Efficient deployment of Wireless Sensor Networks (WSNs) is critical to ensuring optimal performance across key metrics such as coverage, connectivity, energy efficiency, and network longevity. Traditional deployment strategies such as random, grid-based, and deterministic placements often fail to accommodate heterogeneous node capabilities or adapt to dynamic environmental conditions. This study proposes a Particle Swarm Optimization (PSO)-based deployment framework tailored for heterogeneous WSNs. The framework incorporates intelligent role assignment, metaheuristic optimization, and adaptive maintenance phases to balance coverage quality, minimize energy consumption, and enhance fault tolerance. A comprehensive simulation-based evaluation involving nine deployment strategies demonstrates that the proposed PSO-based approach achieves superior performance, including an average coverage of 91.4 ± 1.8%, extended operational lifetime exceeding 3,400 rounds, and improved connectivity robustness. The framework also exhibits strong scalability and resilience in the presence of node failures. These findings establish the proposed deployment model as a viable solution for real-world, large-scale, and energy-constrained WSN applications.
Rahul Priyadarshi (Fri,) studied this question.