Zone based ant colony routing in manet by  kumar bharagava (comp.sc. engg)
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Zone based ant colony routing in manet by kumar bharagava (comp.sc. engg)

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  • 1. ZONE BASED ANT COLONY ROUTING IN MANET @ Ants are good citizens, they place group interests first By :- Kumar Bhargava Roll no :- cs-09-65 Reg.no. :-0901230399 Comp. sc. engineering GUIDED BY: MISS B.SAHOO SYNERGY INST OF ENGINEERING AND TECH.(ORISSA,CUTTACK) INDIA.
  • 2. ABSTRACTMobile ad-hoc networks (MANETs) are a collection ofmobile nodes communicating wirelessly without acentralized infrastructure. The biggest challenge inMANETs is to find a path between communicatingnodes, that is, the MANET routing problem.The considerations of the MANET environment and thenature of the mobile nodes create further complicationswhich results in the need to develop special routingalgorithms to meet these challenges. Swarmintelligence, a bio-inspired technique, which has provento be very adaptable in other problem domains, has beenapplied to the MANET routing problem as it forms agood fit to the problem.
  • 3. ZONE BASED ANT COLONY ROUTING IN MANET ZONE BASED ANT COLONY ROUTING IMPLIMENTATION OF ARA IN MANET MANET ARA : @ ANT COLONY ROUTING ALGORITHIM
  • 4. ZONE We can define zone as an area with particular characteristic or a particular use. or A zone is a group of interfaces that have similar functions or features. ROUTERSROUTERSZONE :1 ZONE:2
  • 5. ANT COLONY ALGORITHIM ***The ant colony optimization meta- heuristic is a particular class of ant algorithms. Ant algorithms are multi- agent systems, which consist of agents with the behavior of individual Ants. The basic idea of the ant colony optimization meta-heuristic is taken from the food searching behavior of real ants.In computer science, metaheuristic designates a computational method thatoptimizes a problem by iteratively trying to improve a candidate solution withregard to a given measure of quality
  • 6. NEST FOODThe basic idea of the ant colony optimization metaheuristic is taken from the food searching behavior of realants.This behavior of the ants can be used to find the shortestpath in networks. Especially, the dynamic component of thismethod allows a high adaptation to changes in mobile ad-hocnetwork topology, since in these networks the existence of linksare not guaranteed and link changes occur very often.
  • 7. ANT COLONY OPTIMIZATION META-HEURISTIC ALGORITHM…..Let G = (V,E) be a connected graph with n = |V| nodes. Thesimple ant colony optimization meta-heuristic can be used tofind the shortest path between a source node vs and adestination node vd on the graph G.The path length is given by the number of nodes on the path.Each edge e(i, j) ∈ E of the graph connecting the nodes vi andvj has a variable ϕi,j (artificial pheromone), which is modifiedby the ants when they visit the node. The pheromoneconcentration, ϕi,j is an indication of the usage of this edge.
  • 8. An ant located in node vi uses pheromone ϕi,j of node vj ∈ Ni to compute the probability of node vj as next hop Ni is the set of one-step neighbors of node vi.The transition probabilities pi,j of a node vi fulfill theconstraint: During the route finding process, ants deposit pheromone on the edges. In the simple ant colony optimization metaheuristic algorithm, the ants deposit a constant amount Δϕ of pheromone.
  • 9. An ant changes the amount of pheromone of the edge e(vi, vj) when moving from node vi to node vj as follows: ϕi,j := ϕi,j +Δϕ ----------------------(1)Like real pheromone the artificial pheromone concentrationdecreases with time to inhibit a fast convergence of pheromoneon the edges. In the simple ant colony optimization meta-heuristic, this happens exponentially: ϕi,j := (1 − q) · ϕi,j, q∈ (0, 1]-------------------- (2)
  • 10. ALGORITHM FOR ACO Each initiated decision variable Xi = vji is called a solutioncomponent and denoted by cij . The solution is constructed byincrementally choosing the components from the GraphG(V,E). As , the components can be associated with either thevertices or the edges of the graph.Each component has a pheromone value associated with it ij .The ants move through the graph, and at each nodeprobabilistically choosing the next component to add to thesolution determined by the pheromone value of thecomponents.
