PERFORMANCE ANALYSIS OF ANTHOCNET ROUTING PROTOCOL FOR HYBRID AD HOC NETWORK

International Journal of Multidisciplinary Consortium
Volume – 2 | Issue – 1 | March 2015
ijmc.editor@rtmonline.in| http://ijmc.rtmonline.in | ISSN 2349-073X
P u b l i s h e d b y : M o d e r n R o h i n i E d u c a t i o n S o c i e t y | P a p e r I d : 0 4 3 0 6 0 Page 228
PERFORMANCE ANALYSIS OF ANTHOCNET ROUTING PROTOCOL FOR
HYBRID AD HOC NETWORK
by
Khushboo Gupta | Ph.D. Research Scholar of CSE | khushboogupta2806@gmail.com |
UPTU, Lucknow, India
&
Prof. (Dr.) K. P. Yadav | Director, Department of CS | drkpyadav732@gmail.com |
SDIT, Ghaziabad, INDIA
ABSTRACT
Mobile Ad hoc Networks (MANETs) are communication networks which consist of
wireless nodes, placed together in an ad hoc manner, i.e. with minimal prior planning. The
random node movement unpredictable behavior makes the topology very dynamic in
nature. MANETs poses substantially different challenges to routing protocols than more
traditional wired networks. The classification of MANETs protocols are Proactive based,
Reactive based or Bio-inspired routing protocols. AntHocNet is a bio-inspired routing
protocol based on ant colony optimization (ACO) which has many parallels with biology
thus the solutions of biology can also be used to solve the problems of computer networks.
This paper discusses the implementation and performance analysis of the AntHocNet
algorithm which is based on the nature-inspired Ant Colony Optimization framework for
routing in mobile MANETs. AntHocNet is an adaptive hybrid algorithm which combines
the reactive route setup process with the proactive maintenance process. The reactive
route setup is carried out at the start of a communication session or whenever the source of
a current session has no more routing information available for the destination. The
proactive route maintenance is run for the entire duration of the session. Its aim is to keep
information about existing routes up to date and explore new routes. During simulation,
the performance of AntHocNet is compared with DSR and AODV routing protocols.
KEYWORDS: ACO, Hybrid Routing Algorithm, AntHocNet, DSR, AODV, NS2.
INTRODUCTION
Over the last three decades there has been such tremendous growth in the field of
computer networks that it has paved a way for a wireless era from a wired one. Wireless
networks can find their applications in many fields such as in military, emergency
operations, radio satellites, wireless mesh networks, wireless sensor network are among
the few. To support this new communication paradigm, robust, reliable and efficient
algorithms are needed to allow network to offer a good or at least unacceptable level of
service. Hence, routing is one of the primary functions which each node has to perform in
order to have fully functional network. Routing in such type of networks is major research
issue and many proposals have appeared within its scope. The routing protocols are
divided into three categories [1]. Firstly the proactive protocols like DSDV [2], OLSR [3],
reactive protocols like AODV [4] and hybrid protocols like TORA, ZRP [5], and

