nodeWSNsec: A Hybrid Metaheuristic Approach for Reliable Security and Node Deployment in Wireless Sensor Networks

Abstract

Efficient and reliable node deployment in Wireless Sensor Networks is crucial for optimizing coverage of the area, connectivity among nodes, and energy efficiency. Random deployment of nodes may lead to coverage gaps, connectivity issues and reduce network lifetime. This study proposes a hybrid metaheuristic approach combining a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to address the challenges of energy-efficient and reliable node deployment. The GA-PSO hybrid leverages GA's strong exploration capabilities and PSO's rapid convergence, achieving an optimum stability between coverage and energy consumption. The performance of the proposed approach is evaluated against GA and PSO alone and the innovatory metaheuristic-based Competitive Multi-Objective Marine Predators Algorithm (CMOMPA) across varying sensing ranges. Simulation results demonstrate that GA-PSO requires 15 to 25% fewer sensor nodes and maintains 95% or more area coverage while maintaining connectivity in comparison to the standalone GA or PSO algorithm. The proposed algorithm also dominates CMOMPA when compared for long sensing and communication range in terms of higher coverage, improved connectivity, and reduced deployment time while requiring fewer sensor nodes. This study also explores key trade-offs in WSN deployment and highlights future research directions, including heterogeneous node deployment, mobile WSNs, and enhanced multi-objective optimization techniques. The findings underscore the effectiveness of hybrid metaheuristics in improving WSN performance, offering a promising approach for real-world applications such as environmental monitoring, smart cities, smart agriculture, disaster response, and IIoT.