Particle Swarm Optimized Power Consumption of Trilateration

TL;DR

This paper applies particle swarm optimization to enhance trilateration-based localization in wireless sensor networks, reducing localization time and energy consumption while increasing the number of fully localized nodes.

Contribution

It introduces a novel application of single- and multi-objective PSO to optimize wireless sensor localization, achieving significant algorithmic improvements.

Findings

Up to 32% improvement in localization objectives

Effective multi-objective PSO for energy and time minimization

Enhanced localization accuracy and efficiency

Abstract

Trilateration-based localization (TBL) has become a corner stone of modern technology. 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 32% in the evaluated objectives.