Impact of Mobility Models over Multipath Routing Protocols€¦ · The authors compare the performance of three routing protocols AODV, DSR and DSDV with the varying pause time and

Proceedings are available on @ International Journal of Information Technology & Computer Science ( IJITCS ) ( http://www.ijitcs.com ) (ISSN :

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Impact of Mobility Models over Multipath Routing Protocols

Zahid Khan 1, Tayeba2, Haleem Farman3, Isra Iqbal Awan2, Abdul Nawaz2

1Department of Informitique, University of Nice, France

2Department of Computer Science, Islamia College (Chartered University) Peshawar, Pakistan 3Department of

Computer Science, University of Peshawar, Pakistan

Abstract :

Mobile Ad-hoc Network (MANETs) is a peer to peer fashion network of intelligent nodes in order to establish a

communication session for disaster or emergency like environment, where a proper structure seems to be

hard/impossible. Recently, many reactive and proactive protocols have been proposed for MANETs. Multi-path

routing protocols have main objective to load balance for highly congested network. This paper aims to study the

behavior of multi-path routing protocols (Ad-hoc On Demand Multipath Distance Vector (AOMDV), Multi-Path

Dynamic Addressing routing (MDART)) under five different mobility models (Random Waypoint mobility, Random

Walk mobility,

Reference Point Group mobility, Gauss Markov, Manhattan Grid mobility model). The RPGM model outperform for

both AOMDV and MDART regarding Throughput, End-to-End Delay, Average Packet loss, and Packet Delivery

Fraction (PDF). The output of selected performance matrices under selected mobility models has an inverse relation

with node density for both multi-path protocols.

Keywords: Multi-path, AOMDV, MDART, RPGM, Random waypoint, Random Walk, Guass Makov, Manhattan

Grid, mobility models

I. INTRODUCTION

Mobile ad-hoc network (MANETs) is the collection of self-configuring and independent nodes, where the

mobile node forms a dynamic topology. A MANETs is an infrastructure less environment, where nodes are not bound

to any base station or access point, every node have intelligence to discover source to destination route for the sack of

data and resources sharing [1, 2]. The dynamic topological structure of MANETs is a challenging task, the random

mobility of nodes unstable the structure and overall nodes lost path information as well as reduce its throughput highly.

To get control over the above challenges the MANETs nodes should be enough intelligent to maintain its status. The

intelligent behaviors depend on strong routing algorithms. Routing is the selection of shortest and optimal routes for

communicating devices. It deals with the selection of optimal tracks between sender and receiver through different

routing protocols [3].

In MANET Reactive and Proactive are the two well-known routing approaches while Hybrid is the

combination of both approaches [1, 4]. Multipath routing concept arises to get control over the above said problems like



path loss due to high dynamicity. Multipath routing is the selection of multiple routes for the communicating devices

[5]. Examples of multipath routing protocols are AOMDV, MDART, and MP-OLSR.

In this paper, we examined multipath routing protocols (MDART, AOMDV) under different mobility models

like Random-Way Point, Random-Walk, Gauss Markov, Reference Point Group Mobility model, and Manhattan Grid

etc to investigate its performance. The throughput, delay, packet-loss, and packet delivery fractions are different

performance parameters which are measured in each of the above mentioned models.

This paper is organized as follows: Section 2 is about the discussion of selected protocols (AOMDV &

MDART), section 3 is about the literature of different protocols performance, section 4 have detailed about the

mobility models, section 6 consist of the experimental results and their discussion.

II. OVERVIEW OF MULTIPATH ROUTING PROTOCOLS

The selected Multipath routing protocols (AOMDV & MDART) have been introduced in this section.

Multipath protocols aim to reduce traffic overhead. MDART and AOMDV are well known multipath protocols hence

selected for simulation and experiments in this paper.

A) Ad-hoc On Demand Multipath Distance Vector (AOMDV)

AOMDV is reactive multipath routing protocol having multi path to destination, but used one at a time of

transmission. AOMDV is an extension of the AODV but the basic difference is that of unipath and multipath

communication. The rest of functionalities of AOMDV and AODV are same like route discovery and route

maintenance [6]. In case of AOMDV nodes doesn’t repeat the process of route discovery, it occur when all the selected

paths are failed to deliver the data, while in AODV the same process of route discovery repeated when the selected

single path is failed. In AOMDV when one path is failed the source node has the alternate path to transfer the data [7].

