Proceedings are available on @ International Journal of Information Technology & Computer Science ( IJITCS ) ( http://www.ijitcs.com ) (ISSN :
2091-1610 ) on volume No : 19 , Issue No : 1 ………………….. …………………………….. 72
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
Proceedings are available on @ International Journal of Information Technology & Computer Science ( IJITCS ) ( http://www.ijitcs.com ) (ISSN :
2091-1610 ) on volume No : 19 , Issue No : 1 ………………….. …………………………….. 73
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
Proceedings are available on @ International Journal of Information Technology & Computer Science ( IJITCS ) ( http://www.ijitcs.com ) (ISSN :
2091-1610 ) on volume No : 19 , Issue No : 1 ………………….. …………………………….. 74
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].
Proceedings are available on @ International Journal of Information Technology & Computer Science ( IJITCS ) ( http://www.ijitcs.com ) (ISSN :
2091-1610 ) on volume No : 19 , Issue No : 1 ………………….. …………………………….. 75
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.
Proceedings are available on @ International Journal of Information Technology & Computer Science ( IJITCS ) ( http://www.ijitcs.com ) (ISSN :
2091-1610 ) on volume No : 19 , Issue No : 1 ………………….. …………………………….. 76
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].
Proceedings are available on @ International Journal of Information Technology & Computer Science ( IJITCS ) ( http://www.ijitcs.com ) (ISSN :
2091-1610 ) on volume No : 19 , Issue No : 1 ………………….. …………………………….. 77
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
Proceedings are available on @ International Journal of Information Technology & Computer Science ( IJITCS ) ( http://www.ijitcs.com ) (ISSN :
2091-1610 ) on volume No : 19 , Issue No : 1 ………………….. …………………………….. 78
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
Proceedings are available on @ International Journal of Information Technology & Computer Science ( IJITCS ) ( http://www.ijitcs.com ) (ISSN :
2091-1610 ) on volume No : 19 , Issue No : 1 ………………….. …………………………….. 79
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
Proceedings are available on @ International Journal of Information Technology & Computer Science ( IJITCS ) ( http://www.ijitcs.com ) (ISSN :
2091-1610 ) on volume No : 19 , Issue No : 1 ………………….. …………………………….. 80
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.
Proceedings are available on @ International Journal of Information Technology & Computer Science ( IJITCS ) ( http://www.ijitcs.com ) (ISSN :
2091-1610 ) on volume No : 19 , Issue No : 1 ………………….. …………………………….. 81
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
REFERENCES
[1]. Kumar, MK Jeya, and R. S. Rajesh. "Performance analysis of MANET routing protocols in different mobility
models." IJCSNS International Journal of Computer Science and Network Security 9.2 (2009): 22-29.
Proceedings are available on @ International Journal of Information Technology & Computer Science ( IJITCS ) ( http://www.ijitcs.com ) (ISSN :
2091-1610 ) on volume No : 19 , Issue No : 1 ………………….. …………………………….. 82
[2]. Araghi, Tanya Koohpayeh, Mazdak Zamani, and Azizah BT Mnaf. "Performance Analysis in Reactive Routing
Protocols in Wireless Mobile Ad Hoc Networks Using DSR, AODV and AOMDV." Informatics and Creative
Multimedia (ICICM), 2013 International Conference on. IEEE, 2013.
[3]. Mbarushimana, Consolee, and Alireza Shahrabi. "Comparative study of reactive and proactive routing protocols
performance in mobile ad hoc networks."Advanced Information Networking and Applications Workshops, 2007,
AINAW'07. 21st International Conference on. Vol. 2. IEEE, 2007.
[4]. Park, Jiwon, Sangman Moh, and Ilyong Chung. "A multipath aodv routing protocol in mobile ad hoc networks
with sinrbased route selection." Wireless Communication Systems. 2008. ISWCS'08. IEEE International
Symposium on. IEEE, 2008.
[5]. Nasipuri, Asis, Robert Castañeda, and Samir R. Das. "Performance of multipath routing for on-demand protocols
in mobile ad hoc networks." Mobile Networks and applications 6.4 (2001): 339-349.
[6]. Rajesh SL, Somashekar C Desai & Ramakrishna KT, “PERFORMANCE EVALUATION OF AODV AND
AOMDV ROUTING PROTOCOLS IN WIRELESS MESH NETWORK”, ISSN (Print): 2319 - 2526, Volume-
2, Issue - 6, 2013
[7]. Moravejosharieh, Amirhossein, et al. "Performance Analysis of AODV, AOMDV, DSR, DSDV Routing
Protocols in Vehicular Ad Hoc Network." Research Journal of Recent Sciences ISSN 2277: 2502.
[8]. Chowdhury, Kaushik R., and Ian F. Akyildiz. "CRP: A routing protocol for cognitive radio ad hoc networks."
Selected Areas in Communications, IEEE Journal on 29.4 (2011): 794-804.
[9]. Khiavi, Mina Vajed, Shahram Jamali, and Sajjad Jahanbakhsh Gudakahriz. "Performance Comparison of AODV
and AOMDV Routing Protocols in Mobile Ad Hoc Networks." (2013).
[10]. Rohit Jain, Abhinav Mehta, Vinay Somani. “Performance Evaluation of Fault Tolerance Protocols in MANET”
International Journal of Computer Applications (0975 – 8887) Volume 61– No.2, January 2013.
[11]. Gaurav Sachdeva, Sukhvir Singh. “Energy Efficient DHT Based Multipath Routing in Wireless Sensor
Networks” IJARCSSE, ISSN: 2277 128X
[12]. Caleffi, Marcello, and Luigi Paura. "M DART: multi path dynamic address routing." Wireless Communications
and Mobile Computing 11.3 (2011): 392-409.
