Coverage Enhancement Strategy for WSNs Based on Multiobjective Ant Lion Optimizer

Abstract

In order to improve the quality of service and prolong the network lifetime of wireless sensor networks (WSNs), optimizing the network coverage rate and the sensor nodes moving distance in the secondary deployment process has become a crucial research object. Aiming at these two goals, this paper improves the classic multi-objective ant lion optimization algorithm, and proposes an improved multi-objective ant lion optimization algorithm based on fast non-dominated sorting (NSIMOALO). Firstly, we use the idea of fast non-dominated sorting algorithm and elite strategy of NSGA-II, which avoids the algorithm from falling into the local optimal solution and improves the solution accuracy of the algorithm. Secondly, a more reasonable congestion calculation equation is proposed, which greatly increases the diversity of the population. Finally, we introduce Lévy flight to update the ant position by dynamically changing the weight coefficients among the antlion, elite antlion and Lévy flight, which improves the global optimization ability of the algorithm. The standard function simulation results show that NSIMOALO algorithm has higher convergence and coverage. The algorithm is applied to WSNs sensor nodes deployment, and 80 sensor nodes are deployed in a monitoring area of 200m×300m. Compared with MOALO algorithm, NSGA-II and NSMOFPA, the coverage rate of NSIMOALO algorithm is increased by 12.753%, 12.413% and 4.492% respectively, and the sensor nodes average moving distance is decreased by 2.551m, 2.316m and 4.457m respectively.