Coverage Enhancement Strategy for WSNs Based on Multiobjective Ant Lion Optimizer

In order to improve the quality of service (QoS) 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 article improves the classic multiobjective ant lion optimization (MOALO) algorithm and proposes an improved MOALO algorithm based on fast nondominated sorting (NSIMOALO). First, we use the idea of fast nondominated 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. Second, 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 the 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
$200 {}\times {}300$
m. Compared with the MOALO algorithm, the NSGA-II algorithm, and the nondominated ordered multiobjective flower pollination algorithm (NSMOFPA), the coverage rate of the NSIMOALO algorithm is increased by 12.753%, 12.413%, and 4.492%, respectively, and the sensor nodes average moving distance is decreased by 2.551, 2.316, and 4.457 m, respectively.