PERFORMANCE EVALUATION OF AOMDV PROTOCOL BASED ON VARIOUS SCENARIO AND TRAFFIC PATTERNS

International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.6, December 2011
DOI : 10.5121/ijcsea.2011.1603 33
PERFORMANCE EVALUATION OF AOMDV
PROTOCOL BASED ON VARIOUS SCENARIO AND
TRAFFIC PATTERNS
P.Periyasamy1
and 2
Dr.E.Karthikeyan
1
Department of Computer Science and Applications, Sree Saraswathi Thyagaraja
College, Pollachi - 642 107, Tamil Nadu, India
pereee@yahoo.com
2
Department of Computer Science, Government Arts College, Udumalpet - 642 126,
Tamil Nadu, India
e_karthi@yahoo.com
ABSTRACT
A MANET is an interconnection of mobile devices by wireless links, which forms a dynamic topology.
Routing protocols play a vital role in transmission of data across the network. The two major
classifications of routing protocols are unipath and multipath. In this paper, we have evaluated the
performance of a widely used on-demand multipath routing protocol called AOMDV. This protocol has
been selected due to its edge over other protocols in various aspects, such as reducing delay, routing load
etc. The evaluation of AOMDV protocol is carried out in terms of four scenario patterns such as RWM,
RPGM, MGM, and GMM in two different traffic patterns such as CBR and TCP using NS2 and Bonn
Motion.
KEYWORDS
MANET, unipath, multipath, AOMDV, RWM, RPGM, MGM, GMM, CBR, TCP, scenario patterns and
traffic patterns.
1. INTRODUCTION
1.1. Mobile Ad Hoc Networks
Mobile Ad hoc NETwork (MANET) is an interconnection of mobile devices by wireless links.
It does not need much physical infrastructure such as routers, servers, access points or cables.
Each mobile device functions as router as well as node. The most important characteristics of
MANET are i) Dynamic topologies ii) Bandwidth-constrained links iii) Energy constrained
operation and iv) limited physical security [7]. The various applications of MANETs are: i)
military - communication among soldiers in enemy environments, ii) personal area network -
printers, PDA, mobile phones, iii) business indoor application - meetings, symposium, demos, iv)
civilian outdoor application - taxis, cars, sport stadiums, v) emergency application - emergency
rescue operations, police, and earthquakes, and vi) home intelligence devices.

