Grid quorum‐based spatial coverage in mobile wireless sensor networks using nature‐inspired firefly algorithm

Abstract

Optimization and design of mobile wireless sensor networks (MWSNs) must assure adequate spatial coverage of the site. The spatial coverage optimization aims to enrich discoverability of MWSN by specifying mobile sensors geographical locations in order to maximize their coverage. In this paper, an enhanced metaheuristic algorithm called “firefly algorithm with crossover and detection phases” is introduced for optimizing the area coverage percentage of MWSN. The proposed algorithm is tested on many datasets with different criterions and compared with other algorithms including differential evolution, whale optimization algorithm, and flower pollination algorithm. The experimental results are analysed with one‐way ANOVA test. In addition, the proposed algorithm is compared with particle swarm optimization, and the results are analysed with Wilcoxon signed‐rank test. The overall analysis results prove the prosperity and efficient exploration of the proposed algorithm.

A modified firefly algorithm based on neighborhood search

“…33 Eldrandaly et al used FA to optimize the spatial coverage in wireless sensor networks (WSN). 34 Simulation results showed the new algorithm outperformed flower pollination algorithm, differential evolution (DE) and whale optimization algorithm. Tian et al presented a modified clustering method based on FA, 35 in which FA was used to extract the states of multiple targets.…”

“…To deal with the superior solution set search problem, a novel FA was designed based on the quantitative demand level of users 33 . Eldrandaly et al used FA to optimize the spatial coverage in wireless sensor networks (WSN) 34 . Simulation results showed the new algorithm outperformed flower pollination algorithm, differential evolution (DE) and whale optimization algorithm.…”