STATISTICAL MARKOV MODEL BASED NATURAL INSPIRED GLOWWORM SWARM MULTI-OBJECTIVE OPTIMIZATION FOR ENERGY EFFICIENT DATA DELIVERY IN MANET

Abstract

An interference and collision is a significant problem to be solved in Mobile Ad Hoc Network (MANET). Few techniques were developed in existing work to perform energy aware routing in MANET. But, handling both interference and collision on route path was remained open issues which increases the energy usage and reduces the throughput of MANET.  In order to overcome such limitations, Statistical Markov Model Based Natural Inspired Glowworm Multi-objective Optimization (SMM-NIGMO) technique is proposed.  The SMM-NIGMO technique contains three key processes namely interference prediction, optimal node selection, route path identification. The SMM-NIGMO technique at first proposes a Statistical Markov Model to determine the interference level of each mobile node in MANET at the time ‘ ’. After  that, SMM-NIGMO Technique designs a Natural Inspired Glowworm Swarm Multi-Objective Optimization (NIGSMO) algorithm to carry out the optimal node selection process in MANET. During this process, SMM-NIGMO Technique finds the mobile nodes with lower inferences and higher residual energy as optimal in dynamic environment. After discovering the best nodes, SMM-NIGMO Technique uses the Request-To-Send (RTS) and Clear-To-Send (CTS) mechanism for identifying the best route path in MANET. During the route path finding process, SMM-NIGMO Technique selects the mobile node to transmit the packets to destination without any collisions.   As a result, SMM-NIGMO Technique enhances the performance of energy efficient data delivery in MANET as compared to existing works. The simulation of SMM-NIGMO Technique is performed using parameters such as throughput, energy consumption, packet loss rate and end to end delay. The simulation result depicts that the SMM-NIGMO Technique is able to improve the throughput and also reduces the energy consumption of data transmission in MANET as compared to state-of-the-art works.