34
1.2. Routing Protocols
To have communication within the network, a routing protocol is used to discover routes between
nodes. The primary goal of such ad-hoc network routing protocol is to establish correct and
efficient route between a pair of nodes so that messages may be delivered in a timely manner.
Route construction should be done with a minimum of overhead and bandwidth consumption.
The two major classifications of MANET routing protocols are unipath and multipath routing
protocols.
1.2.1. Unipath Routing Protocols
The unipath routing protocols [1] discover a single route between a pair of source and destination.
A new route discovery is required in response to every route break which leads high overhead and
latency. The two components of unipath routing protocols are i) Route Discovery: finding a route
between a source and destination. ii) Route Maintenance: repairing a broken route or finding a
new route in the presence of a route failure. The most commonly used unipath routing protocols
are Ad Hoc On-demand Distance Vector (AODV) [6], Dynamic Source Routing (DSR) [6], and
Destination Sequenced Distance Vector (DSDV) [6].
1.2.2. Multipath Routing Protocols
The multipath routing protocols [1] discover multiple routes between a pair of source and
destination in order to have load balancing to satisfy Quality of Service (QoS) requirements. The
three main components of multipath routing protocols are i) Route Discovery: finding multiple
nodes disjoint, links disjoint, or non-disjoint routes between a source and destination. ii) Traffic
Allocation: Once the route discovery is over, the source node has selected a set of paths to the
destination and then begins sending data to the destination along the paths. iii) Path
Maintenance: regenerating paths after initial path discovery in order to avoid link/node failures
that happened over time and node mobility.
The benefits of the multipath routing protocols are i) Fault tolerance: Being redundant
information routed to the destination via alternative paths it reduces the probability of the
disruption of communication in case of link failures, ii) Load Balancing: selecting diverse traffic
through alternative paths in order to avoid congestion in links, iii) Bandwidth aggregation:
Splitting the data into multiple streams and then each of which has routed through a different path
to the same destination. Hence the effective bandwidth can be aggregated and iv) Reduced delay:
In the unipath routing protocols, the path discovery process needs to be initiated to find a new
route in order to avoid a route failure and this leads to high route discovery delay. This delay is
minimized in multipath routing protocols by backup routes that have been identified in route
discovery process. The most recently used multipath algorithms are Temporarily-Ordered
Routing Algorithm (TORA) [3], Split Multipath Routing (SMR) [3], Multipath Dynamic
Source Routing (MP-DSR) [1], Ad hoc On-demand Distance Vector-Backup Routing (AODV-
BR)[3] and Ad Hoc On-Demand Multipath Distance Vector Routing (AOMDV) [3].
The AOMDV protocol is widely used in mobile communication because of its edge over other
protocols in various aspects, such as reducing delay, routing load [1,3] etc. It is an on-demand
multipath routing protocol – starts a route discovery procedure when needed for MANET. We
evaluate its performance in terms of different mobility models such as Random Way point
Mobility (RWM), Reference Point Group Mobility (RPGM), Manhattan Grid Mobility (MGM)
and Gauss-Markov Mobility (GMM) and also different traffic patterns such as CBR and TCP
traffic patterns. To analyze these protocols, traffic patterns and mobility models are essential and
are discussed in subsequent sections.

35
The rest of this paper is organized as follows: In section 2, the characteristics of traffic patterns
are discussed; in section 3 the various mobility models are described; in section 4, the
functionality of AOMDV protocol is given; in section 5 the simulation model is discussed; in
section 6 the performance metrics are described; in section 6 the experimental results are
discussed and finally in section 7 the conclusion is given.
2. TRAFFIC PATTERNS
Traffic Patterns describe how the data is transmitted from source to destination. The two types of
traffic patterns employed in MANET are CBR and TCP Traffic patterns.
2.1. CBR Traffic Pattern
The qualities of Constant Bit Rate (CBR) traffic pattern [2,14] are i) unreliable: since it has no
connection establishment phase, there is no guarantee that the data is transmitted to the
destination, ii) unidirectional: there will be no acknowledgment from destination for confirming
the data transmission and iii) predictable: fixed packet size, fixed interval between packets, and
fixed stream duration.
2.2. TCP Traffic pattern
The qualities of Transmission Control Protocol (TCP) traffic pattern [2,14] are i) reliable: since
connection is established prior to transmitting data, there is a guarantee that the data is being
transmitted to the destination, ii) bi-directional: every packet that has to be transmitted by the
source is acknowledged by the destination, and iii) conformity: there will be flow control of data
to avoid overloading the destination and congestion control exists to shape the traffic such that it
conforms to the available network capacity [2]. Today more than 95% of the Internet protocol
traffic is carried out through TCP.
3. MOBILITY MODELS
Mobility models describe the movement pattern of the mobile users, their location; velocity and
acceleration [4,12]. They play a vital role in determining the performance of a protocol and also
differentiated in terms of their spatial and temporal dependencies. i) Spatial dependency is a
measure of how two nodes are dependent in their motion. When the two nodes are moving in the
same direction, then they have high spatial dependency. ii) Temporal dependency is a measure of
how current velocity (magnitude and direction) are related to previous velocity. The two nodes
are having the same velocity and direction means that they have high temporal dependency. The
commonly used mobility models are RWM, RPGM, MGM and GMM.
3.1. Random Way point Mobility (RWM)
RWM [4] model is the commonly used mobility model in which every node randomly chooses a
destination and moves towards it from a uniform distribution (0, Vmax) at any moment of time,
where Vmax is the maximum allowable velocity for every node. Each node stops for a duration
defined by the 'pause time' parameter when it reaches the destination. After the pause time it
again chooses a random destination and repeats the whole process until the end of the simulation.