  • 11. ALGORITHM FOR ACO Require: parameters1 WHILE iterations not complete do2. construct Solutions;3. Update Pheromones;4. Daemon Actions ; {optional}5. end while
  • 12. (cont.)The components can be associated with either the vertices or theedges of the graph. Each component has a pheromone valueassociated with it ij . The ants move through the graph, and ateach node probabilistically choosing the next component to addto the solution determined by the pheromone value of thecomponents. Construct Solutions, The choice of the next feasible component/node and of construction solution is made by the path selection equation which depends on the ant algorithm system being used and then solution is deposited.
  • 13. (cont.)Update Pheromones serves two tasks: To increase thepheromone values of the components which are good, and todecrease the pheromone values of the components which arebad. The pheromone decrease is achieved throughevaporation. Daemon Actions are usually used to perform centralized actions that cannot be performed by a single ant and that may be problem specific.
  • 14. MANET MANET is abbreviated as mobile ad-hoc network what is AD-HOC NETWORK ?*****An ad-hoc network is a wireless network created for particular purpose which is of decentralized type.
  • 15. OVERVIEW OF MANET GENERALA wireless ad-hoc network is a collection of mobile/semi mobilenodes with no pre-established infrastructure, forming atemporary network. Each of the nodes has a wireless interfaceand communicate with each other over either radio or infrared.Laptop computers and personal digital assistants thatcommunicate directly with each other are some examples ofnodes in an ad-hoc network. Nodes in the ad-hoc network areoften mobile, but can also consist of stationary nodes, such asaccess points to the Internet. Semi mobile nodes can be used todeploy relay points in areas where relay points might beneeded temporarily.
  • 16. Figure shows a simple ad-hoc network with three nodes. Theoutermost nodes are not within transmitter range of eachother. However the middle node can be used to forwardpackets between the outermost nodes. The middle node isacting as a router and the three nodes have formed an ad-hocnetwork.
  • 17. Mobile ad-hoc network: MANETA Mobile Ad Hoc network (MANET) is a collection of wirelessmobile nodes, which dynamically form a temporary network, withoutusing any existing network infrastructure or centralizedadministration.Routing in mobile ad hoc networks:• Each node is host and router,• No infrastructures or centralized control• Nodes might move and join and leave the network at any time• One shared communication medium• Short range and noisy transmissions• Very dynamic and spatial-aware problem
  • 18. 100 MILLION $ QUESTIONTHERE ARE MANY ALGORITHM PRESENT IN THIS COMPUTER WORLD…. Y ? ONLY ANT COLONY OPTIMIZATION TECHNIQUE
  • 19. CHALLENGES IN MANET• distributed state in unreliable environment• dynamic topology• limited network capacity• wireless communication 1. variable link quality 2. interference and collisions
  • 20. WHY ANT COLONY OPTIMIZATION META- HEURISTIC SUITS TO AD-HOC NETWORKS• Dynamic topology: This property is responsible for thebad performance of several routing algorithms in mobilemulti-hop ad-hoc networks. The ant colony optimizationmeta-heuristic is based on agent systems and works withindividual ants. This allows a high adaptation to the currenttopology of the network.
  • 21. • Link quality: It is possible to integrate the connection/linkquality into the computation of the pheromoneconcentration, especially into the evaporation process. This willimprove the decision process with respect to the link quality. Itis here important to notice, that the approach has to bemodified so that nodes can also manipulate the pheromoneconcentrationindependent of the ants, i.e. data packets, for this a node has tomonitor the link quality.
