Efficient Path Planning for Wireless Sensor Networks: Minimizing Exposure with a Modified Bat Algorithm

In Wireless Sensor Networks (WSNs), achieving effective coverage while minimizing the exposure of sensor nodes to potential threats is of paramount importance. This study addresses the Minimal Exposure Path (MEP) Problem, where the objective is to find a path that strikes a balance between optimizing coverage and minimizing sensor node exposure. To tackle this challenge, the study proposes an innovative approach by harnessing the power of the Modified BAT Algorithm (MBA). The Modified BAT Algorithm is an advanced optimization technique inspired by the echolocation behaviour of bats. It offers enhanced exploration and exploitation capabilities through non-linear parameter adjustments, frequency adaptation, and local search mechanisms. In this work, the MBA adapt to the MEP problem, leveraging its unique features to find solutions that substantially improve coverage while efficiently reducing sensor node exposure. The core of our approach involves encoding potential paths as sequences of sensor nodes, aiming to optimize their arrangement. By formulating the problem as an optimization task, the MBA-driven algorithm guides the search process to identify paths that not only provide comprehensive coverage of the target area but also minimize the number of exposed sensors along the path. Through rigorous experimentation and comparative analysis, the research study demonstrates the efficacy of the proposed approach in solving the MEP problem. By utilizing the Modified BAT Algorithm's adaptability and optimization prowess, significant enhancements achieved in coverage quality while effectively mitigating sensor node exposure risks. This work not only contributes to the advancement of solutions for enhancing WSN performance but also showcases the potential of nature-inspired algorithms in addressing complex optimization challenges in real-world applications.