MPOLSR [6]. Another most important type of protocols in recent times is the Bio-inspired
protocols. Bio-inspired protocols are found to be capable of demonstrating self-organizing
behavior due to their robustness and efficiency; examples of such protocols are
AntHocNet, BeeAdHoc [7], and ANSI [8].
AntHocNet follows hybrid approach unlike other bio-inspired algorithms. Most of the
previous bio-inspired algorithms were adopting a proactive scheme by periodically
generating ant-like agents for all possible destinations, while AntHocNet generates ants
according to both proactive and reactive schemes. The fact that AntHocNet learns with the
system and applies it to the environment makes it better than the existing algorithms. Thus
AHN has been used for path selection in the ever changing network scenario. This will
lead to better network performance as compared to the existing routing protocols. This
paper discusses the results of simulation conducted on AntHocNet algorithm, whose
design is based on a self-organizing behavior of ants, Ant Colony Optimization and on
shortest path discovery.
The objective of this paper is to find an efficient solution for Packet Delivery Ratio, End to
End Delay and Throughput which involves evaluation and comparison of the three
algorithms namely, AntHocNet (AHN), Ad hoc On-demand Distance Vector Routing
(AODV) and Dynamic Source Routing (DSR) for wireless ad-hoc networks. The paper is
organized as follows: Section II addresses the work already carried out in this field.
Section III gives the Literature Survey relevant to our work. Section IV gives a brief
discussion on system design and implementation followed by simulation results in Section
V. Section VI gives the conclusions and future works enhancement possible.
RELATED WORK
By taking a quite different approach, insect societies [9, 10] have become source of
inspiration for routing nowadays. The Ant Colony Optimization (ACO) algorithms derive
their motivation from this nature concept i.e. the behavior of social insects and ant
colonies. Ant Colony Optimization is a population-based, general search technique for the
solution of difficult combinatorial problems which is inspired by the pheromone trail
laying behavior of real ant colonies. Based on ACO approach many routing algorithms
have been generated for wired and wireless networks. The shortest path finding process is
highly distributed and self-organized, adaptive, scalable and robust.
As it can be easily observed, real ants can converge on the shortest path that connects their
nest to source of food. When an ant travels they use what is known as pheromone.
Pheromone is a volatile chemical substance which is secreted by ants. As a result of this, a
path from nest to the food source is created which resembles a network path in real world.
During the next cycle all ants take the path of the pheromone which is deposited by the
first ant. On their way these ants also deposit their pheromone. The path attracts more ants
will experience an increasing level of pheromones, until the majority of the ants converge
on the shortest path. The path with the highest pheromone content is chosen as the optimal
path. Higher pheromone content accounts for higher bandwidth in that path. An optimal

path is obtained based on the bandwidth parameter. The selected path will be followed by
all ants. This leads to the derivation of the optimal path. This indirect form of
communication used by ants to find the shortest path is known as Stigmergy.
In literature there exists a large family of ad hoc routing protocols [11]. But there is a need
for new routing protocols for specific mobile Ad hoc networks. Ant-colony-based routing
algorithm (ARA) works in an on-demand way in which ant set-up multiple paths between
source and destination at the start of a data session [12]. Ant-AODV [13] is a hybrid
algorithm combining ants with the basic AODV protocol. In this there are some fixed
numbers of ants which keeps going around the network in a random manner and
proactively updating the AODV routing tables in the nodes they visit whenever possible.
Probabilistic emergent routing algorithm (PERA) works in an on-demand way in which
ants being broadcast towards the destination at the start of a data session. Multiple paths
are set up, but only the one with the highest pheromone value is used by data (the other
paths are also available for backup) [14]. There are many other ACO routing algorithms
which have been proposed for MANETs. Most of them move quite far away from the
original ACO routing ideas in-order to obtain the efficiency needed in MANETs, and
many of them are almost same as single-path on-demand algorithms. AntHocNet has been
designed based on the Ant Colony Optimization (ACO) framework, and its general
architecture shares strong similarities with the architectures of typical ACO
implementations for network routing [15].
LITERATURE SURVEY
AntHocNet is a multipath routing algorithm for mobile ad-hoc networks which combines
both proactive and reactive phases. AntHocNet is the hybrid algorithm which includes the
concept of Ad Hoc networks and AntNet [16]. It is based on AntNet [17], [18], which is
basically designed for wired networks, with some modifications to be used on ad-hoc
networks. AntHocNet emerges as a reactive, adaptive, multipath and proactive algorithm
[19].
During reactive path setup, it has agents operating on-demand to set up routes to
destinations. It does not maintain paths to all destinations at all times, but sets up paths
whenever required. In reactive path setup phase, ant agents called reactive forward ants are
launched by the source in order to find multiple paths to the destination, and backward
ants return to set up the paths. The paths are represented in pheromone tables indicating
their respective quality. In case of broadcasting the nodes receives number of ants. Here
the node compares the path travelled by each new ant to that of former received ants of
this generation. It rebroadcasts only if its number of hops and travel time are both within
an acceptance factor of best forward ant. After path setup, data packets are routed over the
different paths using these pheromone values combined with small probability value at
each node. While the data session is active, paths are monitored, maintained and improved
proactively using different agents, called proactive forward ants.