  • 22. • Support for multi-path: Each node has a routing table withentries for all its neighbors, which contains also the pheromoneconcentration. The decision rule, to select the next node,is basedon the pheromone concentration on the current node, which isprovided for each possible link. Thus, the approach supportsmultipath routing.
  • 23. THE ROUTING ALGORITHMIn this section we discuss the adaptation of the ant colonyoptimization meta-heuristic for mobile ad-hoc networks anddescribe the Ant colony based Routing Algorithm (ARA).The routing algorithm is very similar constructed as manyother routing approaches and consists of three phases.Route Discovery PhaseIn the route discovery phase new routes are created. Thecreation of new routes requires the use of a forward ant(FANT) and a backward ant (BANT). A FANT is an agentwhich establishes the pheromone track to the source node. Incontrast, a BANT establishes the pheromone track to thedestination node.
  • 24. FANT ESTABLISHING THE PHEROMONE TRACK TO SOURCE NODE F 5 F 2 Fs 4 D F F 1 F 3 F F 6
  • 25. BANT ESTABLISHINGTHE PHEROMONE TRACK TO THE DESTINATION NODE B 5 B 2 4 DS B B B 1 3 6 B B
  • 26. ROUTE FAILURE HANDLINGThe ARA also handles routing failures, which are causedespecially through node mobility and thus very common inmobile ad-hoc networks. ARA recognizes a route failurethrough a missing acknowledgement. If a node gets a ROUTEERROR message for a certain link, it first deactivates this linkby setting the pheromone value to 0. Then the node searches foran alternative link in its routing table. If there exists a secondlink it sends the packet via this path. Otherwise the nodeinforms its neighbors, hoping that they can relay the packet.Either the packet can be transported to the destination node orthe backtracking continues to the source node. If the packetdoes not reach the destination, the source has to initiate a newroute discovery phase.
  • 27. PROPERTIES OF ANT COLONY ROUTING ALGORITHMDistributed operation: In ARA, each node owns a set ofpheromone counter ϕi,j in its routing table for a link betweennode vi and vj . Each node controls the pheromone counterindependently, when ants visit the node on route searches.**************We call the pheromone concentration here as a counter, because of its regularly decreasing by the node.
  • 28. Locality: The routing table and the statistic informationblock of a node are local and they are not transmitted to anyother node.Multi-path: Each node maintains several paths to acertain destination. The choice of a certain route depends onthe environment, e.g., link quality to the relay node.Sleep mode: In the sleep mode a node snoops, only packetswhich are destined to it are processed, thus saving power.
  • 29. OVERHEAD OF ANTCOLONY ROUTING ALGORITHMThe expected overhead of ARA is very small, because there areno routing tables which are interchanged between the nodes.Unlike other routing algorithms, the FANT and BANT packetsdo not transmit much routing information. Only a uniquesequence number is transmitted in the routing packets. Mostroute maintenance is performed through data packets, thusthey do not have to transmit additional routing information.ARA only needs the information in the IP header of the datapackets.
  • 30. Reference……..[1] S.Corson. Mobile ad hoc networking (manet): Routing protocol performance issuesand evaluation considerations. http://www.ietf.org/rfc/rfc2501.txt.[2] Al Huda Amri and et. al. Scalability of manet routing protocols for heterogeneousand homogenous networks. Computers and Electrical Engineering, 2008.[3] Ian F. Akyildiz, Xudong Wang, and Weilin Wang. Wireless mesh networks: asurvey.Computer Networks and ISDN Systems, 47(4):445–487, 15 March 2005.[4] R¨ome, Kay, and Friedemann Mattern. The design space of wireless sensornetworks. IEEE Wireless Communications, 11(6):54–61, December 2004.[5] J. Kennedy and R. Eberhart. Particle swarm optimization. In Proceedings of IEEEInternational Conference on Neural Networks, IV, pages 1942–1948, 1995.
  • 31. ANY QUESTIONS PLZ………?THANK YOU……………