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

ZONE BASED ANT COLONY ROUTING IN MANET @ Ants are good citizens, they place group interests

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By :- Kumar BhargavaRoll no :- cs-09-65Reg.no. :-0901230399Comp. sc. engineering

GUIDED BY: MISS B.SAHOO

SYNERGY INST OF ENGINEERING AND TECH.(ORISSA,CUTTACK) INDIA.

ABSTRACT

Mobile ad-hoc networks (MANETs) are a collection of mobile nodes communicating wirelessly without a centralized infrastructure. The biggest challenge in MANETs is to find a path between communicating nodes, that is, the MANET routing problem. The considerations of the MANET environment and the nature of the mobile nodes create further complications which results in the need to develop special routing algorithms to meet these challenges. Swarm intelligence, a bio-inspired technique, which has proven to be very adaptable in other problem domains, has been applied to the MANET routing problem as it forms a good fit to the problem.  

ZONE BASED ANT COLONY ROUTING IN MANET

ZONE BASED ANT COLONY ROUTING

MANET

IMPLIMENTATION OF ARA IN MANET

ARA : @ ANT COLONY ROUTING ALGORITHIM

ZONE

ZONE :1 ZONE:2

ROUTERS

ROUTERS

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.

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 realants.

In computer science, metaheuristic designates a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality

FOOD

NEST

The basic idea of the ant colony optimization meta heuristic is taken from the food searching behavior of real ants.

This behavior of the ants can be used to find the shortestpath in networks. Especially, the dynamic component of this method allows a high adaptation to changes in mobile ad-hoc network topology, since in these networks the existence of links are not guaranteed and link changes occur very often.

ANT COLONY OPTIMIZATION META-HEURISTICALGORITHM…..

Let G = (V,E) be a connected graph with n = |V| nodes. The simple ant colony optimization meta-heuristic can be used to find the shortest path between a source node vs and a destination 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.

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.

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 pheromone on the edges. In the simple ant colony optimization meta-heuristic, this happens exponentially:

ϕi,j := (1 − q) · ϕi,j, q∈ (0, 1]-------------------- (2)

ALGORITHM FOR ACO

Each initiated decision variable Xi = vji is called a solution component and denoted by cij . The solution is constructed by incrementally choosing the components from the Graph G(V,E). As , the components can be associated with either the vertices 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 node probabilistically choosing the next component to add to the solution determined by the pheromone value of the components.

ALGORITHM FOR ACO

1 WHILE iterations not complete do

2. construct Solutions;

3. Update Pheromones;

4. Daemon Actions ; {optional}

5. end while

Require: parameters

(cont.)

The components can be associated with either the vertices 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 node probabilistically choosing the next component to add to the solution determined by the pheromone value of the components.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.

Update Pheromones serves two tasks: To increase the pheromone values of the components which are good, and to decrease the pheromone values of the components which are bad. The pheromone decrease is achieved through evaporation.

Daemon Actions are usually used to perform centralized actions that cannot be performed by a single ant and that may be problem specific.

(cont.)

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.

OVERVIEW OF MANET

GENERAL

A wireless ad-hoc network is a collection of mobile/semi mobile nodes with no pre-established infrastructure, forming a temporary network. Each of the nodes has a wireless interface and communicate with each other over either radio or infrared. Laptop computers and personal digital assistants thatcommunicate directly with each other are some examples of nodes in an ad-hoc network. Nodes in the ad-hoc network are often mobile, but can also consist of stationary nodes, such as access points to the Internet. Semi mobile nodes can be used to deploy relay points in areas where relay points might be needed temporarily.

Figure shows a simple ad-hoc network with three nodes. The outermost nodes are not within transmitter range of each other. However the middle node can be used to forward packets between the outermost nodes. The middle node is acting as a router and the three nodes have formed an ad-hoc network.

Mobile ad-hoc network: MANET

A Mobile Ad Hoc network (MANET) is a collection of wireless mobile nodes, which dynamically form a temporary network, without using any existing network infrastructure or centralized administration.

