Particle Swarm Optimized Power Consumption of Trilateration
This work addresses energy efficiency and performance in wireless sensor networks, but it is incremental as it applies known optimization techniques to a specific domain.
The study tackled the problem of optimizing trilateration-based localization in wireless sensor networks by using particle swarm optimization to minimize localization time and energy consumption while maximizing localized nodes, achieving algorithmic improvements of up to 32%.
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.