36
3.2. Reference Point Group Mobility (RPGM)
The military battlefield communication uses RPGM [4] model in which each group has a logical
center called Group Leader (GL) for determining the group's motion behavior. Each node in this
mobility deviates from its velocity (both magnitude and direction) from that of the leader is
calculated as follows:
SVV MAXLM
SDRrandomtt **())()( += (1)
AMAXLM
ADRrandomtt **())()( += θθ (2),
where VM and VL are the magnitude of member and leader respectively, θM and θL are direction
of member and leader respectively, SMAX and AMAX are maximum speed and angle respectively, 0
< ADR and SDR < 1, SDR is the Speed Deviation Ratio and ADR is the Angle Deviation
Ratio. SDR and ADR employed to control the deviation of the velocity of group members from
that of the leader.
3.3. Manhattan Grid Mobility (MGM)
MGM [4] models are very useful to emulate the movement pattern of mobile nodes on streets.
This is sometimes called Urban Area (UR) model. It forms a number of horizontal and vertical
streets like a grid called maps. Each mobile node can be allowed to move along the grid of
horizontal and vertical streets on the map. It provides a pervasive computing service between
portable devices.
3.4. Gauss-Markov Mobility (GMM)
GMM [4] models adopt different levels of randomness through one tuning parameter. In which
each mobile node is initialized by a particular speed and direction. The movement updates the
speed and direction of each mobile node in a fixed interval of time n. The value of speed and
direction of the nth
instance is calculated based on the value of speed and direction of the (n-1)th
instance as follows:
X nsssnsn 1)21()1(1 −−+−+−= ααα (3)
X ndddnd n 1)21()1(1 −−+−+−= ααα (4),
where sn and d n are the new speed and direction of the mobile node at interval n, α is the
tuning parameter to vary the randomness such that 10 ≤≤ α , s and d are constants of
representing speed and direction as α→n ,
Xs n1−
and X nd 1− are random variable derived from
Gaussian distribution. The random values are obtained by setting 0=α and the linear motion is
obtained by setting 1=α . The intermediate randomness is obtained by varying α between 0 and
1 and the new position of the mobile node is calculated as follows:
)1cos(11 d nsnxnxn −−+−= (5)
)1cos(11 d nsnynyn −−+−= (6),
where ),( ynxn and )1,1( ynxn −− are the x and y coordinates of the mobile node positions at
nth and )1( −n th
time intervals respectively.

37
4. AD HOC ON-DEMAND MULTIPATH DISTANCE VECTOR ROUTING
(AOMDV)
The AODV [6] protocol starts a route discovery process through a route request (RREQ) to the
destination throughout the network. Once a non-duplicate RREQ is received, the intermediate
node records the previous hop and checks for a valid and fresh route entry to the destination. The
node sends a route reply (RREP) along with a unique sequence number to the source. On
updating the route information, it propagates the route reply and gets additional RREPs if a RREP
has either a larger destination sequence number (fresher) or a shorter route found.
To eliminate the occurrence of frequent link failures and route breaks in highly dynamic ad hoc
networks, AOMDV has been developed from a unipath path on-demand routing protocol AODV.
The AOMDV [1,3,13] protocol finds multiple paths and this involves two stages which are as
follows: i) A route update rule establishes and maintains multiple loop-free paths at each node,
and ii) A distributed protocol finds link-disjoint paths.
The AOMDV protocol finds node-disjoint or link-disjoint routes between source and destination.
Link failures may occur because of node mobility, node failures, congestion in traffic, packet
collisions, and so on. For finding node-disjoint routes, each node does not immediately reject
duplicate RREQs. A node-disjoint path is obtained by each RREQ, arriving from different
neighbor of the source because nodes cannot broadcast duplicate RREQs. Any two RREQs
arriving at an intermediate node through a different neighbor of the source could not have
traversed the same node. To get multiple link-disjoint routes, the destination sends RREP to
duplicate RREQs regardless of their first hop. For ensuring link-disjointness in the first hop of the
RREP, the destination only replies to RREQs arriving through unique neighbors. The RREPs
follow the reverse paths, which are node-disjoint and thus link-disjoint after the first hop. Each
RREP intersects at an intermediate node and also takes a different reverse path to the source to
ensure link-disjointness.
5. SIMULATIONS MODEL
Figure 1. Overview of the simulation model
The performance of AOMDV is evaluated in terms of Scenario and Traffic patterns using NS 2
[5,8] and Bonn Motion [11]. The following Figure 1. illustrates the simulation model [18] and
the simulation parameters are described in Table 1.
The result of simulation is generated as trace files and the awk & perl scripts are used for report
generation.

