Balancing Coverage and Clustering in Mobile WSNs: An Optimization-Based Approach

Abstract

Wireless Sensor Networks' (WSNs') performance and dependability are severely hampered by coverage gaps, particularly in harsh or dynamic situations.  An integrated approach for effectively identifying and patching coverage gaps with mobile sensor nodes is presented in this paper.  To find any coverage gaps, the suggested method starts with Delaunay Triangulation, which builds a mesh network from fixed sensor locations.  The boundaries of uncovered sections are then precisely located using virtual edge detection.  In order to prioritize healing operations, the area of each coverage hole is then computed. A Particle Swarm Optimization (PSO)-based deployment technique is presented in order to efficiently restore coverage, allowing mobile sensor nodes to be positioned as efficiently as possible inside the gaps that have been found.  The approach ensures maximum coverage restoration and minimal energy consumption while dynamically adjusting to shifting network topologies.  According to simulation results, the suggested system performs noticeably better with regard to of coverage efficiency, agility, and computational scalability than conventional static models.  A strong and clever system for automated coverage maintaining in next-generation WSNs is presented in this work.