Research on Wireless Sensor Network Localization Based on An Improved Whale Optimization Algorithm

Abstract

DV-HOP algorithm for wireless sensor network (WSN) has the disadvantage of large node positioning error and low accuracy. Firstly, the node localization model is elaborated, secondly, Fuch chaotic opposition learning is used in the initialization of the whale optimization algorithm population to improve the initial position diversity, an adaptive strategy is used for the parameters in the encircling predation behavior to avoid the algorithm falling into local optimum prematurely, Gaussian perturbation is used to update the individual positions during the iterative search to improve the global search capability, and finally IWOA is solved for the optimal value of the node localization objective function. The performance of IWOA algorithm is verified in simulation experiments, and the solution accuracy and solution quality are improved in different degrees. The IWOA algorithm demonstrates good localization results in terms of comparative data results of node localization unknown nodes, reference nodes, node density, communication radius aspects and area of the region.