Energy Efficient Multipath Routing Using Multi-Objective Grey Wolf Optimizer based Dynamic Source Routing Algorithm for MANET

Abstract

Network disconnection due to energy depletion problem is higher in Mobile Adhoc Network (MANET) as the communication among nodes happens without any centralized management. Often, Dynamic Source Routing (DSR) protocol is considered the best suited reactive routing protocol for MANET. However, DSR does not consider energy consumption as a parameter for routing and suffers from same energy depletion problem. Optimized routing protocols were developed to minimize the energy constraint problem; but still the problem exists and impacts the performance mainly because these techniques support only single-path routing. Introducing multi-path routing can balance the data transmission through multiple paths with reliable and energy efficient routing. Hence this paper stresses on resolving the energy constraint problem in MANET routing through energy optimization and also implementing multi-path routing to improve the data transmission performance. To achieve this objective, Multi objective Grey Wolf Optimization (MGWO) based DSR protocol is proposed which utilizes energy, delay, lifetime, and link quality as objective parameters. Initially, the routes are discovered based on the DSR strategy and the objective parameters are estimated using which the MGWO models an objective or fitness function. Fitness values are computed for each available path and are sorted in the best order as in wolves’ hierarchy of MGWO. Finally, multi-path data transmission will be performed over the high fitness paths (alpha, beta and delta) while the next best omega paths will be utilized when the energy of dominant paths depletes below the level of omega paths. This MGWO-DSR protocol effectively minimizes the energy depletion problem and also ensures reliable multi-path data transmission in MANET. The NS-2 simulation results also illustrates that the MGWO-DSR provides better multi-path routing than other compared models in terms of lower energy consumption, delay and hop count while higher throughput, Peak Signal to Noise Ratio (PSNR) and network lifetime.