suitable for several positions
as the multimedia teleconferences, construction site,
network residence and military conflicts etc. [1-3].
The Mobility models define nodes movement pattern
in ad hoc networks. The random behaviour of these
models as well as their implementations on the final ones
(computer, Tel…), requires some researches on the
evaluation of rout i n g p rotocols based on sim ul a t i ons.
The aim of a routing protocol is to discover the best
route that links up two nodes while guarantying a QoS in
communication. The quick change and unpredictable of
the topology of MANET network according to the ran-
dom mobility of nodes, makes route research difficult to
the routing protoc ol.
It is clear that the service quality QoS [4] in MANET
is not guaranteed because of the inherent dynamic nature
of a mobile ad hoc environment. In general, the per-
formances depend on the routing mechanism and nature
of mobility. In order to guarantee the QoS we should
process to deepened studies of evaluation regarding to
find the routing pro tocol and the mobilit y model that are
more adapted to an application. The QoS call for some of
the performance metrics as the throughput, the End-to-
End Delay and the jitter etc. Therefore many researches
were carried out on evaluation performances of the
MANETs as the performance analysis of the different
routing protocols and the effect of the random mobility
models on ad hoc networks [5-12].
The rest of this paper is organized as follows: in the
next section, we survey related work. In Section 3 we
discuss the problem formulation, followed by the simula-
tion environ ment used in this study. The results obtained
in this simulation are also discussed in Section 5. In the
end, Section 6 completes the paper.
2. Related Work
In the [13] Gupta and Kumar introduced a random net-

396 M. AMNAI ET AL.
work model for studying throughput scaling in a fixed
wireless network; the authors in the [14] have showed
that at the time of movement nodes, the throughput scal-
ing changes completely. According to [13,14] the author s
in [15] showed that the throughput and the delay are
characterized by three parameters: the number of hops,
the transmission range, the mobility and velocity of the
node. The authors propose schemes that exploit th e three
features to obtain different points on the through-
put-delay curve in an optimal way.
In [16] the authors showed that th e delay is influenced
by different network parameters: channel access prob-
ability, transmission power or radius, network load and
density of nodes.
The tradeoffs delay- throughput is the obj ect of a study
for the authors of the paper [17]. The same authors de-
veloped an algorithm to achieve the optimal tradeoffs
delay-throughput on certain conditions on the delay.
In [9] the experimental results illustrate that perform-
ance of the routing protocol AODV varies according to
different random mobility models: Random Waypoint,
Random Walk with Reflections and Random Walk with
Wrapping.
The effects of various mobility models on the per-
formance of the two routing protocols (DSR-Reactive
Protocol) and (DSDV-Proactive Protocol) has been stud-
ied in [1]. The four mobility models considered are:
Random Waypoint, Group Mobility, Freeway and Man-
hattan models. The study has shown that the perform-
ances vary with the change of used mobility models.
The performances of the three mobility models: Ran-
dom Waypoint, Random Walk with Reflections and
Random Walk with Wrapping, have been evaluated in
[18] with AODV routing protocol. The results show that
Random Waypoint Model is the best model which out-
performs both Random Walk Model and Random Direc-
tion Model in two different scenarios. The results indi-
cate that Random Waypoint produces the highest
throughput, while the throughput of the Random Walk
Model and Random Direction drastically falls over a
period of time.
The authors of the paper [19] present the performance
of Destination-sequenced Distance Vector (DSDV) in
four different mobility models called : Random Waypoint,
Reference Point Group Mobility (RPGM), Gauss
Markov and Manhattan Mobility Model. In this paper,
the results show that DSDV protocol with RPGM mobil-
ity model has optimized results varying network load and
speed.
Various protocols as AODV, DSDV, Dynamic Source
Routing (DSR) and TORA (Temporally-Ordered Rout-
ing Algorithm) are compared in [20]. The performance
parameters considered for analyzing are packet-delivery
fraction and End-to-End packet delivery delay according
to the mobility speed, traffic and network size. The used
mobility models are: Random Waypoint, Random Walk
and Random Directions. It is shown that AODV with
Random Waypoint is more performance than DSDV,
TORA and DSR and even with the Random Walk and
Random Direction models. It is suggested that AODV
can be used under high mobility because it is as efficient
as the DSDV, TORA and DSR protocols.
In [21] the authors have used a method to evaluate
performance, in terms of delay, Dynamic Source Routing
(DSR) in MANET with a multi-services traffic.
It is proposed in [22] a formulation of the routing
problem in multi-services MANETs as well as the im-
plementation of an adaptation of DSR protocol.
The three models of mobility (Random Waypoint,
Random Direction and Mobgen-Steady State) have been
evaluated in [5] bye using the traffic CBR. It is shown
that the optimal delay is ach ieved by Random Way Po int
in weak densities of nods and by Mobgen-Steady State
over high density of nodes. Nevertheless, the optimal
throughput is achieved by Random Way Point during the
weak and big densities of nods. In the paper [6] we ana-
lyzed the behaviour of the AODV protocol with the same
previous mobility models. But this time the study is
taken with a multiservice traffic (VBR). The AODV
protocol has shown a sensitive behaviour for the type of
used traffic. This change of behaviour of AODV enables
to do this comparative study using the two types of traf-
fic (CBR) and (VBR).
3. Problem Formulation
It is evident that the QoS must guarantees a certain level
of performances for different applications. However, the
ad hoc network is used in applications with different lev-
els of QoS. The network traffic is classified into time
sensitive traffic. In this category we find the applications
real time traffic that requires the minimal guarantee of
delay. Generally it must work without losing the data
(e.g. video conferencing) [23]. Some applications in real
time possess limits of the delay that must be guaranteed,
but these bounds can be slightly exceeded. In this cate-
gories many application can also tolerate a small amount
of packet loss [24]. The second category, it’s data traffic
which has no delay requirements but short averag e delay
is desired. Data traffic requires lossless transmission
[23].
From bit rate point of view, we have got two classes of
traffic Constant Bit Rate (CBR) and Variable Bit Rate
(VBR). In the first class some applications generate the
traffic in fixed rate. As regards practicing, some applica-
tions generate a traffic CBR. In the second class most of

