Energy-Efficient and Coverage-Optimized Wireless Sensor Networks using a Multi-Objective Jellyfish Search Algorithm

This paper investigates the application of a multi-objective metaheuristic algorithm, the Multi-Objectives Jellyfish Search (MOJS), to enhance the performance and reliability of Wireless Sensor Networks (WSNs). WSNs, a recent technological advancement, facilitate the strategic deployment of numerous miniature, battery-powered sensors to monitor and gather data from diverse environmental settings. However, the implementation of WSNs faces significant challenges due to limited energy resources. We propose a novel approach, termed WSN-MOJS, which aims to optimize WSN implementation by maximizing coverage and minimizing energy consumption. Simulations were conducted using MATLAB software to design a network consisting of multiple sensor nodes to monitor a designated zone. The process begins by randomly initializing candidate node placements, which are then evaluated using two objective functions as follows: total coverage, and energy expended by the sensor nodes. The MOJS updating process is iteratively applied over multiple iterations. To test the performance of our WSN-MOJS approach, we conducted several simulations by varying the number of nodes, candidate solutions, and iterations. The results indicate that the proposed WSN-MOJS algorithm ensures maximum coverage with an average number of nodes and minimizes energy consumption within a minimal computation complexity due to its exploration and exploitation capabilities. Increasing the number of candidate solutions and iterations significantly improves the Pareto front. Consequently, the non-dominated solutions become well-distributed, and the fitness values are enhanced.