Performance Evaluation and Comparison of Ad-Hoc Source Routing Protocols

THE PROCEEDINGS OF IEEE SPONSORED INTERNATIONAL CONFERENCE RASIET-07
IET ALWAR (RAJ) 22nd
– 23rd
DEC 07. RASIET 07/COMPUTING
166
Performance Evaluation and Comparison of Ad-Hoc
Source Routing Protocols
Narendra Singh Yadav* R.P.Yadav**
Abstract
Mobile ad hoc network is a reconfigurable network of mobile nodes connected by multi-hop
wireless links and capable of operating without any fixed infrastructure support. In order to
facilitate communication within such self-creating, self-organizing and self-administrating
network, a dynamic routing protocol is needed. The primary goal of such an ad hoc network
routing protocol is to discover and establish a 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. This paper examines two routing protocols,
both on-demand source routing, for mobile ad hoc networks– the Dynamic Source Routing (DSR),
an flat architecture based and the Cluster Based Routing Protocol (CBRP), a cluster architecture
based and evaluates both routing protocols in terms of packet delivery fraction normalized
routing load, average end to end delay by varying speed of nodes, traffic sources and mobility.
Keywords: Mobile ad hoc networks; Routing protocols; Dynamic source routing; Cluster based
routing protocol; Simulation; Performance evaluation
1. Introduction
Mobile ad hoc networks are formed by
autonomous system of mobile nodes that
utilize multi-hop radio relaying and connected
by wireless links without any preexisting
communication infrastructure or centralized
administration. Such networks are also known
as infrastructure less or multi-hop wireless
networks.
Communication is directly between nodes or
through intermediate nodes acting as routers.
Such network requires each mobile host to be
*Asst prof. Department of Electronics &
Communication Engineering
narensinghyadav@yahoo.com
** Dr. R.P. Yadav, Malaviya National Institute of
Technology, Jaipur,
rp_yadav@yahoo.com
more intelligent so that it can perform both
functions of transmitting and receiving of data
as a host and of forwarding packets for other
mobile host as a router. The advantages of such
a network are rapid deployment, robustness,
flexibility and inherent support for mobility.
Ad hoc networks, due to their quick and
economically less demanding deployment, find
applications in military operations,
collaborative and distributed computing,
emergency operations, wireless mesh networks,
wireless sensor networks and hybrid networks.
Due to dynamic topology, lack of fixed
infrastructure and frequent link failure the
traditional routing for wired networks cannot
be directly applied to mobile ad hoc networks
because wired routing methods assume the
network to be stable and routing overhead to be
almost negligible. They either lack the ability
to adapt to the dynamic topology change of
mobile ad hoc networks or may cause large

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167
overhead that degrades network performance.
Therefore, routing is the most studied problem
in mobile ad hoc networks and a number of
routing protocols have been proposed [1-13],
derived from either distance-vector [14] or
link-state [15] based on classical routing
algorithms.
Routing protocols for Mobile ad hoc networks
can be classified into two main categories:
Proactive or table driven routing protocols and
Reactive or on-demand routing protocols. In
proactive protocols, every node maintains the
network topology information in the form of
routing tables by periodically exchanging
routing information. They include the
Destination Sequenced Distance Vector
(DSDV) [2], the Wireless Routing Protocol
(WRP) [3], Source-Tree Adaptive Routing
(STAR) [5] and Cluster-head Gateway Switch
Routing protocol (CGSR) [4]. On the other
hand, reactive protocols obtain routes only on
demand, which include the Dynamic Source
Routing (DSR) protocol [6], the Ad hoc On-
demand Distance Vector (AODV) protocol [7],
the Temporally Ordered Routing Algorithm
(TORA) [8], and the Associativity Based
Routing (ABR) protocol [10].
A crucial issue for mobile ad hoc networks is
the handling of a large number of nodes. As
more nodes join a mobile ad hoc network,
contention is more likely and the open nature
of a mobile ad hoc network makes it important
that a network continues to operate even if
there are more nodes involved Most of the
previous work [16-23] is limited on performing
simulations for ad hoc networks with a limited
number of nodes (50-100 nodes) deployed in
small geographical areas. The main reason is
that simulations with many nodes, spread in a
large area, need too much time to be completed
using common simulators such as ns-2.
Nowadays, the ad hoc network technology
becomes more and more popular and, as a
result, large-scale ad hoc networks may be
deployed in battlefields, regions of disaster and
large towns.
The rest of the paper is organized as follows:
Section II provides an overview of the routing
protocols used in the study. The simulation
environment and performance metrics are
described in Section III and then the results are
presented in Section IV. Finally Section V
concludes the paper.
2. Overview Of DSR And CBRP
As each protocol has its own merits and
demerits, none of them can be claimed as
absolutely better than others. Two mobile ad
hoc routing protocols – the Dynamic Source
Routing (DSR), the flat architecture based On-
Demand source routing protocol and the
Cluster Based Routing Protocol (CBRP), the
cluster architecture based On-Demand source
routing protocol are selected for study.
Dynamic Source Routing protocol (DSR)
The Dynamic Source Routing Protocol [15] is
an on-demand routing protocol designed to
restrict the bandwidth consumed by control
packets by eliminating the periodic table-
update messages required in the table-driven
approach. It is beacon-less and hence does not
require periodic hello packet transmissions,
which are used by a node to inform its
neighbors of its presence. The key
distinguishing feature of DSR is the use of
source routing. That is, the sender knows the
complete hop-by-hop route to the destination.
These routes are stored in a route cache. The
data packets carry the source route in the
packet header.
When a node in the ad hoc network attempts to
send a data packet to a destination for which it
does not already know the route, it uses a route
discovery process to dynamically determine
such a route. Route discovery works by

