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Advances in Wireless and Mobile Communications.
ISSN 0973-6972 Volume 10, Number 3 (2017), pp. 369-387
© Research India Publications
http://www.ripublication.com
ACODeRA: A Novel ACO Based on Demand Routing
Algorithm for Routing in Mobile Ad Hoc Networks
Khushneet Kaur Batth1 and Rajeshwar Singh2
1Ph.D. Research Scholar, Desh Bhagat University, Punjab, India. Email: [email protected]
2Professor, Department of ECE, Doaba Khalsa Trust Group of Institutions, Punjab, India
Abstract
In Mobile Ad Hoc Networks, nodes do not have fixed infrastructure, sufficient
storage, energy to operate for longer hours and radio transmission range to
communicate among nodes for far off distances. Also, traditional routing
protocols fail or do not provide desired quality of services needed for users in
the area of deployment of these nodes. These limitations have created
challenging issues for researchers. A number of routing protocols are being
proposed, but they either fail during real time implementation or their
performance parameters degrade. In recent years, Swarm Intelligence (SI)
based, more specially Ant Colony Optimization (ACO) based routing
algorithms are being proposed by researchers. Each one is based on different
characteristics and properties of the ants. It has proved to be better solution for
routing problems in MANETs. In this paper, we propose an ACO routing
algorithms named ACODeRA for routing in MANETs. We simulate the
proposed algorithm using NS-2 and compare the result with traditional AODV
routing protocol. The proposed ACODeRA is multi-path routing algorithm
and improves the performance parameters such as packet delivery factor.
Also, we compare end to end delay for AODV and ACODeRA. The proposed
ACODeRA performs better for routing in Mobile Ad Hoc Networks.
Keywords: Swarm Intelligence (SI), Ant Colony Optimization (ACO),
AODV, ACODeRA, MANETs.
370 Khushneet Kaur Batth and Rajeshwar Singh
1. INTRODUCTION
Mobile Ad Hoc Networks (MANETs) [1, 2] are built up of a collection of mobile
nodes which have no fixed infrastructure. The nodes communicate through wireless
network and there is no central control for the nodes in the network. Routing is the
task of directing data packets from a source node i.e. transmitter to a given destination
node i.e. receiver. This task is particularly complex due to the dynamic topology,
limited process and storing capability, bandwidth constraints, high bit error rate and
lack of the central control. It is very challenging for the researchers and engineers to
develop and implement a routing algorithm to accomplish the task of routing in
changing topology of Mobile Ad Hoc Networks. Ants routing resembles basic
mechanisms from distributed Swarm Intelligence (SI) in biological systems and turns
out to become an appealing solution when routing becomes a crucial problem in a
complex network scenario, where traditional routing techniques either fail completely
or at least face intractable complexity. Ants based routing is gaining more popularity
because of its adaptive and dynamic nature [3,6,7].
A number of Swarm Intelligence (SI) based, more specially Ant Colony Optimization
(ACO) [8-14] based routing algorithms are proposed by researchers. It is a novel
evolutionary algorithm, which has the characteristics such as positive feedback,
negative feedback, multiple interactions, stigmergy, distributing computing and the
use of a constructive heuristic etc. ACOs features, match the demands of network
optimization, and a number of ant based algorithms are proposed by researchers.
In this paper a new ant-based algorithm named ACODeRA is presented that combines
many features AODV and DSR, DSDV [4,5, 38-40]. This routing algorithm has
features to establish multiple routes from source to destination and updates table as
per local and global updates available. It reduces the route discovery time and hence
able to manage the network topology change very effectively. Further, this paper is
organized as follows: Section 2 describes Swarm Intelligence in detail; Section 3
describes Ant Colony Optimization. Section 4 provides detailed description of Ant
Routing Algorithms. Section 5 of this paper describes experimental setup and
simulation parameters. Section 6 is presented with performance metrics and result
analysis with graphs. Section 7 presents conclusion and future scope.
