GA-based approaches for Optimization energy and coverage in wireless sensor network: State of the art

Abstract

Wireless sensor networks (WSNs) have become one of the leading research subjects in computer science over the last few years. WSNs are resourceconstrained concerning available energy, bandwidth, processing power, and memory space. Thus, optimization is essential to get the best results of these constraining parameters. Due to the advantages of genetic algorithms, different GA methods have been implemented to optimize different objectives like energy, coverage, QoS, and many other metrics. This paper presents a survey on the current state of the art during the last four years in wireless sensor network optimization using genetic algorithms to optimize energy consumption and the coverage of WSNs to give an up-to-date background to researchers in this field. Also, a classification of the works, based on the used methods, is provided.