AOMDV doesn’t require any special type of control packet to control the overall processing, but use the

control mechanism of AODV with an extra field in the header [8, 9].

B) MULTI-PATH DYNAMIC ADDRESSING ROUTING (MDART)

MDART is a proactive multipath routing protocol, extension of DART (Dynamic Addressing routing)

protocol. The basic difference between both is path information, DART is unipath, whereas MDART is Multipath [10].

The multipath supporting feature of MDART doesn’t increase traffic overhead and also have no effect on

communication session [10]. MDART based on dynamic addressing paradigm, means DHT (Dynamic Hash Table)

algorithm is used to implement the hierarchical structure of network in such a way to reduce overall routing overhead.

DHT (Dynamic Hash Table) provides mapping of network addresses and node identities

[11, 12, 13, 14, 15, 16].

III. MOBILITY MODELS

The mobility models are designed to specify the movement of mobile nodes and to determine different parameters

like pause-time, speed, movement pattern with respect to variation in time. These mobility models help in the



simulation of such scenarios which are difficult to present realistic [17]. Every protocol acts differently in different

mobility scenarios. In this paper we studied the behavior of the multipath routing protocols AOMDV and MDART in

five different mobility models such as Random Waypoint mobility, Random Walk mobility, Reference Point Group

mobility, Gauss Markov and Manhattan Grid mobility model.

A) RANDOM WAY POINT MOBILITY MODEL

RWM is one of the prominent and well known mobility modal. According to the internal operation of this model, a

specific node starts its motion from initial point and goes towards the destination with specific speed within simulation

area. After reaching the destination, specified node wait for some time (pause time) and then randomly select other

direction to move [18]. The topological situation of RWP is dependent on two parameters, pause time and speed. If a

node moves with high speed having short pause time then the topology is said to be more dynamic [19] .

B) RANDOM WALK MOBILITY MODEL

In Random Walk mobility model, any node start its motion from the current location and move towards the destination

in random direction with specific speed within the given range (0-2π) and (Vmin-Vmax) respectively. If the

accelerated node reach to the specified area boundary, it

bounces off with the determined angle [20] [21].

C) REFERENCE POINT GROUP MOBILITY MODEL (RPGM)

RPGM creates number of groups with a specified leader in each group. The motion group depends on the

group leader speed. The group movement is determined by the cumulative value that is specified by the motion,

direction and speed of the corresponding nodes. Individual nodes of any group moves randomly about their predefined

reference point [22] [23]. As the individual nodes move in the time interval (T to T+1), hence their location are updated

accordingly to the group logical center, which is further combined with the random motion vector to represent the

random motion of each individual node about the reference point [24].

D) MANHATTAN GRID MOBILITY MODEL

In Manhattan Grid mobility model the simulation area is divided into a grid of vertical and horizontal streets.

At the intersection of horizontal and vertical line, the node has choice to turn along left or right or move backward.

The probability to take rotate in four paths is 25%, while 50% in case of two paths. In Grid environment nodes

movement depends on its surrounding and its previous movement [18] [19].

E) GAUSS MARKOV MOBILITY MODEL

GMM used Gaussian model, hence it eliminates sharp and sudden turn chances. The key feature of the Gauss

Markov mobility model is spatial dependency in which the future action is determined by the previous one. For

example the speed and direction of the Kth instance is based on the speed and direction of the Kth-1 instance. The speed

and direction is assigned to each node at the start of the simulation and updated after a fixed time interval [19] [22].



IV. LITERATURE REVIEW

This section is about the literature of different protocols performance analysis as that of our experimental work.

In [25], the authors show the performance of DSDV under TCP and UDP with the varying performance metrics such

as speed, pause time and node density. Their result shows that UDP outperform under dense environment, while TCP

works well in highly mobile environment.

Gupta in [26] study the behavior of On-Demand routing protocols like AODV, DSR and TORA in term of end-to-end

delay and packet delivery ratio with the varying pause time and node density. Their results show that DSR will work

better in moderate mobility environment, while TORA outperformed in large networks having high density.

The authors measure the performance of the two Reactive routing protocols AODV and DSR using RPGM

model. They showed that AODV works better in real time transmissions using UDP connection while DSR works

better for TCP connection and for low bandwidth network [27] .

Jayakumar and Gopinath in [28] study the performance comparison of AODV and DSR using Manhattan Grid

as mobility model in the term of delay, packet delivery fraction, normalized routing load and normalized medium

access load. Their simulation shows that the performance of protocols is not influence by the mobility pattern. The

packet delivery fraction is nearly closed for both protocols in dense environment.