2. SWARM INTELLIGENCE [15-20]
Swarm Intelligence, another novel branch of Artificial Intelligence, has attracted
several researchers to apply SI based optimization techniques in solving varied
problems of Robotics, Networking, Wireless Communications, Drones, Electronics,
Information Theory and other diverse areas. Swarm Intelligence concept was first
introduced by Gerardo Beni and Jing Wang [24] in 1989 with relation to cellular
robotic systems.
In general terms, Swarm Intelligence [17, 18, 19, 22, 23] is modeling of collective
behaviors of simple agents interacting locally among themselves, and their
environment, which leads to the evolution of a coherent functional global pattern.
ACODeRA: A Novel ACO Based on Demand Routing Algorithm for Routing.. 371
These models are inspired by social behavior of insects and other animals. Talking in
terms of computation, Swarm Intelligence models are computing algorithm models
used for undertaking and solving complex distributed optimization problems. The
basic principle of Swarm Intelligence primarily focuses on “Probabilistic-based Search Algorithms”. In Swam Intelligence, the most significant concept is “Swarm”.
Swarm is used to refer any restrained collection of interacting agents or individuals.
Communication among these swarms in distributed manner without any requirement
of centralized control mechanism makes these models highly realistic and robust to be
implemented in diverse applications.
The concept of Swarm Intelligence was started by two main Algorithms: Ant Colony
Optimization (ACO) being developed by Dorigo and Stutzle in 2004 and Particle
Swarm Optimization (PSO) being developed by Kennedy and Eberhart in 2001. With
time, various other algorithms have come up and make the Swarm Intelligence more
rich and implementable in different applications like Fish Swarm, Monkey Swarm,
Glowworms, Bee Colony, Artificial Immune System, Firefly Algorithm and many
more.
Swarm Intelligence primarily works on two founding principles: 1. Self Organization,
2. Stigmergy
Self-Organization: The concept of Self Organization was defined by Bonabeau et al
[27] [28] in 1999 as “Self Organization is a set of Dynamic Mechanisms whereby
structures appear at the global level of a system from interactions of its lower-level
components”. Self organization lays the foundation of three important characteristics:
Structure, Multi-Stability and State Transition.
Structure: It is founded from a homogeneous start-up state. E.g. Ant Foraging trails.
Multi-Stability: Co-existence of many stable states. E.g. Behavior of ants to search for
food random in field.
State Transitions: Change of System Behavior. Example: Location of new food source
after finishing the entire food transmit from source to nest.
Stigmergy: The word “Stigmergy” is mix of two words: Stigma= Work and
Ergon=Work which means Simulation by work. It is based on the principle that the
main area to operate for swarms in Environment and work is not dependent on
specific agents.
It can be summarized as, “Coordination, Cooperation and Regulation of tasks doesn’t
depend on workers directly, but on construction themselves”. The worker is properly
guided rather than directed to perform the work. It is also a special form of
stimulation called Stigmergy.
Stigmergy can be of following types:
Sign-Based Stigmergy: Ant Foraging Behavior; Ants Trail Following from
nest to food source and vice versa.
Sematactonics: Building of nests by Termites.
372 Khushneet Kaur Batth and Rajeshwar Singh
Quantitative: Ants Foraging Behavior
Qualitative: Nest building by Wasps.
For modeling the behaviors of Swarms, Millonas [29] laid the following 4
Principles as follows:
Proximity Principle of Swarm: Swarms should be highly capable to perform
simple computations with respect to the environment existing around them in
terms of time and space. E.g. Search for living place and building nest in
coordination.
Quality Principle of Swarm: Swarm should be highly respondent to
environmental quality factors like food, safety and other stuff.
Diverse Response Principle of Swam: The swarm should not allocate all of its
resources along excessively narrow channels and it should distribute resources
into many nodes.
Stability and Adaptability Principle of Swarm: Swarms are expected to adapt
environmental fluctuations without rapidly changing modes since mode
changing involves tremendous amounts of energy.
