load balancing ant algo in MANET by navish jindal

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load balancing ant algo in MANET by navish jindal

  1. 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. 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 2
  3. 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. 4. MANET 4
  5. 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. 5
  6. 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. 6
  7. 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 7 Pheromone = α/Nh
  8. 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. 8 Pn = P - 0.8* P
  9. 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 9
  10. 10. RANDOM AND RAPID SEARCH 10
  11. 11. ANTS AGENTS CAN BE DIVIDED INTO TWO SECTIONS 11
  12. 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. 12
  13. 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. 13
  14. 14. PHASES OF ANT BASED ALGORITHM 1. Route Discovery Phase 2. Route Maintance Phase 3. Route Failure Handling 15
  15. 15. 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. 16
  16. 16. 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 17
  17. 17. 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. 18
  18. 18. 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. 19
  19. 19. PHEROMONE VALUES :P3> P1 >P5 20
  20. 20. 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. 21
  21. 21. 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. 22
  22. 22. 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 23
  23. 23. 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. 24
  24. 24. PERFORMANCE METRICS Performance parameters AODV ANT ALGORITHM Packet delivery ratio low high End to end delay high low 25
  25. 25. 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. 26
  26. 26. 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. 27
  27. 27. 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
  28. 28. 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

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