Ad-hoc on demand distance vector (AODV) [20] is a reactive routing protocol and routes
are determined when needed. Hello messages may be used to detect the neighbor links. An
active node broadcasts a hello message to all its neighbor so that they can receive. If a
node fails to receive several hello messages from a neighbor, a link break is detected.
Whenever a source has a data to send to an unknown destination, it broadcasts a route
request for that destination. At each intermediate node when a reply request is received, a
route to the source has been created. If the accepting node did not receive this reply earlier,
says that it is not the destination and does not have a current route to the destination, then
it rebroadcasts the request. If the receiving node is the destination, then the current route to
the destination, it generates a route reply. The route reply is unicast in hop by hop fashion
to the source control messages and hello messages.
Dynamic Source Routing (DSR) is a routing protocol for wireless mesh networks. This
protocol is based on source routing whereby all the routing information is maintained at
mobile nodes. It has only two major phases, which are Route Discovery and Route
Maintenance. Route Reply would only be generated if the message has reached the
intended destination node. The DSR protocol is composed of two mechanisms that work
together to allow the discovery and maintenance of source routes in the ad hoc network:
Route Discovery is the mechanism by which a node S wishing to send a packet to a
destination node D obtains a source route to D. Route Discovery is used only when S
attempts to send a packet to D and S has no prior knowledge about the route to D. Route
Maintenance is the mechanism by which node S is able to detect, while using a source
route to D. If the route to D is no longer used it indicates that the network topology has
changed or the link along the route no longer exists. When Route Maintenance indicates a
source route is broken, S can attempt to use any other route it happens to know to D, or
can invoke Route Discovery to find again a new route. Route Maintenance is used only
when S is actually sending packets to D [21].
SYSTEM DESIGN AND IMPLEMENTATION
A. SIMULATOR CHOSEN
We have chosen to work with NS2 [22]. NS2 is available under Linux, with a GPL license.
Some standard algorithms are already implemented in this simulator, and DSR is one of
these.NS2 is a network simulator; built with C++ and TCL. As every simulator, the main
purpose is to simulate different networks, to test different protocols, and to find the
limitations of each. It has been developed in the California University, by LBL, Xerox
PARC, UCB, and USC/ISI through the VINT project supported by DARPA.
The simulator is composed of two parts:
 The TCL code: it is used to communicate with the simulator, and permits to define
different simulation parameters
 The C++ code: it is the main part of the project, because it defines how the
simulator has to behave.

