1. LOAD BALANCING IN ANT BASED
ALGORITM IN MANET
SEMINAR
SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD
OF THE DEGREE OF
MASTER OF TECHNOLOGY
(Computer Science and Engineering)
1
Submitted By:-
Navish Jindal
1269166
M.Tech(C.S.E)
L.L.R.I.E.T. Moga

2. CONTENTS
 What is MANET
 Ant Based Algorithm
 Pheromone
 Pheromone creation
 Pheromone evaporation
 Principles of ant colony optimization
 Phases of ant based optimization
 Load balancing
 Performance metrics
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3. WHAT IS MANET
A mobile ad-hoc network (MANET) is a set of
mobile nodes which communicate over radio and do
not need any infrastructure.
This kind of networks are very flexible and suitable
for several situations and applications.
All the nodes are mobile and connected dynamically.
Topology changes rapidly.
Suitable for emergency situations like natural or
human-induce disasters, military conflicts,emergency
medical situations, etc. 3

4. MANET
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5. ANT BASED ALGORITHM
 Ant as a single individual has a very limited
effectiveness. But as a part of a well-organized
colony, it becomes one powerful agent, working for
the development of the colony.
 Each ant is able to
communicate, learn, cooperate, and all together they
are capable of develop themselves and colonies a
large area.
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6. PHEROMONE
 Pheromones are the chemicals that are excreted by ants
of same species when they move.
 These are also called alarm pheromones.
 Pheromone deposited on ground forms a pheromone
trails which allows the ants to find good sources of food
that have be previously identified by other ants.
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7. PHEROMONE CREATION
Initiate pheromone value for all the nodes route table
calculated by equation
Nh = number of hops for ant to travel from source to
next node.
α = constant value parameter 1 > > 0
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Pheromone = α/Nh

8. PHEROMONE EVAPORATION
 The benefit is to remove any unused routes that will
consume memory space. The evaporation function is
calculated by:
 All pheromone values in routing table decreases over
time.
Pn: New evaporation pheromone value.
P: Pheromone value calculated at each node.
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Pn = P - 0.8* P