38
Table 1. Simulation Parameters
Parameter Value
Simulator NS-2.34
MAC Type 802.11
Simulation Time 100 seconds
Channel Type Wireless Channel
Routing Protocol AOMDV
Antenna Model Omni
Simulation Area 1520 m x 1520 m
Traffic Type CBR(udp), TCP(ftp)
Data Payload 512 bytes/packet
Network Loads 4 packets/sec
Radio Propagation Model TwoRayGround
Interface Queue Length 50
Interface Queue Type DropTail/PriQueue
Number of nodes 25,50,75,100
Interval 1000 sec
Mobility Model Random Way point Mobility, Reference
Point Group Mobility, Manhattan Grid
Mobility, Gauss-Markov Mobility
6. PERFORMANCE METRICS
Performance Metrics [9,15,16] are quantitative measures that can be used to evaluate any
MANET routing protocol. We considered the following six metrics in order to evaluate the multi
path on-demand routing protocol AOMDV in terms of four different scenarios such as RWM,
RPGM, MGM and GMM and also two different traffic patterns such as CBR and TCP traffic
patterns.
6.1. Packet Delivery Fraction (PDF)
PDF is the ratio of data packets delivered to the destination to those generated by the sources and
is calculated as follows:
Packet Delivery Fraction=
Numberof PacketsReceived
Number of PacketsSent
x 100.
6.2. Average Throughput
Average Throughput [17] is the number of bytes received successfully and is calculated by
AverageThroughput=
Numberof bytesreceived x8
Simulationtime x 1000
kbps.

39
6.3. Routing Overhead
Routing overhead is the total number of control packets or routing packets generated by routing
protocol during simulation and is obtained by
Routing Overhead = Number of RTR packets.
6.4. Normalized Routing Overhead
Normalized Routing Overhead is the number of routing packets transmitted per data packet
towards destination and calculated as follows:
Normalized Routing Overhead=
Number of Routing Packets
Number of PacketsReceived
.
6.5. Average End-to-End Delay (Average e2e delay)
Average End-to-End [10] delay is the average time of the data packet to be successfully
transmitted across a MANET from source to destination. It includes all possible delays such as
buffering during the route discovery latency, queuing at the interface queue, retransmission delay
at the MAC (Medium Access Control), the propagation and the transfer time. The average e2e
delay is computed by,
sec,1
)(
m
n
n
i
SiRi
D
∑
=
−
=
where D is the average end-to-end delay, n is the number of data packets successfully transmitted
over the MANET, ' i ' is the unique packet identifier, Ri is the time at which a packet with
unique identifier ' i ' is received and Si is the time at which a packet with unique identifier ' i ' is
sent. The Average End-to-End Delay should be less for high performance.
6.6. Packet Loss
Packet Loss is the difference between the number of data packets sent and the number of data
packets received. It is calculated as follows:
Packet Loss= Number of data packetssent− Numberof data packetsreceived.
7. RESULTS AND DISCUSSION
The performance evaluation of AOMDV protocol is tested in terms of CBR and TCP traffics for
different count of nodes namely 25, 50, 75, and 100 under four different scenarios (MANET
environments) and the results are as follows:

