Wireless Sensor Network coverage optimization Based on an Improved Pied kingfisher optimization algorithm

Abstract

The node coverage optimization problem of wireless sensor network (WSN) is a critical challenge in practical applications of WSN. In this paper, in order to solve the problem of uneven distribution and low coverage when WSN nodes are randomly deployed, a WSN coverage optimization strategy based on the improved pied Kingfisher optimization (IPKO) is proposed. The proposed improved pied Kingfisher optimization consists of three steps. First, a logistic-sine map is incorporated during population initialization to ensure a more homogeneous population distribution. Second, a subtractive averaging strategy is employed to enhance the convergence ability of the algorithm and strengthen the global search capability. Third, a subtraction-average-based optimizer is integrated during the commensalism phase to expand the population search range. The results of the simulation experiments prove that the performance of IPKO excellent in WSN coverage optimization. Especially in some large network scenarios, IPKO achieves a coverage of 97.86%, which represents a 20% improvement over the initial coverage. Simulation results show that our WSN coverage optimization strategy is efficient and the network coverage can be enhanced dramatically by the proposed IPKO.