Energy Efficient Localization in Wireless Sensor Networks Using Computational Intelligence

Abstract

Wireless Sensor Networks consist of many sensing devices which are distributed inside of a given area. Each sensor node consists of multiple heterogeneous components such as power supply, CPU, memory, and a transceiver. Since the location of sensors is needed in most of the WSNs, Trilateration-based localization (TBL) has been used to locate the sensors in the network. This study formulates the concern on how wireless sensor networks can take advantage of the computational intelligent techniques using both single and multi-objective particle swarm optimization (PSO) with an overall aim of concurrently minimizing the required time for localization, minimizing energy consumed during localization, and maximizing the number of nodes fully localized through the adjustment of wireless sensor transmission ranges while using TBL process. A parameter-study of the applied PSO variants is performed, leading to results that show algorithmic improvements of up to 21% in the evaluated objectives.