3. ANT COLONY OPTIMIZATION [30-37]
Ant Colony Optimization (ACO) was discovered and introduced by M.Dorigo and
colleagues as a Nature-Inspired meta-heuristic for providing optimal solutions to hard
combinatorial optimization (CO) problems. A Meta heuristic is regarded as set of
algorithms that can be used to elaborate heuristic method applicable to wide range of
problems. It is regarded as general purpose framework to different optimization
problems with few modifications. “Marco Dorigo” in his Ph.D Thesis “Optimization,
Learning and Natural Algorithms”, in which he elaborated the way to solve problems
using behavior being used by real ants, presents the first Algorithm defining the
framework in 1991. Real Ants are highly sophisticated and intelligent swarms to find
the shortest path from food source to nest by depositing pheromone on the ground and
laying the trails so that other ants can follow. The most important component of ACO
Algorithms is the combination of a priori information regarding the structure with a
posteriori information about the structure of previously obtained optimal solutions.
In order to determine the shortest path, a moving ant lay the pheromone which acts as
base for other ants to follow and deciding the high probability to follow it. As a result,
it leads to the emergence of collective behavior and forms a positive feedback loop
system through which other ants can follow the path and makes the pheromone more
stable and best path for transferring the food back to nest.
Combinatorial Optimization Problem- Definition
The first step for the application of ACO to a combinatorial optimization problem
(COP) consists in defining a model of the COP as a triplet (S,Ω,f) , where:
S is a search space defined over a finite set of discrete decision variables;
ACODeRA: A Novel ACO Based on Demand Routing Algorithm for Routing.. 373
Ω is a set of constraints among the variables; and
f:S→R+0 is an objective function to be minimized (as maximizing over f is the
same as minimizing over –f ,|every COP can be described as a minimization
problem).
The search space S| is defined as follows. A set of discrete variables Xi , i=1,…,n ,
with values vji∈Di={v1i,…,v|Di|i} , is given. Elements of S are full assignments, that
is, assignments in which each variable Xi has a value vji assigned from its domain Di.
The set of feasible solutions SΩ is given by the elements of S that satisfy all the
constraints in the set Ω .
A solution s∗∈SΩ is called a global optimum if and only if
f(s∗)≤f(s) ∀s∈SΩ .|
The set of all globally optimal solutions is denoted by S∗Ω⊆SΩ . Solving a COP
requires finding at least one s∗∈S* Ω.
ACO for Travelling Salesman Problem (TSP) [32]
Ant Algorithms are developed on population based approach are applied to combat
various NP-hard combinatorial optimization problems. It was the first ACO
algorithm, known as “ANT System” [34] [35]. Ant System was applied to Travelling
salesman problem (TSP).
The TSP comprise of group of cities connected to each other and distance between
them is also known. The objective is to attain the shortest path which facilitates to
374 Khushneet Kaur Batth and Rajeshwar Singh
visit every city at least once. In order words, to calculate Hamiltonian type coverage
of minimal distance between cities on a fully connected graph.
TSP plays vital role in ACO algorithms because of the following reasons:
Easily applied to ACO algorithms as ants have same kind of behavior to
determine the efficient path from nest to source by laying pheromone trails
randomly and then choosing the best path for other ants to follow.
Regarded as NP-hard problem.
It is a standard testing platform for new algorithms to be checked out and a
good performance on TSP is taken into consideration as proof for their
correctness and efficiency.
TSP being the first combinatorial problem, that was solved by ant algorithms.