9. PRINCIPLES OF ANT COLONY
ALGORITHM
The ability of ants to self organize is based on three
principles.
 Random and rapid search
 Stigmergy
 Shortest path
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10. RANDOM AND RAPID SEARCH
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11. ANTS AGENTS CAN BE DIVIDED
INTO TWO SECTIONS
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12. STIGMERGY
 Stigmergy is defined as method of indirect
communication in a self organising system where its
individual parts communicate with one another by
modifying their local environment.
 The implementation of ant algorithms is made possible
by the use of so-called stigmergic
variables, i.e., variables that contain the information used
by artificial ants to indirectly communicate.
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13. SHORTEST PATH
 In a simple case, when two ants leave the colony at the
same time and take different paths to a food
source, marking their trails with PHEROMONE, the ant
that took the shorter path will return first.
 As a result of this effect, the shortest path will emerge
rapidly because a shorter path will have a higher
pheromone concentration. In this way all the ants follow
the best and shortest path.
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15. PHASES OF ANT BASED ALGORITHM
1. Route Discovery Phase
2. Route Maintance Phase
3. Route Failure Handling
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16. ROUTE DISCOVERY PHASE
 Route discovery phase uses control packet to discover
route from source to destination. The control packets are
mobile agents which walk through the network to
establish routes between nodes.
 Route Discovery Phase, shows the propagation of
Forward ANTS from source to destination.
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17. A need to send to G
Lookup Cache for route A to G
Route
found
?
Start Route
Discovery
Protocol
Continue
normal
processing
Route
Discovery
finished
Packet
in
buffer? Send
packet to
next-hopdone
Buffer
packet
no
Write route in
packet header
yes
wait
ROUTE DISCOVERY
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18. ROUTE MAINTANCE PHASE
 It is essential to find the goodness of a route regularly
and update the pheromone counts for the different routes
at the source nodes.
 To accomplish this, when a destination node receives a
packet, it probabilistically sends a Congestion Update
message to the source. This Congestion Update message
also serves an ACK to the source.
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19. ROUTE FAILURE HANDLING
PHASE
 This phase is responsible for generating alternative
routes in case the existing route fails. Every packet is
associated with acknowledgement; hence if a node does
not receive an acknowledgement, it indicates that the
link is failed.
 This helps in load balancing. That is, if the optimal path
is heavily loaded, the data packets can follow the next
best paths.
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20. PHEROMONE VALUES :P3> P1 >P5
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21. PROBLEM IN MANET DUE TO LOAD
 There is a Congestion due to broadcasting of Ants in Ant
algorithm.
 They are unable to provide good performance in large
volume of traffic.
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22. LOAD BALANCING
 This load balancing is achieved by calculating the
threshold value of each routing table through average
number of requests accepted by each node.
 The number of packets can be controlled that has been
send.
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23. PERFORMANCE METRICS
 Packet Delivery Ratio :- It is calculated by dividing the
number of packets received by the destination over the
packets originated by source.
 The better the delivery ration the more accurate routing
protocol.
PDR= Σnumber of all packets received / number of all
packets send
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24. AVERAGE END TO END DELAY
 Average end to end is the average time that a packet
takes to reach the destination in seconds.
 It is calculated by subtracting time at which first packet
was transmitted by source from time at which first data
packet arrived to destination.
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25. PERFORMANCE METRICS
Performance parameters AODV ANT ALGORITHM
Packet delivery ratio low high
End to end delay high low
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26. CONCLUSION
 The algorithm is based on balancing the load among
the routes by calculating threshed value of each
routing table.
 This defines MANET comparison of its better
performance with AODV routing protocol based on
ant algorithm is in terms of packet delivery
ratio, end to end delay.
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27. FUTURE WORK
The future work could be to investigate different
methods to further limit the traffic or load and compare
the ant based algorithm for other proactive and reactive
routing protocols.
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28. REFERENCES
[1] Mesut G unes¸,UdoSorges,ImedBouazizi {mesut, udo, imed}@i4.informatik.rwth-aachen.de
Department of Computer Science, Informatik 4 Aachen University of Technology
Aachen, Germany “ARA – The Ant-Colony Based Routing Algorithm for MANETs ∗”
[2] Payman Arabshahi and Andrew Gray, “Adaptive Routing in Wireless Communication Networks
using Swarm Intelligence”.
[3] Kalaavathi B, Madhavi S, VijayaRahavan and Duraiswamy K, “Review of Ant Based Routing
Protocols for MANET” International Conference on Computing, Communication and
Networking, Dec 2008.
[4]. Vinh Dien HOANG. Zhenhai SHAO and Masayunki FUJISE, “Efficient Load balancing in
MANETs to Improve Network Performance”, International Confrence on ITS Telecommunications
Proceedings, 2006.
28

29. REFERENCES
[5] Mamoun Hussein Mamoun “Important Characteristic of Differences between DSR and AODV
Routing Protocol”, MCN 2007 Conference, November 7-10, 2007.
[6] S. Prasad, Y.P.Singh and C.S.Rai , “Swarm Based Intelligent routing for MANET”, International
Journal of Recent trends in Engineering Vol 1,No.1 ,May 2009.
[7] Hamideh Shokrani and Sam Jabbehdari, “A Survey of Ant Based Routing Algorith for Mobile Ad-
Hoc Networks”, International Confrence on Signal Processing Systems, 2009.
[8] Al-Dahoud Ali, Mohamed A. Belal and Moh’d Belal Al-Zoubi, “Load Balancing of Distributed
Systems Based on Multiple Ant Colonies Optimization”, American Journal of Applied Sciences 7
(3): 433-438, 2010.
[9] Shivanajay Marwaha Jadwiga Indulska Marius Portmann “Biologically Inspired Ant-Based Routing
In Mobile Ad hoc Networks (MANET): A Survey” Symposia and Workshops on
Ubiquitous, Autonomic and Trusted Computing, 2009 29