Zone based ant colony routing in mobile ad-hoc network

Ant colony optimization (ACO) is a stochastic approach for solving combinatorial optimization problems like routing in computer networks. The idea of this optimization is based on the food accumulation methodology of the ant community. Zone based routing algorithms is build on the concept of individual node´s position for routing of packets in mobile ad-hoc networks. Here the nodes´ position can be further utilized to discover routes by the Ants in optimized way. Position based routing algorithms (POSANT) had some significant loopholes to find route (source to destination) like it never guarantees the route would be the shortest one, in cases while it is able to find it. On the contrary, routing algorithms which are based on ant colony optimization find routing paths that are close in length to the shortest paths. The drawback of these algorithms is the large number of control messages that needs to be sent or the long delay before the routes are established from a source to a destination. Here In this paper we have used Zone based ANT colony using Clustering which assures to find shortest route using the DIR principle (In this principle, the source or intermediate node transmits message to several neighbors and the node whose direction is closest to the direction of destination gets selected as the next hop forwarding node.) together with minimum overhead for route discovery and mobility management. Unlike other Zone based approach, in clustering it is not required to consider zone related information of each node while finding shortest path. Here, it is being proposed a new routing algorithm for mobile ad hoc network by combining the concept of Ant Colony approach and Zone based routing approach using clustering to get shortest path with small number of control messages to minimize the overhead. Simulations show that Zone Based ant colony routing algorithm has relatively short route establishment overhead than other zone based ant colony algorithms in highly mob- ile scenarios.