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. INDEX TERMS Wireless sensor network, non-line of sight, mobile localization, received signal strength indicator, fuzzy logic method.

A Grey Model and Mixture Gaussian Residual Analysis-Based Position Estimator in an Indoor Environment

“…To address this problem, numerous mathematical methods were applied to WSN positioning, such as the linear-programming (LP) approach [ 12 ], min-max algorithm [ 13 ] and robust multilateration algorithm [ 14 ]. Furthermore, with exploiting the idea of grouping, a robust estimator has been proposed in [ 15 , 16 , 17 ] by converting the positioning problem into a generalized trust region sub-problem [ 9 ]. This method changes the global convex optimization problem into a non-convex problem for each sub-region so that it can be solved easily by ML.…”

“…These methods firstly identify which condition the mobile node is in and then use different models to process different conditions. NLOS error identification methods can be divided into two categories: hard decision [ 21 , 22 , 23 , 24 , 25 ] and soft decision [ 4 , 6 , 9 ]. In [ 21 ], a probabilistic location selection method through pedestrian dead reckoning is proposed.…”

“…We can adjust the existing method according to our model to obtain a better identification scheme. In [ 6 , 9 ], the authors propose a hierarchical voting algorithm to detect the environment and give the degree of membership to each condition (LOS or NLOS).…”

“…In the range-based localization system, the practical hardware of beacon nodes can be UWB (ultra-wideband), Bluetooth, wireless local area network (WLAN), etc. These modules adopt different ranging methods which mainly include time of arrival (TOA) [ 7 ], roundtrip time of flight (RTOF), time difference of arrival (TDOA) [ 8 ], received signal strength (RSS) [ 9 ], angle of arrival (AOA) [ 10 ] and phase difference of arrival (PDOA). When the propagation path is direct, the environment is line of sight (LOS) and thus we can get accurate position estimation.…”

Uma Proposta de Protocolo MAC com Redução de Sobrecarga para Redes de Sensores sem Fio

“…Para avaliar a qualidade do sinal de um enlace e estimar a distância entre o transmissor e o receptor, muitos trabalhos têm usado o indicador de intensidade do sinal recebido (RSSI, do inglês Received Signal Strength Indicator) [10], [11]. Embora a distância estimada com base no RSSI não seja precisaé possível reduzir o erro no posicionamento utilizando técnicas como a apresentada em [10].…”