Statistical Markov Model Based Natural Inspired Glowworm Swarm Multi-Objective Optimization for Energy Efficient Data Delivery in MANET.

Mobile Ad Hoc Network (MANET) is an infra-structure less multi-hop network where the mobile nodes are moved randomly. An interference and collision is a significant problem to be solved in MANET. Handling both interference and collision on route path 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 at first proposes a Statistical Markov Model to determine the interference level of each mobile node in MANET at the time" and reduces the interference level. 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. Then, Request-To-Send (RTS) and Clear-To-Send (CTS) mechanism are used in SMM-NIGMO technique for selecting route path and minimizing the collision during the data transmission. With the reduction of interference and collision on the route path, SMM-NIGMO technique enhances the throughput and also lessens the energy as compared to existing works. The performance of the proposed technique validated using following parameters such as energy consumption, throughput, End-to-End delay, Packet delivery ratio and Packet loss rate. The simulation result depicts that the SMM-NIGMO Technique is able to improve the throughput and also reduces the energy consumption, End-to-End delay of data transmission in MANET as compared to state-of-the-art works. [ABSTRACT FROM AUTHOR]