M. AMNAI ET AL.
397
the applications generate variable bit rate streams (VBR).
This traffic is characterized by changing of the amount of
information transmitted by unit time, (i.e. the bit rate).
The degree of variation in bit rate is different from one
application to another [25].
Among the major challenges of the axes of research in
the ad hoc networks with a density of nodes, what are the
routing protocols as well as the fitting mobility models to
use for a scenario of given application?
To achieve this objective, some researches have fo-
cused on performances evaluation of routing protocols
and the mobility models given that most of previous re-
searches focused on traffic CBR which is not adapted to
the multimedia applications of the type of traffic VBR
[26].
The objective of ou r work is to ev alu ate differen tly th e
performances of AODV routing protocol, and to study
the behaviour of this protocol using the traffic CBR and
VBR with different mobility models. Thereafter, we
propose an adaptive method that exploit the results and
represent the optimal delay and the optimal throughput.
In this method to get the optimal delay for the three mo-
bility models the minimum acceptable values of delay
assigned to each number of nodes are considered. In or-
der to represent the optimal throughpu t for three mobility
models, the maximales values of throughput are consid-
ered for each number of nodes.
We have studied the impact of the nodes density on
performances (End-to-End Delay, Throughput and Packet
Delivery Ratio) of AODV routing protocol. The three
mobility models considered are: Random Way Point,
Mobgen-Steady St at e and R andom Direction.
The VBR traffic closely matches the statistical char-
acteristics of a real trace of video frames generated by an
MPEG-4 encoder [26]. Two parameters were used to
control the traffic stream. The first parameter, the initial
seed, results in the variants of traffic trace. This parame-
ter was kept constant at 0.4 [25], as the same traffic trace
needed to be used in all the experiments. The second
parameter, the rate factor, determined the level of scaling
up (or down) of the video input while preserving the
same sample path and autocorrelation function for the
frame size distribution. Its value is 0.33 for 40 source,
and 0.25 for 10, 20, 30 sour ces [24].
Based on [20], the AODV performs and can be used
under high mobility, better than DSDV, TORA and DSR
protocols.
It is clear that the reliable of performance results is
based on, the effective selection of the parameters of the
simulations. In simulations of mobile ad hoc networks,
the probability distribution that manages the movement
of the nodes typically varies according to the time, and
converges to a “steady-state” distribution. When node
speeds and locations are chosen from their steady-state
distributions, the parameters of performance for a given
protocol, convergent towards their values to steady-state
values as well. In [27], the authors show that more than
1000 seconds of simulation ti me may be needed to reach
steady state [28]. For this reason the simulation time
used in our wor ks is 1200 seconds.
The ad hoc reactive routing protocol considered Ad-
Hoc On-Demand Distance Vector Routing (AODV) [20]
as a dynamic multi-hop on-demand routing protocol for
mobile wireless ad hoc networks. AODV discovers paths
without source routing and maintains table instance of
route cache. This is loop free and uses destination se-
quence numbers. In AODV a node informs its neighbors
about its own existence by constantly sending “hello
messages” at a defined interval. This enables all no des to
know the status about their neighbors, i.e., if they went
down or moved out of reach. To resolve a route to an-
other node in the network AODV floods its neighbors
with a route request (RREQ). The receiving node checks
if it has a route to the specified node. If a route exists
then the receiving node replies to the requesting by
sending a route reply (RREP). If on the other hand a
route does not exist the receiving node sends a RREQ
itself to try to find a route for the requesting node. If the
original node does not receive an answer within a
time-limit the node can deduce that the sought nodes are
unreachable. To be sure that the route still exists, the
sender has to keep the route alive by periodically sen ding
packets. All nodes along the route are responsible for the
upstream links which means that a broken link will be
discovered by the closest node. This node signal the
broken link by sending an error message (RERR) down-
stream so that the using nodes can start to search for a
new route.
The mobility model is designed to describe the
movement pattern of mobile user, and how their location,
direction of movement, pause distribution, speed and
acceleration change over time. The mobility models
emulate a real world scenario for the way people might
move in, for example, a conference setting or museum...
3.1. Random Way Point (RWP)
In this model, each node is assigned an initial location, a
destination, and a speed. The points initial location and
destination are chosen independently and uniformly on
the area in which the nodes move. The speed is chosen
uniformly on an interval, independently of both the ini-
tial location and destination. After reaching the destina-
tion, a new destination is selected from the uniform dis-
tribution, and a new speed is chosen uniformly on
[min-speed, max-speed], independently of all previous