ACO Algorithm applied to TSP
Procedure ACO algorithm for TSPs
Define parameters, initialize pheromone trails
While (termination condition not met) do
ConstructSolutions
ApplyLocalSearch %optional
UpdateTrails
end
end ACO algorithm for TSPs
Ant Colony Optimization-Definition
Ant Colony Optimization technique is based on ants i.e. how ant colonies find the
efficient path between nest and food source. In search of food, ants roam randomly in
the environment. On location of the food source, ant’s first return back to their nest by
laying a trail of chemical substance called “Pheromone” in their path. Pheromone lays
the foundation for communication medium for other ants to follow the way and go to
the food source. When other ants follow the path, the quantity of pheromone increases
on that particular path. The rich the quantity of pheromone along the path, the more
likely is that other ants will detect and follow the path. In other words, ants follow that
path which is marked by strongest pheromone quantity. As pheromone evaporates
over time, which in turn reduces its attractive strength? The longer the time taken by
ant to travel the path from food source to nest, the quicker the pheromone will
evaporate. So, the path should be shorter so that the active strength of pheromone is
maintained and ants can easily transfer the food from source to nest. So, in turn of this
policy the shortest path will naturally emerge.
ACODeRA: A Novel ACO Based on Demand Routing Algorithm for Routing.. 375
The following algorithm explains Ant Colony Optimization:
Initialize Parameters
Initialize pheromone trails
Create ants
While stopping criteria is not reached, do
Let all ants construct their solution
Update pheromone trails
Allow Daemon Actions
End while
Suitability of Ant Colony Optimization Routing Algorithm
Ant Colony Optimization Routing Algorithm mentioned above is highly suitable and
performs well for the following reasons: Provide traffic adaptive and multipath
routing. Rely on both passive and active information monitoring and gathering.
Making use of stochastic components. Don’t allow local estimates to have global
impact.Setup paths in a less selfish way than in pure shortest path schemes
favoring load balancing. Showing limited sensitivity to parameter settings
4. DESCRIPTION OF ANT ROUTING ALGORITHMS [37]
Mobile Ad Hoc Network with V nodes connecting with E links are represented by
weighted digraph as:
G = ( V, E )
In the proposed Ant Based on Demand Routing Algorithm ACODeRA, two ants are
used. One FANT, which is created at Source S and moves to destination D. The other
is BANT, which is created at Destination and follows the part of FANT and updates
the route table. The FANT available at node i, follows the path through node j by
probability formulae:
𝑷(𝒊, 𝒋) =𝝆(𝒊, 𝒋)
∑ 𝝆(𝒊, 𝒔)𝒔𝝐𝑵𝒊 𝒊𝒇 𝒔𝝐𝑵𝒊 − − − (𝟏)
= 0 otherwise.
Where, Ni is neighbor node set of node i.
ρ ( i, j ) is pheromone strength on link e ( i, j )
376 Khushneet Kaur Batth and Rajeshwar Singh
∑ 𝑷(𝒊, 𝒋) = 𝟏
𝒔𝝐𝑵𝒊
− − − − − (𝟐)
When forward ant FANT moves on link e ( i, j ) then ρ ( i, j ) is updated by
formulae:𝝆(𝒊, 𝒋) ⃪ (𝟏 − 𝒒)𝝆(𝒊, 𝒋) + ∆𝝆(𝒊, 𝒋) − − − (𝟑)
And ∆𝝆(𝒊, 𝒋) =𝜶
𝑫(𝒊,𝒋)− − − −(𝟒)
where ∆𝝆 ( 𝒊, 𝒋 ) 𝒊𝒔 𝒊𝒏𝒄𝒓𝒆𝒎𝒆𝒏𝒕 𝒐𝒇 𝒑𝒉𝒆𝒓𝒐𝒎𝒐𝒏𝒆 𝒃𝒚 𝑭𝑨𝑵𝑻
α is tunable parameter and utilized to calculate allowed delay D ( i, j ) and q ϵ ( 0, 1 )
is pheromone evaporation parameter.