Manveen, Rambir and Sandeep in [29] simulate and compare the two reactive protocols (AODV & DSR) and

the reactive multipath routing protocol AOMDV in term of throughput, delay and PDF. They concluded that AOMDV

works better than AODV and DSR in term of throughput and PDF.

In [30] the authors evaluate the performance behavior of OLSR and MP-OLSR routing protocols using

performance metrics like throughput, delay and PDF. Their result shows that MPOLSR perform better that OLSR for

higher node mobility in term of end-to-end delay. For small networks having nodes in the range of 50-100 nodes the

OLSR works better than MPOLSR with the increasing simulation time.

The authors compare the performance of three routing protocols AODV, DSR and DSDV with the varying

pause time and constant node density using Random Way Point model [31]. They concluded that AODV works better

in low mobility situation but failed in term to delivered packets with high mobility. While the DSR works better than

DSDV and AODV in high mobility.

CE Perkins compares two reactive protocols AODV and DSR with the changing node density [32]. They found

that DSR outperforms than AODV in term of throughput and delay with the less saturated environment and produce

low overhead than AODV. While in dense environment AODV works better than DSR.

In this paper we will examine the reactive and proactive multipath routing protocols (AOMDV & MDART)

with different mobility models with the changing node density, keeping speed and pause time constant.



V. SIMULATION RESULTS AND DISCUSSIONS

This section is about the discussion of all experimental results and their conclusion. All the experiments

are related to two Multi-path routing protocols (AOMDV, M-DART) with five different mobility models. The

simulation of selected protocols and models are carried out under a specified environment. All the simulation

parameters, selected protocols, mobility models, and performance metrics are given in below Table-1.

Parameters Values Mobility Models

Simulator NS-2.35 1) Random Waypoint

mobility

2) Random Walk mobility

3) Reference Point Group mobility

4) Gauss Markov

5) Manhattan Grid mobility model.

Scenario Tools BonnMotion 2.1a,

Setdest

Simulation Time 500 s

Simulation Area 500x500

Transmission Time 500 s Performance Metrics

Traffic Type UDP 1) Throughput

2) End-to-End Delay

3) Packet loss

4) Packet Delivery

Fraction

Data Payload 0.01Mbps

No. of Connections 8 connections

Selected Multi-path p rotocols

Protocol-1 M-DART

Protocol-2 AOMDV

Table 1: Simulation Setup

Each selected protocol is further evaluated under different five mobility models and its performance is measured

through selected performance metrics.

A) BEHAVIOR ANALYSIS OF M-DART UNDER DIFFERENT MOBILITY MODELS:

The functionality of every protocol changes with respect to different mobility models. The below section depicts the

variation of selected performance matrices under selected mobility models as shown in table-1.

a) M-DART Throughput:

In the selected mobility models (Random Waypoint mobility, Random Walk mobility, Reference Point

Group mobility, Gauss Markov, Manhattan Grid mobility model) M-DART works better in the group mobility models

like RPGM because of high spatial dependencies for small values of Angle Deviation Ratio (ADR) and Speed

Deviation Ratio (SDR) [19] [33, 35]. Higher spatial dependency means the large link duration that provide good result for

average throughput (kbps) and low overhead in RPGM [34] [35].



The given Figure 1 shows the simulation results of M-DART protocol under the selected mobility models

using UDP traffic connection. Due to continuous motion of nodes, the performance in term of throughput is degraded

in all models except RPGM, because in RPGM the motion and speed of nodes is determined by the group leader.

In RPGM most of the communication is among the group’s leaders that’s way there is low routing overhead.

Hence density of network less affects the performance of M-DART under RPGM. The remaining four models

behaviors have nearly same impact over the throughput of M-DART.

Figure 1: MDART Throughput under different Mobility Models

b) M-DART End to End Delay

According to the simulation results of M-DART in Figure 6.1.2, the group mobility RPGM is degraded in

term of end to end delay, because of the high throughput. The number of incoming packets for the receiver is greater

than usual, so the processing of these packets take more time. The E2E delay of Manhattan Grid model is optimal

throughout simulation, because in Manhattan Grid the path changes according to predefined maps. The predefine map

reduce the packets delay overall, hence Manhattan Grid outperform than other selected models in term of end to end

delay. The

Figure 2: MDART E2E Delay under different Mobility Models



c) M-DART Average Packet Loss

The packet loss ratio of M-DART under group mobility RPGM, is less than the other mobility models as

shown in Figure 3. The reason behind that is, In RPGM the mobility of nodes is under the control of group leader in

specific pattern.

