WSN Localization Method Based on Hybrid PSO-GRNN Approach

Abstract

Determining the localization and tracking of sensor nodes in indoor environments is the goal of this study. The difficulty of significant estimation errors in target localization brought on by unpredictable noise in received signal strength indicator (RSSI) readings, particularly in indoor environments, is a major area of research right now. This study proposed a hybrid technique called particle swarm optimization-generalized regression neural network (PSO-GRNN) to improve the sensor nodes' ability to estimate location and target tracking with more precision, as an alternative to conventional RSSI-based strategy. The GRNN algorithm can use the RSSI values as initiation data of the algorithm of GRNN to locate the location and tracing of the target node. An essential component of the GRNN architecture is the spread constant(σ), The method of trial-and-error to select a value of spread constant(σ), which is insecure and does not always yield the best result, is used to choose the parameter. The PSO method is used to determine the optimal GRNN spread constant value. The hybrid PSO-GRNN method was used to overcome these drawbacks and enhance localization and target tracking accuracy without the need for further apparatuses. The tracking algorithm PSO-GRNN the hybrid outperformed the conventional LNSM technique and produced impressive results. Comparing the proposed method to the conventional RSSI, a considerable gain of 87.58% is possible.