When FANT reaches to Destination D, it is destroyed and BANT is created and
routed through the path of FANT. During this process, pheromone parameter ρ(i,j) is
again updated by formulae:
𝝆(𝒊, 𝒋) ⃪ (𝟏 − 𝒒)𝝆(𝒊, 𝒋) + ∆ᴧ𝝆(𝒊, 𝒋) 𝑰𝒇(𝒊, 𝒋)𝝐𝑹
𝝆(𝒊, 𝒋) ⃪(𝟏 − 𝒒)𝝆(𝒊, 𝒋) 𝑶𝒕𝒉𝒆𝒓𝒘𝒊𝒔𝒆 − − − − − − − (𝟓)
R is reverse route, which FANT passes from source to destination
∆ᴧ𝝆(𝒊, 𝒋) = 𝜷 𝐦𝐢𝐧 [ 𝑩(𝒊, 𝒋) ] − −(𝟔)
β is tunable parameter used to influence bandwidth B ( i, j ), min B ( i, j ) is minimum
bandwidth of the link to maintained the desired quality of service.
Now we can define the probabilities P1 and P2, of two paths through mobile nodes i
and j, from same source S to same destination D as:
P1= (S, i1, i2, i3,….., im,D) and P2= (S, j1, j2, j3,….., jm,D)
Let us consider, intermediate node sets are
I = { im | 1≤ m ≤ |V|} and J = { jn | 1≤ n ≤ |V|}
If I∩J = Ф then P1 and P2 are two similar routes.
Route Discovery - When node S sends a packet to destination node D. Node searches
the destination route in the routing table. If it is not available in the routing table, it
creates a FANT with source address and broadcasts to all adjacent nodes.
I. When an intermediate node i receives the FANT and checks destination address of
FANT, if destination address is not same: (a) node i adds own address and time FANT
arrived to i, (b) node i adds the source address as destination address for routing
BANT, into routing table and computes pheromone value according to formulae
ACODeRA: A Novel ACO Based on Demand Routing Algorithm for Routing.. 377
shown above (c) the bandwidth is considered for the link between nodes. (d) Hop
count is also updated. (e) node i broadcasts FANT to neighbor again, if did not
receive the route of destination.
II. When node i receives duplicate FANT i.e with same sequence number and source
address, the above steps are followed. If sequence number is less than or equal to max
sequence number and route record of FANT includes address of present node, then
the FANT is discarded. Otherwise node updates the max sequence number by new
value and executes the steps shown in I.
III. When destination of FANT is same as that of i, route is discovered. FANT is
discarded after retrieving relevant information from FANT, and BANT is sent on to
the path followed by FANT. Again the pheromone table is updated as per BANT
equation defined above. When BANT reaches to the source S all the pheromone
tables are updated.
Route Maintenance – When there is congestion at node i, node i will retrace the path
of FANT to inform to source S to change route. Pheromone value is changed from
source and same value is updated in routing table, so that congested path is not further
loaded. When all the routes fail, source S will initiate a new route request again.
When there is error in the node, mainly due to change in location of mobile node. The
pheromone value is made to 0, so that route is not followed by the broadcasting node.
Basic Ant Colony Routing Algorithm- Flowchart The following diagram shows the Flowchart- showing the flow of Ant Colony
Routing Algorithm.
Fig. 1 Flowchart showing the Working of Ant Colony Routing Protocol
378 Khushneet Kaur Batth and Rajeshwar Singh
5. EXPERIMENTAL SETUP AND RESULTS
The Network Simulator NS-2 experimental setup is used for performance evaluation
of the AODV and ACODeRA routing protocols. It measures the ability of protocols to
adapt to the dynamic network topology changes while continuing to successfully
deliver data packets from source to their destination. In order to measure this ability,
different scenarios are generated by varying the pause time of nodes. We use
following scenario generation commands for generating scenario file for 50 nodes:
./setdest –v 1 –n 50 –p 10.0 –M 10.0 -t 1000 -x 1500 -y 300;
./setdest –v 1 –n 50 –p 20.0 –M 10.0 -t 1000 -x 1500 -y 300;
./setdest –v 1 –n 50 –p 30.0 –M 10.0 -t 1000 -x 1500 -y 300;
./setdest –v 1 –n 50 –p 40.0 –M 10.0 -t 1000 -x 1500 -y 300;
./setdest –v 1 –n 50 –p 50.0 –M 10.0 -t 1000 -x 1500 -y 300;
Similarly, for connection pattern generation we use, cbrgen.tcl file. By using
command
“ns cbrgen.tcl -type cbr -nn 50 -seed 1.0 -mc 16 –rate 4.0;” the connection pattern
is generated.