[13]. Ghodsi, Ali. Distributed k-ary system: Algorithms for distributed hash tables. Diss. KTH-Royal Institute of
Technology, 2006
[14]. Mishra, Ishani, and Divya Sharma. "Comparative Analysis of Multipath Routing Algorithms for Mobile Ad-hoc
Networks." INTERNATIONAL JOURNAL OF COMPUTERS & DISTRIBUTED SYSTEMS 3.3
(2013): 8-14.
[15]. Giri, Avinash, Jitendra Prithviraj, and Ashok Verma. "Analysis of DHT Based Multi-Path Routing Protocol with
Other Routing Protocols in MANETS."Analysis 1.1 (2012).
Proceedings are available on @ International Journal of Information Technology & Computer Science ( IJITCS ) ( http://www.ijitcs.com ) (ISSN :
2091-1610 ) on volume No : 19 , Issue No : 1 ………………….. …………………………….. 83
[16]. Giri, Avinash, Jitendra Prithviraj, and Ashok Verma. “Analysis of unipath and multipath routing protocols in
mobile Adhoc networks.” (IJSSAN), ISSN NO. 2248-9738 (print), vol-2, iss-1,2, 2012
[17]. Amnai, Mohamed, Youssef Fakhri, and Jaafar Abouchabaka. "Impact of Mobility on Delay-Throughput
Performance in Multi-Service Mobile Ad-Hoc Networks." International Journal of Communications, Network &
System Sciences 4.6 (2011).
[18]. Akkaya, Kemal, and Mohamed Younis. "A survey on routing protocols for wireless sensor networks." Ad hoc
networks 3.3 (2005): 325-349.
[19]. Ribeiro, Andrea, and Rute Sofia. "A survey on mobility models for wireless networks." SITI, University
Lusófona, Tech. Rep. SITI-TR-11-01 (2011).
[20]. Mihail L. Sichitiu. “Mobility Models for Ad Hoc Networks” Dept. of Electrical and Computer Eng., Campus
Box 7911, NC State University.
[21]. Camp, Tracy, Jeff Boleng, and Vanessa Davies. "A survey of mobility models for ad hoc network research."
Wireless communications and mobile computing 2.5 (2002): 483-502.
[22]. Jadoonl, Misbah, et al. "Location and Non-Location Based Ad-Hoc Routing Protocols under Various Mobility
Models: A Comparative Study." International Arab Journal of Information Technology (IAJIT) 9.5 (2012). [23].
Muthumayil, K., et al. "Performance Analysis of Reference Point Group Mobility model, Random Mobility models
in Associativity Based long-lived Routing (ABR) protocol." (2012).
[24]. Sacko, Diouba, et al. "A Survey of group merge and split mobility models."Ubiquitous Computing and
Communications Journal 2 (2007).
[25]. Zahid, Haleem Farman, et al. "Performance Evaluation of TCP (Transmission Control Protocol) and UDP (User
Datagram Protocol) over Destination Sequence Distance Vector (DSDV) for Random Waypoint Mobility Model."
World Applied Sciences Journal, vol.20, no. 7 , pp. 910916, November 2012.
[26]. Gupta, Anuj K., Harsh Sadawarti, and Anil K. Verma. "Performance analysis of AODV, DSR & TORA routing
protocols." IACSIT international journal of Engineering and Technology 2.2 (2010): 226-231. [27]. Bindra,
Harminder S., Sunil K. Maakar, and A. L. Sangal. "Performance Evaluation of Two Reactive Routing Protocols
of MANET using Group Mobility Model." International Journal of Computer Science Issues (IJCSI) 7.4 (2010).
[28]. Jayakumar, G., and G. Gopinath. "Performance comparison of manet protocols based on manhattan grid
mobility model." Journal of Mobile communication 2.1 (2008): 1826.
[29]. Manveen Singh Chadha, Rambir Joon, Sandeep “Simulation and Comparison of AODV, DSR and AOMDV
Routing Protocols in MANETs”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-
2307, Volume-2, Issue-3, July 2012
[30]. Pooja Kurariya, Ashok Verma, Shivendu dubey Rashween Saluja. “Behaviour of Mobile Adhoc Network under
OLSR and MPOLSR protocols with increasing number of nodes”. International Journal of Engineering Research
& Technology (IJERT) ISSN 2278-0181 Vol. 2 Issue 11, November – 2013
Proceedings are available on @ International Journal of Information Technology & Computer Science ( IJITCS ) ( http://www.ijitcs.com ) (ISSN :
2091-1610 ) on volume No : 19 , Issue No : 1 ………………….. …………………………….. 84
[31]. Usop, Nor Surayati Mohamad, Azizol Abdullah, and Ahmad Faisal Amri Abidin. "Performance evaluation of
AODV, DSDV & DSR routing protocol in grid environment." IJCSNS International Journal of Computer Science
and Network Security 9.7 (2009): 261-268.
[32]. Perkins, Charles E., et al. "Performance comparison of two on-demand routing protocols for ad hoc networks."
Personal Communications, IEEE 8.1 (2001): 16-28.
[33]. Sumathy, S., Beegala Yuvaraj, and E. Sri Harsha. "Analysis of Multicast Routing Protocols: PUMA and
ODMRP." International Journal of Modern Engineering Research (IJMER) 2 (2012).
[34]. Patel, Ankur, et al. "Group Mobility Model Based Proactive and Reactive Routing Protocol in MANET."
International Journal of Electronics and Computer Science Engineering (IJECSE, ISSN: 2277-1956) 1.04 (2012):
2377-2386. [35]. Chaba, Yogesh, R. B. Patel, and Rajesh Gargi. "Analysis of mobility models for Mobile ad hoc
networks." The Journal of Computer Science and Information Technology 6.1 (2007):50-55.