398 M. AMNAI ET AL.
destinations and speeds. The node stays for a specified
pause time upon reaching each destination, before re-
peating the process [9,11,26].
3.2. Random Direction (RD)
In the Random Direction Mobility Model each node is
assigned an initial direction, speed and a finite travel
time. The node then travels to the border of the simula-
tion area in that direction. Once the simulation boundary
is reached, the node pauses for a specified time, chooses
another angular direction (between 0 and 180 degrees)
and continues the process. The Random Direction Mo-
bility Model was created to overcome clustering of nod es
in one part of the simulation area produced by the Ran-
dom Waypoint Mobility Model. In the case of the Ran-
dom Waypoint Mobility Model, this clustering occurs
near the center of the simulation area.
3.3. Mobgen Steady-State (Mbg-SS)
The implementation [20] of the RWM model with set-
dest for NS2, starts with a constant pause time to the
initial location [29,30]. In th e other hand, the initial posi-
tions are chosen uniformly. With mobgen for NS2 [31],
an other implementation of the model RWM in NS2,
begins roughly by the half of the nods in movement and
the second half in pause [32]. For this reason, simula-
tions using setdest takes more time to converge to the
stationary state that simulations using mobgen. When
node speeds and locations are chosen from their
steady-state distributions, the performance metrics for a
given protocol, convergent towards their values to
steady-state values as well. For this reason, at the time of
the usage of setdest or mobgen, the performances net-
work systematically can change with the time and the
measures of collected performances during the conver-
gence period cannot reflect the values in the long term
[27]. The model of mobility Mobgen-Steady State is an
improvement of the model RWP. In this model th e initial
positions and the speeds of the knots are chosen from
their stationary distributions. Convergence is immediate
and the results of performances are reliable. The code of
the model Mobgen-Steady State is availab le to [33].
4. Simulation Environment
In order to achieve our aim we need to investigate how
the AODV protocol behaves when load of nodes increases
with different Mobility Models (Random Waypoint,
Random Direction and Mobgen Steady-State). Simula-
tions have been carried out by Network Simulator 2.34
NS-2. Multimedia traffic VBR (MPEG-4) and CBR are
used. In Table 1, we provide all simulation parameters.
Table 1. Simulation parameters.
Parameter Value
Simulation Time 1200 sec
Number of nodes 10, 20, 30, 40, 50, 60, 70, 80, 90, 100.
Pause Time 0, 10 Sec
Environment Size 1000 m × 1000 m
Traffic Type Variable Bit Rate (VBR) MPEG-4
Maximum Spee d s 10 m/s
Mobility Models Random Waypoint, Random Direction,
Mobgen Steady-State
4.1. Performance Metrics
For the simulation results, we have selected the end-to-
end delay and throughput as a metrics in order to evalu-
ate the performance of the different protocols:
 Average End-to-End Delay: the delay of a packet is
the time it takes the packet to achieve the destination
after it leaves the source. The average packet delay
for a network is obtained by averaging over all pack-
ets and all source destination pairs. The average
End-to-End Delay AVG
T is calculated as showing in
Equation (1):