40
7.1. Packet Delivery Fraction
Table 2. Packet Delivery Fraction (%)
No.of
Nodes
Random Way point
Mobility
Reference Point
Group Mobility
Manhattan Grid
Mobility
Gauss-Markov
Mobility
CBR
Traffic
TCP
Traffic
CBR
Traffic
TCP
Traffic
CBR
Traffic
TCP
Traffic
CBR
Traffic
TCP
Traffic
25 26.4838 98.672 92.3698 99.1337 17.1023 99.3025 6.42408 92.1907
50 91.926 98.461 56.5843 99.281 81.4465 99.6809 24.4066 99.356
75 87.9534 98.9128 99.9552 99.2748 68.24 98.4355 87.7569 95.9479
100 69.3412 97.5973 83.0721 99.4681 51.7682 99.1142 53.1239 98.209
Figure 2.1 and Figure 2.2 shows the delivery rate of the data packets of this protocol is some what
significant in CBR traffic in RWM model. However, it is more significant in TCP traffic in
RPGM model (More than 99 percent irrespective of number of nodes).
Figure 2.1. Packet Delivery Fraction of AOMDV with CBR Traffic

41
Figure 2.2. Packet Delivery Fraction of AOMDV with TCP Traffic
7.2. Average End to End Delay
Table 3. Average End to End Delay (in ms)
Figure 3.1 and Figure 3.2 shows the end-to-end delay of this protocol is more significant in both
CBR and TCP traffics in RPGM model.
No.of
Nodes
Random Way point
Mobility
Reference Point
Group Mobility
Manhattan Grid
Mobility
Gauss-Markov
Mobility
CBR
Traffic
TCP
Traffic
CBR
Traffic
TCP
Traffic
CBR
Traffic
TCP
Traffic
CBR
Traffic
TCP
Traffic
25 0.689685 46.6322 12.9071 150.799 13.453 84.74 1.43374 0
50 35.303 102.126 2.90924 122.038 12.4105 9.14614 0.615091 101.84
75 58.8229 46.8142 45.9368 50.1088 60.1209 1576.36 68.8816 133.305
100 93.9716 72.9806 41.2503 40.4549 565.213 91.4236 630.539 1554.73

42
Figure 3.1. Average End-to-End Delay of AOMDV with CBR Traffic
Figure 3.2. Average End-to-End Delay of AOMDV with TCP Traffic

43
7.3. Average Throughput
Table 4. Throughput (in kbps)
No.of
Nodes
Random Way
point Mobility
Reference Point
Group Mobility
Manhattan
Grid Mobility
Gauss-Markov
Mobility
CBR
Traffic
TCP
Traffic
CBR
Traffic
TCP
Traffic
CBR
Traffic
TCP
Traffic
CBR
Traffic
TCP
Traffic
25 24.13 273.67 84.32 468.39 15.84 355.64 10.4 65.74
50 83.49 348.21 51.57 757.62 74.27 908.06 22.33 499.11
75 80.45 216.96 91.31 745.44 62.22 314.05 80.47 162.45
100 154.37 367.34 184.55 895.97 114.56 494.42 118.43 269.08
Figure 4.1 and Figure 4.2 shows the average throughput of this protocol is more
significant in both CBR and TCP traffics in RPGM model. However, it is some what
significant in both CBR and TCP traffics in RWM model.
Figure4.1. Throughput of AOMDV with CBR Traffic
Figure 4.2. Throughput of AOMDV with TCP Traffic