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

100 MILLION $ QUESTION

THERE ARE MANY ALGORITHM PRESENT IN THIS COMPUTER WORLD….

Y ? ONLY

ANT COLONY OPTIMIZATION TECHNIQUE

CHALLENGES IN MANET

• distributed state in unreliable environment• dynamic topology• limited network capacity• wireless communication 1. variable link quality 2. interference and collisions

WHY ANT COLONY OPTIMIZATION META- HEURISTIC SUITS TO AD-HOC NETWORKS

• Dynamic topology: This property is responsible for the bad performance of several routing algorithms in mobile multi-hop ad-hoc networks. The ant colony optimization meta-heuristic is based on agent systems and works with individual ants. This allows a high adaptation to the current topology of the network.

• Link quality: It is possible to integrate the connection/link quality into the computation of the pheromone concentration, especially into the evaporation process. This will improve the decision process with respect to the link quality. It is here important to notice, that the approach has to be modified so that nodes can also manipulate the pheromone concentrationindependent of the ants, i.e. data packets, for this a node has to monitor the link quality.

• Support for multi-path: Each node has a routing table with entries for all its neighbors, which contains also the pheromone concentration. The decision rule, to select the next node,is based on the pheromone concentration on the current node, which is provided for each possible link. Thus, the approach supports multipath routing.

THE ROUTING ALGORITHM

In 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. In contrast, a BANT establishes the pheromone track to thedestination node.

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F

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F F

F

F

FANT ESTABLISHING THE PHEROMONE TRACK TO SOURCE NODE

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B

B B

B

B B

B

BANT ESTABLISHINGTHE PHEROMONE TRACK TO THE DESTINATION NODE

D

ROUTE FAILURE HANDLING

The ARA also handles routing failures, which are caused especially through node mobility and thus very common in mobile ad-hoc networks. ARA recognizes a route failure through a missing acknowledgement. If a node gets a ROUTE ERROR message for a certain link, it first deactivates this link by setting the pheromone value to 0. Then the node searches for an alternative link in its routing table. If there exists a second link it sends the packet via this path. Otherwise the node informs its neighbors, hoping that they can relay the packet. Either the packet can be transported to the destination node or the backtracking continues to the source node. If the packet does not reach the destination, the source has to initiate a new route discovery phase.

PROPERTIES OF ANT COLONY ROUTING ALGORITHM

Distributed operation: In ARA, each node owns a set of pheromone counter ϕi,j in its routing table for a link between node vi and vj . Each node controls the pheromone counter independently, 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.

Locality: The routing table and the statistic informationblock of a node are local and they are not transmitted to any other node.Multi-path: Each node maintains several paths to acertain destination. The choice of a certain route depends on the environment, e.g., link quality to the relay node.Sleep mode: In the sleep mode a node snoops, only packets which are destined to it are processed, thus saving power.

OVERHEAD OF ANTCOLONY ROUTING ALGORITHM

The expected overhead of ARA is very small, because there are no routing tables which are interchanged between the nodes. Unlike other routing algorithms, the FANT and BANT packets do not transmit much routing information. Only a unique sequence number is transmitted in the routing packets. Most route maintenance is performed through data packets, thus they do not have to transmit additional routing information. ARA only needs the information in the IP header of the data packets.

Reference……..

[1] S.Corson. Mobile ad hoc networking (manet): Routing protocol performance issues and evaluation considerations. http://www.ietf.org/rfc/rfc2501.txt.[2] Al Huda Amri and et. al. Scalability of manet routing protocols for heterogeneous and homogenous networks. Computers and Electrical Engineering, 2008.[3] Ian F. Akyildiz, Xudong Wang, and Weilin Wang. Wireless mesh networks: a survey.Computer Networks and ISDN Systems, 47(4):445–487, 15 March 2005.[4] R¨ome, Kay, and Friedemann Mattern. The design space of wireless sensor networks. 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.

ANY QUESTIONS PLZ………?

THANK YOU……………