The trace file is created by each run and is analyzed using a variety of scripts,
particularly one called file *.tr that counts the number of successfully delivered
packets and the length of the paths taken by the packets, as well as additional
information about the internal functioning of each scripts executed. This trace file is
further analyzed with AWK file and Microsoft Excel is used to produce the graphs.
In order to measure the performance of proposed Ant Based on Demand Routing
Algorithm ACODeRA effectively, we compare the performance of ACODeRA with
AODV for varying pause time. Nodes we have considered are 50 and they move
within an area of 1500m x 300 m using Random Waypoint Model. The maximum
speed of nodes we have considered in simulation is 10m/s. The channel capacity is 2
Mbps and transmission range of each node is taken as 250m. Traffic Type is 20 CBR
(continuous bit rate) traffic sources each send 4 packets per second with a packet size
of 64 bytes. Packet Delivery Fraction and End to End Delay is considered as
evaluation parameter for this simulation. Also, we have considered nodes with Omni-
Antenna and Two Ray Ground Radio Propagation method. Simulation parameters are
appended in Table-1.
ACODeRA: A Novel ACO Based on Demand Routing Algorithm for Routing.. 379
Table: 1. Simulation Parameters
Parameter Value
Simulator NS-2.34
Protocols studied AODV and ACODeRA
Simulation time 25,50,75,100 sec
Simulation area 1500 X 300
Transmission range 250 m
Node movement model Random Way Point
Traffic type CBR (UDP)
Data payload 64 Bytes / packet
6. PERFORMANCE METRICS AND RESULT ANALYSIS
In this paper we have considered End to End Delay in milli second and Packet
Delivery Fraction (PDF) in percentage for evaluation of AODV and ACODeRA
routing protocols.
6.1 Packet Delivery Fraction (PDF).
It is the ratio of the data packets delivered to the destination to those generated by the
sources.
Packet Delivery Fraction (PDF) = Total Packets Delivered to destination / Total
Packets Generated.
Mathematically, it can be expressed as:
e
f f
f
NR
cP
1
1
Where, P is the fraction of successfully delivered packets, C is the total number of
flow or connections, f is the unique flow id serving as index, Rf is the count of packets
received from flow f and Nf is the count of packets transmitted to f. The Packet
Delivery Fraction with varying pause time for AODV and ACODeRA is calculated.
The comparison between AODV and ACODeRA are placed in table 2 and 3 and
graphs are shown in fig..2 and fig. 3.
380 Khushneet Kaur Batth and Rajeshwar Singh
Table 2. Packet Delivery Fraction with varying Simulation Time
Simulation
Time AODV ACODeRA
25 85.41121 89.93382
50 82.9991 90.27102
75 80.13186 88.60818
100 77.23998 86.43871
Fig.2: Packet Delivery Fraction with varying Simulation Time
Table.3: Packet Delivery Fraction with varying Pause Time
Pause Time
AODV ACODeRA
10 84.4112 92.9338
20 81.9991 92.271
30 79.587 91.6082
40 77.1749 90.9454
50 74.7626 90.2826
0
10
20
30
40
50
60
70
80
90
25 50 75 100
Pac
ket
De
live
ry F
ract
ion
Simulation Time in Seconds
AODV
ACODeRA
ACODeRA: A Novel ACO Based on Demand Routing Algorithm for Routing.. 381
Fig.3: Packet Delivery Fraction with varying Pause Time
6.2 End to End Delay:
It includes buffering delay, queuing delay, propagation delay, retransmission delay
and transfer delay. The multi-path route of ACODeRA can select path by the
bandwidth of the path in the route. As a result, the buffering delay and the
retransmission delay are decreased. The End to End Delay with varying simulation
time and varying pause time for AODV and ACODeRA are calculated. The
comparison between AODV and ACODeRA is carried out and data obtained in
appended in table 4 and 5. The graphical presentation is shown in fig.4 and fig..5.