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flooding the network with route request
(RREQ) packets. Each node receiving an
RREQ rebroadcasts it, unless it is the
destination or it has a route to the destination in
its route cache. Such a node replies to the
RREQ with a route reply (RREP) packet that is
routed back to the original source. RREQ and
RREP packets are also source routed. The
RREQ builds up the path traversed across the
network. The RREP routes back to the source
by traversing this path backward. The route
carried back by the RREP packet is cached at
the source for future use. If any link on a
source route is broken, the source node is
notified using a route error (RERR) packet.
The source removes any route using this link
from its cache. A new route discovery process
must be initiated by the source if this route is
still needed. DSR makes very aggressive use of
source routing and route caching. No special
mechanism to detect routing loops is needed.
Also, any forwarding node caches the source
route in a packet it forwards for possible future
use.
Cluster Based Routing Protocol (CBRP)
In CBRP [16] the nodes of a wireless network
are divided into clusters. The diameter of a
cluster is only two hops and clusters can be
disjoint or overlapping. Each cluster elects one
node as the clusterhead, responsible for the
routing process. The head of a cluster knows
the addresses of its members. Clusterheads
communicate with each other through gateway
nodes. A gateway is a node that has two or
more clusterheads as its neighbors when the
clusters are overlapping or at least one
clusterhead and another gateway node when
the clusters are disjoint.
The routing process works in two steps. First, it
discovers a route from a source node S to a
destination node D, afterwards it routes the
packets. When a source has to send data to
destination, it floods route request packets (but
only to the neighboring cluster-heads). On
receiving the request a cluster head checks to
see if the destination is in its cluster. If yes,
then it sends the request directly to the
destination else it sends it to all its adjacent
cluster-heads. The cluster-heads address is
recorded in the packet so a cluster-head
discards a request packet that it has already
seen. When the destination receives the request
packet, it replies back with the route that had
been recorded in the request packet. If the
source does not receive a reply within a time
period, it backs off exponentially before trying
to send route request again. In CBRP, routing
is done using source routing. It also uses route
shortening that is on receiving a source route
packet, the node tries to find the farthest node
in the route that is its neighbor and sends the
packet to that node thus reducing the route.
While forwarding the packet if a node detects a
broken link it sends back an error message to
the source and then uses local repair
mechanism.
3. Simulation and Performance
Metrics
A. Simulation model
Network Simulator2 (NS-2) [26] a object-
oriented, discrete event driven network
simulator developed at UC Berkely written in
C++ and OTcl, particularly popular in the ad
hoc networking research community is used for
the simulations. The traffic sources are CBR
(continuous bit – rate). The source-destination
pairs are spread randomly over the network.
The node movement generator of ns-2 is used
to generate node movement scenarios. The
movement generator takes the number of
nodes, pause time, maximum speed, field
configuration and simulation time as input
parameters. The propagation model is the Two
way ground model [27]. Each data point in the
following figures is the average of 5 runs each
lasting for 300s of the simulated time with the

