A Study of WSN Localization Based on the Enhanced NGO Algorithm

In this paper, an enhanced INGO optimization algorithm is proposed to solve the problem of large positioning error of the original DV-Hop algorithm in wireless sensor networks. By introducing cubic chaotic mapping and increasing the diversity of population initialization to expand the search scope, the sensor node location information can be collected more widely, so that the algorithm can search for the best solution as far as possible. In addition, a hybrid method of optimal - worst reverse learning and lens imaging reverse learning strategy is added to help the algorithm get rid of the local extreme value easily and improve the positioning accuracy. By comparing with the localization results of the classical DV-Hop localization algorithm, SSADV-Hop algorithm, and WOADV-Hop algorithm, the INGO algorithm reduces the average normalized localization error when the beacon node, communication radius, and total number of nodes are different.