B. ALGORITHM CHOSEN
The implementation part is an important part of the project. By implementing the different
solutions, we can test them, find some improvements and understand why one works better
than another.
One representative algorithm from traditional and bio-inspired algorithms each was chosen
for comparison. This would give a broad picture of which type of the chosen algorithm
performs well in which environment. The specific algorithms chosen within each category
were DSR and AODV for traditional and AntHocNet for hybrid.
C. NETWORK SCENARIO
In order to do conduct the tests in a controlled way, we define a common scenario for both
DSR, AODV and AntHocNet, by varying relevant parameters such as the terrain size, rate
of data sent max-speed, packet size node density and pause time. Some tools have been
developed to build these scenarios. For example, if we want to have a random model with
several nodes, it is possible to use ’setdest’. It is a tool that generates random positions and
random speeds for a number of nodes. By doing this, it is easy to use different random
models and to test a protocol.
D. EVALUTION PARAMETER
Below are the parameters used to evaluate the performance of DSR, AODV and
AntHocNet:
 A choice of 50 random moving nodes on a squared 1500 m by 300 m area has
been used.
 Mobility model for nodes as the random waypoint propagation mobility model.
 Simulation time is 250s.
 Data traffic is generated by constant bit rate (CBR) sessions
 Radio propagation, we use two-ray signal propagation model.
E. EVALUTION PARAMETER
1) Packet delivery fraction - Ratio of data packets received by the destinations to the
packets sent by the source. (Number of packet receives / number of packet sends)
2) Average end-to-end delay of data packets - The time taken for the packet to
reach the destination, it includes queuing at the interface queue, delay during route
discovery (ARP) (sum of delay experienced by each packet of the flow)/number of
packets).
3) Throughput –It is the amount of data transferred successfully over a link from one
end to another in a given period of time. (Number of bits transferred /Observation
duration)
SIMULATION AND RESULTS
Simulation is carried with nodes randomly distributed in the terrain size of 1500 × 300. A
constant bit rate of 0.1Mbytes with a packet size of 1500 Bytes is used. The simulation
time is set to 250s. To cover all types of scenario the algorithm may face, we have varied,
node density (number of nodes) and the node mobility (pause time and rwp max-speed).
The node density (number of nodes) was varied in the range [20, 100] in steps of 20 (5

different node densities). Pause time was varied in the range [0, 100] in steps of 20 (6
different pause times). RWP max-speed was varied in the range [20,100] in steps of 20 (5
different scenarios). Tables I, II and III shows the analysis of routing metrics for varying
speed, pause time and nodes after simulation.
Table I: Packet Delivery Ratio, End To End Delay And Throughput Analysis For Varying Speed
Speed P D R E 2 E
Delay
Throughpu
t
AntHocNet
20 28.81 836.17 29.49
40 17.99 1546.21 18.35
60 28.73 895.96 29.35
80 17.05 656.98 17.36
100 22.09 1083.29 22.47
AODV
20 46.24 664.73 38.54
40 31.52 1282.83 29.11
60 30.37 837.82 29.98
80 16.76 705.28 15.42
100 16.99 1244.12 15.73
DSR
20 36.55 751.27 35.13
40 30.19 1371.08 27.29
60 29.92 911.74 27.39
80 18.83 794.28 16.64
100 18.44 1316.65 17.97
Table Ii: Packet Delivery Ratio, End To End Delay And Throughput Analysis For Varying Pause Time
Pause Time P D R E 2 E
Delay
Throughpu
t
AntHocNet
0 22.01 1160.49 22.57
20 23.66 3266.49 24.05
40 31.72 1347.67 32.25
60 28.20 2999.87 28.35
80 32.86 2450.96 33.39
100 83.63 41.51 21.44
AODV
0 31.54 980.66 30.04
20 20.03 2883.63 29.74
40 31.21 1322.43 32.64
60 20.02 3001.73 19.07
80 24.67 2666.32 30.02
100 70.06 45.63 15.84
DSR
0 29.71 1005.49 25.77
20 21.46 3199.49 24.95
40 30.22 1337.67 30.25
60 18.31 3005.11 32.99
80 22.83 2692.89 28.98
100 68.63 48.563 13.78

Table IIi: Packet Delivery Ratio, End To End Delay And Throughput Analysis For Varying Nodes
Nodes P D R E 2 E
Delay
Throughpu
t
AntHocNet
20 67.67 57.03 69.01
40 30.76 397.84 30.99
60 20.49 1265.84 20.89
80 8.83 699.62 9.34
100 11.20 3171.06 11.39
AODV
20 88.54 48.93 87.34
40 40.03 365.55 39.62
60 21.73 1239.44 20.73
80 5.55 738.64 6.74
100 9.22 3254.33 8.53
DSR
20 85.87 50.33 79.51
40 38.34 377.01 37.56
60 19.22 1333.11 18.22
80 5.43 745.21 6.14
100 8.65 3986.22 8.11
A. AVERAGE PACKET DELIVERY RATIO
It has been seen from Figures 1, 2 and 3 that at lesser number of nodes and at low node
mobility AntHocNet has lesser packet delivery ratio than AODV and DSR. It was
observed that the data packet delivery in AntHocNet takes some time as ant packets are
large in size and consumes bandwidth. But as the node density increases the Packet
Delivery Ratio decreases with the increase in error rate in AODV and DSR. It was
observed that AntHocNet is relatively consistent and stable as compared to AODV and
DSR.
Fig. 1 Packet Delivery Ratio vs. Speed

