ANT COLONY OPTIMIZATION ROUTING to MOBILE AD HOC NETWORKS in URBAN ENVIRONMENTS Manjula Poojary, B.Renuka # CSE Department, JNT University Vitam college of Engineering, Visakapatnam. Abstract—In this paper we present on-demand routing algorithm Enhanced Ant Colony Optimization (ACO) for Mobile ad-hoc networks(MANETs). Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. These approaches try to map the solution capability of swarms to mathematical and engineering problems. The introduced routing protocol is highly adaptive, efficient and scalable. The main goal in the design of the protocol was to reduce the overhead for routing. MAN is a collection of mobile nodes which communicate over radio. These kind of networks are very flexible, thus they do not require any existing infrastructure or central administration. Therefore, mobile ad-hoc networks are suitable for temporary communication links. Keyword— Ad-hoc network, MANET, ACO, AntHoc Net. I. INTRODUCTION Routing is the task of finding and using paths to direct data flows through a network while optimizing one or more performance measures. Hence the problem of routing can be solved using Ant Colony Optimization(ACO)[1,2], which is inspired by the ability of certain types of ants in nature to find the shortest path between their nest and a food source through a distributed process based on stigmergic communication . An important aspect of routing is that a distributed and dynamic problem, which means that the description of the problem changes over time and decentralized solutions must be adopted. As a consequence, the optimization algorithm for routing needs to adapt continuously. Here, we focus on routing in a specific type of communication networks, namely mobile ad hoc networks (MANETs)[3]. These are networks that consist entirely of wireless nodes, placed together an ad hoc manner (i.e., on- the-fly, or with minimal prior planning) and without the support of a fixed communication infrastructure. All nodes are mobile, and can enter or leave the network at any time. Data are forwarded among the nodes of the network in multi-hop fashion. MANETs are highly dynamic, have severe restrictions on the effective usable bandwidth (mainly due to the sharing of the wireless medium) ,have limited battery power available at each node. are based on the use of possibly unreliable wireless communication channels, etc. Algorithms and protocols for MANETs should be adapted to deal with these challenging properties. In this report we show how techniques from ACO can be applied to support routing in this kind of networks. We focus in particular on MANETs deployed in urban environments, which are confronted with specific conditions in terms of the network node movement patterns and the wireless radio propagation II. ROUTING IN MOBILE AD HOC NETWORKS Due to the ad hoc and dynamic nature of these networks, the topology can change continuously, and paths between sources and destinations that were initially efficient can quickly become inefficient or even infeasible. This means that routing information should be updated more regularly than in traditional wired telecommunication networks. However, this can be a problem in MANETs, because they typically have limited bandwidth and node resources, and make use of possibly unreliable wireless communication channels. New routing algorithms are therefore needed, which can give adaptivity in an efficient and robust way. Existing MANET routing algorithms can be classified as being proactive, reactive or hybrid. Proactive algorithms try to maintain up-to-date routes between all pairs of nodes in the network at all times. The advantage is that routing information is always readily available when data need to be sent, while the main disadvantage is that the algorithm needs to keep track of all topology changes, which can become difficult when there are a lot of nodes or when they are very mobile. Examples of proactive algorithms are Destination-Sequence Distance-Vector routing (DSDV)[4] and Optimized Link State Routing (OLSR) [5]. Reactive algorithms only maintain routing information that is strictly necessary: they set up routes on demand when a new communication session is started, or when a running communication session falls without route. This approach is generally more efficient, but can lead to higher delays as routing information is often not immediately available when needed. Examples of reactive routing algorithms include Dynamic Source Routing (DSR)[6] and Ad-hoc On-demand Distance-Vector routing (AODV)[7]. Finally, hybrid algorithms use both proactive and reactive elements, trying to combine the best of both worlds. An example is the Sharp Hybrid Adaptive Routing Protocol (SHARP)[8]. III.ANT COLONY OPTIMIZATION FOR ROUTING ACO routing algorithms take inspiration from the behavior of ants in nature and from the related field of ACO to solve the problem of routing in communication networks. The main source of inspiration is found in the ability of certain types of ants (e.g. the family of Argentine ants Linepithema Humile) to find the shortest path between their nest and a food source using a volatile chemical substance called pheromone. Ants traveling Manjula Poojary et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 2 (6) , 2011, 2776-2779 2776