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

The past decade has witnessed tremendous growth inresearch in various issues of concern in wireless sensornetworks (WSNs) such as energy conservation, nodedeployment, routing protocols, Quality of services (QoS)management, security, energy harvesting etc. Most of theissues involved in WSNs research are conflicting in natureand hence require optimization strategies that are capableof mitigating the conflicting objectives such as life timemaximization, node coverage and reliability among others.In this survey paper, we stimulate new research initiativesby reviewing how a more holistic view to optimization canbe achieved through the use of genetic algorithms (GAs) insensor network optimization. We review how geneticalgorithms have been used to model sensor communication,in clustering and routing problems. We also provide aperformance evaluation of various GA-based optimizationstrategies. Our observations shows that while a number ofalgorithms try to select the best cluster headers or routingpath based on some metric, the process normally introducesoverheads in communication which in turn leads to moreenergy dissipation. We propose that future research shouldfocus more on the use of Stochastic Network State Modelto model the behavior of sensor nodes and then predictenergy consumption by a sensor node with minimumoverheads in communications to base station

Keywords:

wireless sensor networks , Geneticalgorithms , optimization strategies