1
Nr ii
rt
i
AVG r
H
H
TN

 (1)
i
t
H
emission instant of package i, i
r
H
reception in-
stant of package i, the total number of packets re-
ceived r
N
 Throughput: the ratio of successfully transmitted
data per second (2).

LC
TRf
L


 (2)
where

LC
Rf
L

 is the payload transmission rate,
(R)b/s Binary transmission rate, (L) Packet size, and


f

is the packet success rate defined as the probabil-
ity of receiving a packet correctly. This probability is a
function of the signal -to-noise ratio


.
 Packet Delivery Ratio: the ratio of the data packets
successfully delivered to the destination.
5. Results Discussion
In this section we present our simulation results and the
performance analysis. The analysis based on comparing
the different metrics of the mobility models that we de-
scribed previ o usl y in Section 3.
5.1. Variable Bit Rate (VBR)
As showing in Figure 1, with AODV, the delay increased

M. AMNAI ET AL.
399
Figure 1. End-to-End Delay vs No. of nodes with VBR.
when the density of nodes increase. When density be-
comes interesting, the delay for the three mobility mod-
els is still consistent. So with Random Direction, AODV
take less time to deliver the packets compared to the two
other models (Random Way Point, Mobgen-ss). On the
other hand, an important point is Mobgen-ss give best
performance than Random Way point in terms of delay.
Based on Figure 2, with Random Direction, AODV
shows higher throughput than both Random Way Point
and Mobgen-ss. Other, the three mobility models are
performed over all density of nodes. So Random Way
point produces a high throughput than the Mobgen-ss
model in the first part, inversely in th e second part Mob-
gen-ss almost outperforms Random Way point.
Figure 3 shows that, in Random Direction AODV
ensures a transfer of packet more than Random Way-
point and Mobgen-ss. But, the Packet Delivery Ratio for
the three mobility models decrease and it’s insufficient
over all density of nodes.
Generally with AODV and by using traffic VBR
(MPEG-4), the results (Figures 4-6) suggest using Ran-
dom Direction in the applications real time that has a
delay bounds that need to be met. Also it can be used on
applications that tolerate a small amount of packet loss.
5.2. Constant Bit Rate (CBR)
Figure 4 shows that the performance of the AODV
Figure 2. Throughput vs No. of nodes wi th VBR.
Figure 3. Packet Delivery Ratio vs No. of nodes with VBR.
Figure 4. End-to-End Delay vs No. of nodes with CBR.
Figure 5. Throughput vs No. of nodes wi th CBR.
Figure 6. Packet Delivery Ratio vs No. of nodes with CBR.
routing protocol in terms of End-to-End Delay is less,
constant and consistent when small density of nodes is

