EFFICIENT COVERAGE OF SENSORS IN A WSN USING AMODIFIED HYBRID PSO AND ALO ALGORITHMS

Abstract

The issue of sensor coverage in Wireless Sensor Networks (WSNs) is crucial, particularly as these networks are deployed in military applications for the armed forces as well as in civilian health applications. Therefore, improving coverage and communication while minimizing interference between sensors is essential. This paper presents a hybrid meta-heuristic approach to optimizing node deployment in WSNs using a modified Particle Swarm Optimization (mPSO) and Ant Lion Optimization (ALO) algorithms. The Particle Swarm Optimization (PSO) algorithm was applied for global search, while the ALO focused on internal search within the Region of
Interest (ROI). Initially, nodes are deployed randomly within the ROI. The algorithm then detects uncovered gaps and iteratively enhances node placement, leading to an improved coverage ratio and minimized node overlap. The results of the hybrid meta-heuristic algorithm show improved performance compared to using PSO and ALO separately. This approach leads to an enhanced network lifetime and energy consumption of the WSN.