44
7.4. Packet Loss
Table 5. Packet Loss (in pkts)
Figure 5.1 and Figure 5.2 shows the Packet Loss of this protocol is significant in CBR
Traffic in RPGM model and in TCP Traffic in MGM model.
Figure 5.1. Packet Loss of AOMDV with CBR Traffic
Figure 5.1. Packet Loss of AOMDV with TCP Traffic
No.of
Node
s
Random Way
point Mobility
Reference Point
Group Mobility
Manhattan
Grid Mobility
Gauss-Markov
Mobility
CBR
Traffi
c
TCP
Traffic
CBR
Traffi
c
TCP
Traffic
CBR
Traffi
c
TCP
Traffi
c
CBR
Traffi
c
TCP
Traffic
25 1635 90 170 100 1871 61 2083 77
50 179 133 966 134 413 71 1688 79
75 269 58 1 133 707 122 274 168
100 1666 221 918 117 2605 108 2551 120

45
7.5. Routing Overhead
Table-6: Routing Overhead (in pkts)
Figure 6.1 and Figure 6.2 shows the Routing Overhead of this protocol is low in both CBR
and TCP traffics in RPGM model. However, it is some what high in both CBR and TCP
traffics in RWM model.
Figure 6.1. Routing Overhead of AOMDV with CBR Traffic
Figure 6.2. Routing Overhead of AOMDV with TCP Traffic
No.of
Nodes
Random Way point
Mobility
Reference Point
Group Mobility
Manhattan Grid
Mobility
Gauss-Markov
Mobility
CBR
Traffic
TCP
Traffic
CBR
Traffic
TCP
Traffic
CBR
Traffic
TCP
Traffic
CBR
Traffic
TCP
Traffic
25 1568 57 152 4 1757 49 1953 73
50 174 36 933 15 410 19 1627 28
75 250 49 0 15 680 92 264 120
100 1112 176 876 8 2084 95 1722 107

46
7.6. Normalized Routing Overhead
Table 6. Normalized Routing Overhead
No.of
Nodes
Random Way point
Mobility
Reference Point Group
Mobility
Manhattan Grid
Mobility
Gauss-Markov
Mobility
CBR
Traffic
TCP
Traffic
CBR
Traffic TCP Traffic
CBR
Traffic TCP Traffic
CBR
Traffic
TCP
Traffic
25 2.66214 0.008524 0.0738581 0.000349559 4.55181 0.00564256 13.6573 0.080308
50 0.0853778 0.0042308 0.741064 0.000810723 0.226145 0.000856628 2.98532 0.0022973
75 0.127291 0.0092856 0 0.000823859 0.447663 0.0119854 0.13442 0.0301659
100 0.295117 0.0196057 0.194451 0.000365614 0.745351 0.00786164 0.595642 0.0162614
Figure 7.1 and Figure 7.2 shows the Normalized Routing Overhead of this protocol is low in both
CBR and TCP traffics in RPGM model. However, it is some what high in both CBR and TCP
traffics in RWM model.
Figure 7.1. Normalized Routing Overhead of AOMDV with CBR Traffic
Figure 7.2. Normalized Routing Overhead of AOMDV with TCP Traffic