400 M. AMNAI ET AL.
used. In this part, with Random Way Point mobility
model, AODV take less ti me to deliver the packets com-
pared to Random Direction and Mobgen-ss mobility
models. When density becomes heavy, the behaviors of
AODV change drastically. The End-to-End Delay in-
crease considerably. In this part, the delay produced by
AODV in Mobgen-ss is less than Random Way point and
high in Random Direction model. AODV in Random
Direction model performs better than other mobility
models.
If we consider just the applications th at sen sitive to th e
delay, with small density of nodes the optimal delay
achieved with Random Way Point and with heavy den-
sity it is achieved by Random Direction. So for type of
applications the results suggest using Ran dom Way Point
on small density and Random Direction on high ones.
Based on the result of the Figure 5, AODV with
Random Way Point and Mobgen-ss models show higher
throughput than Random Direction. After that the
throughput of AODV, with the three mobility models
decrease when increasing density of nodes. But, with
Random Direction AODV produce high throughput bet-
ter of both Random Way Point and Mobgen-ss respec-
tively. On the other hand, if we consider the applications
that require a certain level of throughput the results sug-
gest using AODV with Random Way Point in weak den-
sities and with Random Direction mobility model in big
densities.
As showing in Figure 6, a higher Packet Delivery Ra-
tio for small density, is achieved when using AODV with
Random Way Point and Mobgen-ss mobility models. In
Random Direction mobility model, AODV performed
better in delivering packet data to the destination when
increasing nodes density.
5.3. VBR and CBR
The most popular mobility mod el used in the literature is
Random Way Point [29]. This model, with CBR, gives
best performance in term of delay, throughput and Packet
Delivery Ratio on small density (Figures 4-6).
The first remark when changing traffic from CBR to
VBR (MPEG-4) on the performance (End-to-End Delay,
throughput and Pack et Delivery Ratio) on AODV routing
protocol is, the behaviour of AODV is changing when
using a small density of nodes.
When density is heavy, AODV (with CBR and VBR
traffic) keeps the same behavior (Figures 1-6) in terms
of delay and Packet Delivery Ratio except the case of
throughput. On the other hand increasing the density of
nodes from a small to heavy one has no effect on the
behavior of AODV protocol in association with a traffic
VBR (MPEG-4).
Because the Mobgen Steady State is more realistic
than the Random Direction model the optimal delay
Figure 4 is achieved in small density with Random Way
Point and in heavy density with Mobgen Steady State.
The optimal throug hpu t Figure 5 is achiev ed by Ra ndom
Way Point over all densities of nodes used, this in case
of the CBR traffic. On the other hand, in case of VBR
traffic the optimal delay Figure 1 is identical to that of
Mobgen Steady State and with weak densities the opti-
mal throughput Figure 2 is got by Random Way Point
when the big densities used the optimal one is repre-
sented almost by both Random Way Point and Mobgen
Steady State. hence we promotes the use of the Mobgen
Steady State model in the applications that sensitive to
delay (Figures 1 and 4) and that using a heavy density of
nodes without considering the traffic type (CBR or VBR
(MPEG-4)) but for a small density in association with
CBR traffic we suggest using Random Way Point and
Mobgen Steady State in case of VBR traffic.
On the other side if we consider the applications that
require a certain level of throughput (Figures 2 and 5)
and that both Random Way Point and Mobgen Steady
State are more realistic than Random Direction we sug-
gest using the first one mobility model in weak densities
of nodes without considering the traffic type (CBR or
VBR (MPEG-4)). For the same applications in associa-
tion with a traffic CBR when using heavy densities of
knots the Random Way Point can give the best perform-
ance. For the same applications in association with a
traffic CBR when using heavy densities of knots the
Random Way Point can give the best performance. In-
versely, with traffic VBR, we advise using the Mobgen
Steady State model with the heavy densities of knots.
Finally, based on behaviour of variability of VBR
(MPEG-4), the Packet Delivery Ratio is still insufficient
over all density of nodes. This is for all the three mobil-
ity models. Hence, AODV protocol can be used on ap-
plications that tolerate a small amount of packet loss.
6. Conclusions and Future Work
We have presented the behaviour of AODV routing pro-
tocol with multimedia traffic (VBR) and CBR, by using
various mobility models as Random Way Poin t, Random
Direction and Mobgen Steady State.
With AODV model in association with CBR traffic, in
the first one, the optimal delay is achieved respectively
by Random Way Point in small density and Mobgen
Steady State in heavy density. In the second one, the
optimal throughput is ach ieved by Random Way Point.
In the association of AODV model with traffic VBR
(MPEG-4), in the first, the optimal delay is got by means
of Mobgen Steady State. In the second, the optimal

M. AMNAI ET AL.
401
throughput is achieved respectively by Random Way
Point and Mobgen Steady State.
With this method, we hope to help the future studies in
their choice of parameters. This, in order to design the
realistics scenarios which depict real world applications
more accurately and more of QoS.
Other most important point in this paper is the behav-
ior of AODV, with the three mobility described previ-
ously, depend on the traffic used (CBR or VBR). This
behavior is influenced precisely in case of low densities
of nodes.
One of the most interesting parameters to consider
when supporting real time communication is the delay
jitter. In the future, further study also needs to be done
with delay jitter metric.
On the other hand, in the future, further study should
be devoted to optimize the Packet Delivery Ratio when
using traffic VBR.
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