The overhead is reduced due to the categorization of nodes into groups, where the exchange of control

messages done among the group leaders. Figure 3 depicts that the other models behave same up to some limit in case

of packet loss.

d) M-DART Packet Delivery Fraction (PDF)

In previous section it has been concluded that RPGM contested other models regarding average packet loss. Average

packet loss and PDF has inversely proportion to each other, hence by increasing one the other decrease. As average

packet loss is very low in group mobility (RPGM) hence its packet delivering fraction (PDF) is high as shown in

Figure 4. The impact of node density is directly proportional to Average packet loss and inversely proportional to

PDF as depicts in Figure 3 and Figure 4 respectively.

Figure 3: MDART Average Packet loss under different Mobility Model



B) BEHAVIOR ANALYSIS OF AOMDV UNDER DIFFERENT MOBILITY MODELS:

In this sub section, AOMDV is evaluated under different mobility models and their simulation results are

discussed with proper conclusions. The simulation setup and performance parameters are same as that of M-DART.

a) AOMDV Throughput:

AOMDV outperforms in RPGM mobility model than other models as shown in Figure 5. The higher

throughput of AOMDV has the same reason as that of M-DART throughput mentioned in previous section. The

mobility in other model is unpredictable, the nodes randomly move in any direction hence the established path remains

temporary for transmission, which overall affect their throughputs.

While in RPGM the nodes move under the control of their group leaders, which could increase its path stability and

overall its throughput increases compared to others. Models

b) AOMDV End to End Delay:

The line graph in Figure 6 depicts that AOMDV works better in case of RPGM models as it has low end to

end delay than rest of the selected models. An AOMDV is a reactive protocol by nature where nodes discover its

destination dynamically when communication session is demand.

Figure 4: MDART PDF under different Mobility Models

Figure 5: AOMDV Throughput under different Mobility



In AOMDV a dynamic path established for the communication session as in case of RPGM the topological

structure remains static compare to others hence its delay drastically decreases for the static positions of sender and

receiver. The multipath nature of AOMDV better perform load balancing, when an ongoing session over headed the

protocol transfer traffic on other backup routes.

Figure 6: AOMDV E2E Delay under different Mobility Models

c) AOMDV Average Packet Loss:

The average packet loss is degraded in RWP, RW, GMM and MGM because of the irregular movement

pattern. The average packet loss of above mention models nearly same for higher denser environment except the

RPGM model, where packet loss ratio is lower for all density points as shown in Figure 7. The node density is directly

proportional to average packet loss.

Figure 7: AOMDV Average Packet loss under different Mobility Models

d) AOMDV Packet Delivery Fraction (PDF):

PDF and average packet loss are inversely proportional to each other. As in previous section RPGM has

lower average packet loss rate for all density points hence its PDF is higher as shown in Figure 8.



The higher PDF rate of RPGM is because of the predefined movement pattern under the control of the group

leader. The nodes are in communication with each other for long time until the transmission completed or the link

broken occurs.

Figure 8: AOMDV PDF under different Mobility Models

VI. CONCLUSIONS

The performance of MANETs routing protocols greatly effect by many parameters. The dynamic nature of

MANETs demands a suitable environment, where it outperforms with optimal results. The paper aims to conclude

which mobility model (Random Waypoint mobility, Random Walk mobility, Reference Point Group mobility, Gauss

Markov, Manhattan Grid mobility model) will be suitable for multi-path (AOMDV, M-DART) routing protocols.

First of all, by observing all the experimental results, we have concluded that performance of multi-path routing

protocols under selected mobility models has an inverse relation with node density. Secondly, RPGM outperform

regarding throughput, end to end delay, average packet loss, and PDF for both selected multi-path protocols under all

mobility models.

A thorough study of simulation concludes that all selected mobility models except RPGM perform nearly

same for highly denser topological environment.

In nutshell, on the basis of all experimental work, we can say that RPGM will be the most suited model for

multi-path routing protocols under a moderate denser network

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Impact of Mobility Models over Multipath Routing Protocols€¦ · The authors compare the performance of three routing protocols AODV, DSR and DSDV with the varying pause time and