Optimizing Wireless Sensor Network Deployment with Hybrid Genetic Algorithm

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.