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same scenario configuration but different
random seeds.
Performance metrics
In order to compare the performance of cluster
architecture based, CBRP and flat architecture
based, DSR this paper focus on the following
performance metrics for evaluation:
Packet Delivery Fraction: The ratio of the data
packets delivered to the destinations to those
generated by the sources.
Average end-to-end delay: This includes all
possible delays caused by buffering during
route discovery latency, queuing at the
interface queue, retransmission delays at the
MAC, and propagation and transfer times.
Normalized routing load: The number of
routing packets transmitted per data packet
delivered at the destination.
results
The simulation results are shown in the
following section in the form of line graphs.
Graphs show comparison between the two
protocols on the basis of the above-mentioned
metrics as a function of node density in high
mobility (pause time = 0s) and stationary
(pause time = 300s) scenarios with 30%, 70%
traffic sources.
Packet delivery fraction
Fig.1 shows the packet delivery fraction as a
function of both speed and different number of
traffic sources. Each graph shows the PDF of
both the protocols, DSR and CBRP, in a high
mobility (pause time = 0s) and stationary
(pause time = 300s) scenarios.
0
10
20
30
40
50
60
70
80
5 10 15 20 25
Speed (m/s)
Packetdeliveryfraction(%)
DSR, p = 0s CBRP, p = 0s
DSR, p = 300s CBRP, p = 300s
(a) 30% sources
0
5
10
15
20
25
30
35
40
45
5 10 15 20 25
Speed (m/s)
Packetdeliveryfraction(%)
DSR, p = 300s CBRP, p = 300s
(b) 70% sources
Figure1. Packet delivery fraction as a function of
speed and 30%, 70% traffic sources.
In high mobility (p = 0s) scenarios, the packet
delivery fraction of DSR improves up to S = 10
m/s after that PDF degrades from 64.05% at S
= 10 to 25.85% at S = 25 m/s. as the speed of
nodes increases. The PDF is maximum at S =
10 m/s for DSR. In CBRP packet delivery
improves from 62.77% at S = 5 to 69.83% at S
= 15 m/s after that PDF degrades as the speed
increases. CBRP depicts maximum PDF
(69.83%) at S = 15 m/s. In static scenarios (p =
300s) with 30% CBR sources, DSR performs
better than CBRP.
However in stationary (p = 300s) scenarios
with 70% CBR sources, both the protocols
have nearly the same PDF. However this is not
true for high mobility (p = 0s) scenarios. In
high mobility scenarios CBRP performs better
than DSR and with S = 10 or more CBRP
clearly outperforms DSR at a factor of ~ 2.
DSR has a lower PDF than CBRP in high
mobility (0s pause time) scenarios and more or
less same PDF in stationary (300s pause time)
scenarios. The PDF due to 30% traffic sources
is better than that due to 70% traffic sources.
The performance degradation in PDF is due to
packet drops by the routing algorithm after
being failed to transfer data in the active routes.
The packet drops are due to network
partitioning, link break, collision and
congestion in the ad hoc network

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170
Average end to end delay Fig. 2 shows the
average end to end delay of both the protocols,
DSR and CBRP, as a function of both nodes
speed- 5, 10, 15, 20 and 25 m/s and different
number of traffic sources- 30% and 70% CBR
sources. Each chart shows average end-to-end
delay of both the protocols in a high mobility
(p = 0s) and a stationary (p = 300s) scenarios
(a) 30% sources
0
1
2
3
4
5
6
7
8
9
5 10 15 20 25
Speed (m/s)
Averageendtoenddelay
DSR, p = 300s CBRP, p = 300s
(b) 70% sources
Figure2. Average end-to-end delay as a function of
speed and 30%, 70% traffic sources.
In high mobility (p = 0s) scenarios with 30%
CBR traffic sources, average end to end delay
for DSR is more than CBRP up to S = 10 m/s.
At speed equal to 15 m/s both the protocols
have nearly comparable PDF after the DSR
performs better than CBRP. For stationary (p =
300s) scenarios, average end-to-end delay is
less than 4s for both the protocols except at S =
15 where DSR performs better than CBRP.
In high mobility (p = 0s) scenarios with 70%
traffic sources, both the protocols have nearly
same average end to end delay up to S = 15 m/s
after that the average delay for DSR decreases
where as the average delay for CBRP
increases. In stationary (p = 300s), CBRP
performs better than DSR at all speeds except
at 20 m/s where both protocols have same
delay.
Normalized routing load
Fig. 3 shows the normalized routing load as a
function of both nodes density and different
number of traffic sources. Each graph shows
the normalized routing load of both the
protocols, DSR and CBRP, in a high mobility
(pause time = 0s) and stationary (pause time =
300s) scenarios.
0
5
10
15
20
25
5 10 15 20 25
Speed (m/s)
Normalizedroutingload
DSR, p = 300s CBRP, p = 300s
(a) 30% sources
0
10
20
30
40
50
60
70
5 10 15 20 25
Speed (m/s)
Normalizedroutingload
DSR, p = 300s CBRP, p = 300s
(b) 70% sources
Figure3. Normalized routing load as a function of 5,
10, 15, 20, 25 m/s and 30%, 70% traffic sources.
In high mobility (p = 0s) with 30% traffic
sources, both the protocols have nearly same
NRL at S = 5 m/s but as the speed of node
increases, the NRL for DSR grows and at S =
25 m/s NRL is about 24. CBRP exhibits less
NRL than DSR for all speeds and it is well
below 2. In static (p = 300s) scenarios both the
protocols show nearly comparable NRL (well
below 2).
4. Conclusions
This paper compared the performance of DSR
and CBRP, two on-demand source routing
protocols for ad hoc wireless networks. DSR
and CBRP both use on-demand route
discovery, but with different flooding behavior.
In particular, DSR use network-wide flooding
0
1
2
3
4
5
6
7
5 10 15 20 25
Speed (m/s)
Averageendtoenddelay
DSR, p = 300s CBRP, p = 300s