Fig. 2 Packet Delivery Ratio vs. Pause Time
Fig. 3 Packet Delivery Ratio vs. Number of Nodes
B. AVERAGE END TO END DELAY
It is observed from Figure 4, 5 and 6 that AODV and DSR have lower average end-to-end
delay when compared to AntHocNet at lower node density and lower node mobility.
Moreover the results for average end-to-end delay reflect the increasing level of difficulty
due to which the delay increases with increasing node speeds and node densities in AODV
and DSR. Though AntHocNet utilizes more bandwidth than AODV and DSR as the
number of nodes in the simulation is increased, AntHocNet has lesser overhead than DSR.

Fig. 4 End to End Delay vs. Speed
Fig. 5 End to End Delay vs. Pause Time
Fig. 6 End to End Delay vs. Number of Nodes

C. THROUGHPUT
From the Figure 7, 8 and 9 below, it can be seen that AODV and DSR has greater
throughput compared to AntHocNet at low speed and mobility. It can be accounted to the
fact that as nodes become more dynamic, the route discovery process generates more
routing traffic. Therefore less of the channel will be used for data transfer, thus decreasing
the overall throughput in AODV and DSR. This is not the case with AntHocNet it is
highly adaptive thus the expected throughput of AntHocNet increases as speed increases.
Thus it is concluded that AntHocNet is relatively lower, consistent and stable as compared
to AODV and DSR as number of nodes and speed increases.
Fig. 7 Throughput vs. Speed
Fig. 8 Throughput vs. Pause Time

Fig. 9 Throughput vs. Number of Nodes
CONCLUSION AND FUTURE WORK
This paper simulates and analyzes different topological node structures on NS2
environment, comparing the performance of AntHocNet, AODV and DSR. The
comparison is made based on the performance metrics - packet delivery ratio, average,
average end to end delay, and throughput. It is observed that AntHocNet outperforms
AODV in most of the test cases as tabulated in the result.
Results showed that the metric like end-to-end delay and throughput showed better
performance in AHN than in DSR. While the packet delivery ratio was less in AHN than
in AODV and DSR as ant agents occupy certain bandwidth and simulation setup time
considered was only 250 seconds. Scalability of AntHocNet in comparison with classical
routing algorithm AODV and DSR is demonstrated by simulation results. AntHocNet
performs better in terms of packet delivery ratio at high rates, at large number of nodes and
with high mobility but the performance is inferior to AODV and DSR at low rates and at
less number of nodes.
From this it is concluded that AntHocNet is suitable for large scale, high data rate
networks with high mobility. With the increase in number of nodes the performance of
AODV’s and DSR’s decreases whereas the performance of AntHocNet is either constant
or increases with either increase in number of nodes or at high data rates.
Future improvements with respect to AntHocNet can be done to enhance the protocol by
fine tuning the control packet overhead. Apart from this, the other improvements that can
be made are with respect to implementing a priority concept at the node level where
important packets could be sent first followed by the rest of the packets. Moreover
AntHocNet algorithm can be further extended by comparing the other bio inspired
algorithm where learning approaches have been performed like ants. A comparative
analysis of learning overhead in order to improve the optimal path determination can be
extended as the future work. These suggested improvements could add up to improvise the
AntHocNet algorithm’s performance even better.

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