Analysis of Energy Efficient Routing Protocol Using Nature Inspired Optimization Methods for AD Hoc Networks

Abstract

Due to wide range of applications, Mobile Ad-hoc Networks (MANETs) in recent years have gained a lot of attention .MANETs can operate without any existing fixed infrastructure and have rapid changes in the network topology. Preparing and maintaining the routing table in communication networks, especially in MANETs, is a newlinechallenging task. Nodes dynamics in MANETs make the management of routing tables a complex job to be carried out. There exist many solutions for the routing problem in MANETs; each one having its advantages and limitations. These existing solutions need to be improved to achieve better performance with respect to various parameters. In fact, there is a new generation of bio-inspired routing protocols that have the potential to provide better performance than traditional protocols with the help of moving agents. Agents collaborate with each other to update the routing table quickly with less delay. newline newlineAnt Colony Optimization (ACO) is one of the most interesting bio inspired metaheuristics techniques. It simulates ant behaviour to solve complex combinatorial problems. ACO algorithms include the method of optimization and reinforcement learning. ACO algorithms are decentralized, robust and resilient. These features make ants a good choice for taking routing decisions; where not only shorter paths need to be found, but also there is a need to achieve higher performance and greater reliability over the traditional protocols. It has been observed that ant algorithms influence the performance of the network due to their self organization characteristics. ACO algorithms are based on agent systems and work with a group of ants that allow a high adaptation to the dynamic topology of MANETs. In contrast to other approaches for the formation of routing information, AC algorithms resulted in passing of local information at a fast pace by transmitting collected information to neighbours in the network. Each node maintains a routing table with entries for all of its neighbours which are called as the pheromone tab