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for route discovery and does not depend on any
periodic hello message or timer-based
activities. CBRP, on the other hand, only
broadcasts route requests to cluster heads,
largely reducing the network traffic. Hello
messages are the integral part of CBRP and the
size of a hello message may be large as it
contains the neighbor table and cluster
adjacency table of the sender. As a result, while
CBRP uses hello messages to establish clusters
and in turn reduce the flood in route discovery,
the hello message itself is another kind of
overhead. The general observation from the
simulation is that in static scenarios, due to
similar performance any, either DSR or CBRP,
can be used in large ad hoc wireless networks
but in high mobility scenarios, CBRP
outperforms DSR.
References
1. E. M. Royer and C. K. Toh, “A review of current
routing protocols for ad hoc mobile wireless
networks,” IEEE Personal Communications
magazine, April 1999, pp. 46–55.
2. C. E. Perkins and P. Bhagwat, “Highly Dynamic
Destination-Sequenced Distance-Vector Routing
(DSDV) for Mobile Computers,” in Proceedings of
ACM SIGCOMM 1994, August 1994, pp. 234-244.
3. S. Murhty and J. J. Garcia-Luna-Aceves, “An
Efficient Routing Protocol for Wireless Networks,”
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4. C. C. Chiang, H. K. Wu, W. Liu and M. Gerla,
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5. J. J. Garcia-Luna-Aceves and M. Spohn, “Source-
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Routing in Ad Hoc Wireless Networks,” Mobile
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Demand Distance Vector Routing,” Proceedings of
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Applications 1999, February 1999, pp. 90-100.
8. V. D. Park and M. S. Corson, “A Highly Adaptive
Distributed Routing Algorithm for Mobile Wireless
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9. Y. Ko and N. H. Vaidya, “Location-Aided Routing
(LAR) in Mobile Ad Hoc Networks,” in Proceedings
of ACM MOBICOM 1998, October 1998, pp. 66-75.
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36.
11. P. Sinha, R. Shivkumar and V. Bharghavan,
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Routing Algorithm,” IEEE Journal on Selected
Areas in Communications, vol. 17, no. 8, August
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Dr. R.P.Yadav received M.Tech.
degree from Indian Institute of
Technology, Delhi, India in
Integrated Electronics and Circuits in
1987 and completed Ph.D in
Communication Engineering from
University of Rajasthan in 2001. He
is currently working as Professor &
Head, Department of Electronics and Communication
Engineering at Malaviya National Institute of
Technology, Jaipur, India. He is an active member of
various professional bodies and has organized many
workshops, conferences and seminars. His research
interests include MIMO and Ad hoc Networking,
Microstrip Antennas, coding and Digital Communication
Systems.
Narendra Singh Yadav received
M.Tech. degree in Computer
Science from Birla Institute of
Technology, Ranchi, India in 2002.
He is a Ph.D student at Malaviya
National Institute of Technology,
Jaipur, India. His research interests
include Clustering, Routing and
Security in ad hoc wireless networks, wireless sensor
networks and wireless hybrid networks.

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