Genetic Algorithm Based Energy Efficient Optimization Strategies in Wireless Sensor Networks: A Survey

TL;DR: It is proposed that future research should focus more on the use of Stochastic Network State Model to model the behavior of sensor nodes and then predict energy consumption by a sensor node with minimum overheads in communications to base station.

Abstract: The past decade has witnessed tremendous growth in research in various issues of concern in wireless sensor networks (WSNs) such as energy conservation, node deployment, routing protocols, Quality of services (QoS) management, security, energy harvesting etc. Most of the issues involved in WSNs research are conflicting in nature and hence require optimization strategies that are capable of mitigating the conflicting objectives such as life time maximization, node coverage and reliability among others. In this survey paper, we stimulate new research initiatives by reviewing how a more holistic view to optimization can be achieved through the use of genetic algorithms (GAs) in sensor network optimization. We review how genetic algorithms have been used to model sensor communication, in clustering and routing problems. We also provide a performance evaluation of various GA-based optimization strategies. Our observations shows that while a number of algorithms try to select the best cluster headers or routing path based on some metric, the process normally introduces overheads in communication which in turn leads to more energy dissipation. We propose that future research should focus more on the use of Stochastic Network State Model to model the behavior of sensor nodes and then predict energy consumption by a sensor node with minimum overheads in communications to base station.