47
8. CONCLUSION
In both CBR traffic and TCP traffic, the AOMDV gives significant performance in RPGM model.
Due to randomness in mobility among mobile nodes, the RWM model is widely used in
MANETs. We have selected AOMDV for evaluation due to its edge over other protocols.
REFERENCES
[1] V.C.Patil, Rajashree, V.Biradar, R.R. Mudholkar, S.R.Sawant, “On-Demand Multipath Routing
Protocols for Mobile Ad Hoc Networks Issues and Comparison”, International Journal of Wireless
Communication and Simulation, 2010,Vol. 2(1), pp. 21-38.
[2] Thomas Clausen, Philippe Jacquet, Laurent Viennot, “Comparative study of CBR and TCP
Performance of MANET routing protocols”, Technical report, Project HiPERCOM, INRIA
Rocquencourt, 2002.
[3] R.L.Lagendijik, J.F.C.M.de Jongh, “Multipath Routing in Mobile Ad Hoc Networks”, Traineeship
Report, Version 1.2, TU-Delft/TNO, 2003.
[4] C.P.Agrawal, O.P.Vyas, M.K.Tiwari, “Evaluation of varying mobility models & network loads on
DSDV protocol of MANETs”, International Journal of Computer Science and Engineering, 2009,
Vol. 1(2), pp. 40-46.
[5] The Network Simulator ns-allinone-2.34, http://www.isi.edu/nsnam/ns/
[6] Elizabeth M. Royer, C-K Toh, “A Review of Current Routing Protocols for Ad-Hoc Mobile Wireless
Networks”, IEEE Personal Communications, April 1999, pp.46-55.
[7] Sapna S. Kaushik, P.R.Deshmukh, “Comparison of effectiveness of AODV, DSDV and DSR Routing
Protocols in Mobile Ad hoc Networks”, International Journal of Information Technology and
Knowledge Management, 2009, Vol. 2(2), pp.499-502.
[8] Kevin Fall, K. Varadhan, "The ns Manual", University of Southern California, Information Sciences
Institute (ISI), http://www.isi.edu/nsnam/ns/ns-documentation.html.
[9] Harminder S. Bindra1, Sunil K. Maakar, A. L. Sangal , “Performance Evaluation of Two Reactive
Routing Protocols of MANET using Group Mobility Model”, IJCSI International Journal of
Computer Science Issues, 2010,Vol. 7(10), pp.38-43.
[10] Anuj K. Gupta, Harsh Sadawarti, Anil K. Verma, “Performance analysis of AODV, DSR & TORA
Routing Protocols”, IACSIT International Journal of Engineering and Technology, 2010, Vol.2 (2),
pp.226-231.
[11] The Bonn Motion, http://bonnmotion.cs.uni-bonn.de/
[12] Mohd Izuan Mohd Saad, Zuriati Ahmad Zukarnain, “Performance Analysis of Random-based
Mobility Models in MANET Protocols”, European Journal of Scientific Research, 2009, Vol. 32(4),
pp. 444-454.
[13] Sujata V.Mallapur, Sujata .Terdal, Enhanced, “Ad-Hoc on Demand Multipath Distance Vector
Routing Potocol (EAOMDV)”, International Journal of Computer Science and Information Security,
2010,Vol. 7 (3), pp.166-170.
[14] Vikas Singla, Parveen Kakkar,“Traffic Pattern based performance comparison of Reactive and
Proactive protocols of Mobile Ad-hoc Networks”, International Journal of Computer Applications,
2010,Vol. 5(10), pp.16-20.

48
[15] S. R. Das, R. Castaeda, J. Yan, “Simulation-based performance evaluation of routing protocols for
mobile ad hoc networks”, Mobile Networks and Applications, 2000,Vol. 5, pp.179–189.
[16] S.Corson, J.Macker, “Routing Protocol Performance Issues and Evaluation Considerations”,
RFC2501, IETF Networking Group, January 1999.
[17] A.Valarmathi, RM.Chandrasekaran, “Congestion Aware and Adaptive Dynamic Source Routing
Algorithm with Load Balancing in MANETs”, International Journal of Computer Applications, 2010,
Vol. 8(5), pp.1-4.
[18] NS-2 with Wireless and Mobility Extensions, http://www.monarch .cs.cmu.edu.

PERFORMANCE EVALUATION OF AOMDV PROTOCOL BASED ON VARIOUS SCENARIO AND TRAFFIC PATTERNS

More Related Content

What's hot (19)

Similar to PERFORMANCE EVALUATION OF AOMDV PROTOCOL BASED ON VARIOUS SCENARIO AND TRAFFIC PATTERNS (20)

Recently uploaded (20)

PERFORMANCE EVALUATION OF AOMDV PROTOCOL BASED ON VARIOUS SCENARIO AND TRAFFIC PATTERNS