Optimizing Wireless Sensor Network Deployment with Hybrid Genetic Algorithm

Abstract

Wireless Sensor Networks (WSNs) have vital applications in diverse domains including military environmental monitoring disaster management and security systems. Achieving both m-connectivity and k-coverage is critical for ensuring network reliability and effective area monitoring. In this study we propose a Hybrid Genetic Algorithm for Wireless Sensor Network deployment (HGA-WSN) that combines genetic algorithms with problem-specific local search to efficiently explore solutions. The paper compares HGA-WSN against a standard genetic algorithm a greedy heuristic and a standard simulated annealing implementation. The experiments encompass various problem instances considering different $\mathrm{m}\mathrm{k}$ and target count values. The results validate HGA-WSN's superior performance in terms of sensor count reduction. The algorithm's efficacy in optimizing WSN deployment across diverse applications is evident from its rapid convergence and enhanced connectivity and coverage achievements. © 2023 Elsevier B.V. All rights reserved.