Maximising Coverage Under Connectivity Constraint Utilising Nature-Inspired Algorithms: A Comparative Analysis

Coverage and connectivity represent two of the most fundamental challenges in Wireless Sensor Networks stemming from their profound influence on network performance. This paper addresses these issues within the planned deployment context and presents meta-heuristic algorithms based on nature-inspired algorithms combined with graph theory to solve the problem. Four utilised algorithms are Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, and Artificial Bee Colony. Proposed methods are tested through Matlab simulations to verify the theoretical findings and practical effectiveness. The main contribution is how the initial population is meticulously generated, resulting in better performance to some extent in several case studies compared with other research findings, especially when applying the bee algorithm. Despite simplistic model assumptions, the methods still showcase the potential for practical application deployed in irregular shape areas, such as building environments with obstacles.