Energy-Efficient Coverage Enhancement Strategy for WSNs Based on a Competitive Learning Optimizer
Jin Xia Ren, Yibo Li · IEEE Internet of Things Journal · 2025
Energy-efficient coverage enhancement (EEC) is a highly non-convex and challenging optimization problem in wireless sensor networks (WSNs) deployment. Traditional intelligent optimization algorithms often suffer from premature convergence and low efficiency when solving the EEC problem. In this paper, a competitive learning optimizer (CLO) is proposed and upgraded to a multi-objective competitive learning optimizer (MOCLO), which is inspired by the behavior of human competitive learning in workplace. In order to verify the single-objective optimization capability of CLO, it is applied to the coverage problem in 2D and 3D WSNs, and the simulation results indicate that CLO achieves the overall best results in terms of solution accuracy, stability and convergence by comparing with five well-regarded optimization algorithms. For solving the EEC problem in 2D and 3D WSNs, MOCLO is applied. By comparing three popular multi-objective optimization algorithms, it is found that MOCLO can consume less energy to achieve higher coverage, which proves the effectiveness and superiority of MOCLO in addressing the EEC problem.