Table.4: End to End delay with varying Simulation Time
Simulation Time AODV ACODeRA
25 3.42 3.02
50 5.11 4.33
75 6.91 5.76
100 8.71 7.19
0
10
20
30
40
50
60
70
80
90
100
10 20 30 40 50
Pac
ket
De
live
r Fr
acti
on
Pause Time in Second
AODV
ACODeRA
382 Khushneet Kaur Batth and Rajeshwar Singh
Fig.4: End to End Delay with varying Simulation Time
Table 5: End to End Delay with varying Pause Time
Pause Time AODV ACODeRA
10 5.95 2.13
20 6.03 3.02
30 6.92 3.56
40 6.99 4.33
50 7.36 5.05
Fig.5: End to End Delay with varying Pause Time
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
25 50 75 100
End
to
En
d D
ela
y in
mill
i Se
c.
Simulation Time in Seconds
AODV
ACODeRA
0
1
2
3
4
5
6
7
8
10 20 30 40 50
End
to
En
d D
ela
y in
Mill
i Se
c.
Pause Time in Seconds
AODV
ACODeRA
ACODeRA: A Novel ACO Based on Demand Routing Algorithm for Routing.. 383
7. CONCLUSION
In this paper, we have evaluated the performance of ACO based, named as Ant
Based-on-Demand Routing Algorithm ACODeRA with traditional routing protocol
AODV for Mobile Ad Hoc Networks using NS-2 event simulator. The proposed
ACODeRA decreases the average end-to-end delay and the times of congestion
happening effectively by on demand creating the multiple routes of link disjoint paths.
This is because the proposed algorithm uses local updating rule and global updating
rule to update the pheromone of link and the probability route table. Also, selects
route and balances traffic flows considering bandwidth and probability from routing
table. Experimental results show that ACODeRA performs better in both the
performance matrices i.e. for Packet Delivery Fraction as well as End to End delay
with varying simulation time and pause time.
It has been found that the overall performance of proposed ACO based ACODeRA
routing protocol for performance matrices, Packet Delivery Fraction as well as End to
End delay is better than that of traditional AODV routing protocol. In our
experimental evaluation, we have taken up comparison of ACODeRA and AODV
protocols with varying simulation time and pause time with fixed number of nodes to
50.
ACKNOWLEDGEMENT
We are thankful to CMU Monarch Project Group for providing Wireless and Mobility
Extensions to NS-2 Simulator.
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AUTHORS BIOGRAPHY:
Khushneet Kaur Batth received her B.E. and M.E degree in
Electronics and Communication Engineering from Punjab Technical
University, Jallandhar, Punjab. She is presently working as Research
Scholar at Desh Bhagat University, Punjab. Her research interests
include Mobile Ad hoc networks, Sensor Networks, Neural Networks,
fuzzy logic and Communication networks.
Dr. Rajeshwar Singh is Ph.D. in Engineering from Department of
Electronics and Communication Engineering, Faculty of Engineering,
BIT Sindri, Dhanbad, Jharkhand. His Master of Engineering degree is in
Electronics and Communication Engineering with specialization in
Digital Systems from Motilal Nehru Regional Engineering College
(currently NIT), Allahabad, U.P. He received his AMIE (India) degree in 1992 from
The Institutions of Engineers, Calcutta. He has also received, Master of Business
Administration (MBA) in Information Technology from MD University, Rohtak,
Haryana. He has more than 25 years of experience in teaching and industry. He has
published more than 60 papers in national and international journals / conferences of
repute and four books in the field of Engineering and Technology. His research
interests include Swarm Intelligence based Optimization (Energy, Security, Routing),
Wireless Sensor Network, intellectual information technology, Mobile Ad hoc
Network Routing, Coding theory, cryptography, e-Governance and Educational
Planning.
388 Khushneet Kaur Batth and Rajeshwar Singh