A Fuzzy C-Means and Hierarchical Voting Based RSSI Quantify Localization Method for Wireless Sensor Network

Abstract

In recent years, wireless sensor networks (WSN) have been widely used in many areas due to the rapid development of wireless communication and microelectronics. The positioning of mobile nodes is one of the key applications of WSN. In this paper, we propose a received signal strength indicator (RSSI)-based positioning scheme. We use the Fuzzy C-Means (FCM) algorithm to provide a practical quantized threshold designer for RSSI data, which is used to convert quantized data based on received signal strength into the distance. Then, we propose a hierarchical voting-based positioning scheme for calculating the position of the mobile node. The proposed algorithm can weaken the influence of non-line of sight (NLOS) error on the positioning result. And the simulation results show that it has better performance than particle swarm optimization (PSO) and quantized distributed gradient target localization using quantized received signal strength (QDG-QRSS) in most cases. The actual experimental results show that the proposed algorithm can also get higher localization accuracy in the indoor environment, and it is robust to the NLOS errors.