scholarly journals A Non-Parametric Propagation Condition Identification Method and Non-Line of Sight Mitigation Algorithm for Wireless Sensor Network

2016 ◽  
Vol 10 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Hao Chu ◽  
Cheng-dong Wu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Propagation Condition ◽  
Localization Accuracy ◽  
Chi Squared ◽  
Potential Applications ◽  
Unknown Node ◽  
Non Line Of Sight

The wireless sensor network (WSN) has received increasing attention since it has many potential applications such as the internet of things and smart city. The localization technology is critical for the application of the WSN. The obstacles induce the larger non-line of sight (NLOS) error and it may decrease the localization accuracy. In this paper, we mainly investigate the non-line of sight localization problem for WSN. Firstly, the Pearson's chi-squared testing is employed to identify the propagation condition. Secondly, the particle swarm optimization based localization method is proposed to estimate the position of unknown node. Finally the simulation experiments are implemented. The simulation results show that the proposed method owns higher localization accuracy when compared with other two methods.

scholarly journals A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network

2019 ◽  
Vol 10 (1) ◽  
pp. 6
Author(s):  
Long Cheng ◽  
Mingkun Xue ◽  
Ze Liu ◽  
Yong Wang
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Data Association ◽  
Position Estimation ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Network Technology ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Non Line Of Sight

As one of the core technologies of the Internet of Things, wireless sensor network technology is widely used in indoor localization systems. Considering that sensors can be deployed to non-line-of-sight (NLOS) environments to collect information, wireless sensor network technology is used to locate positions in complex NLOS environments to meet the growing positioning needs of people. In this paper, we propose a novel time of arrival (TOA)-based localization scheme. We regard the line-of-sight (LOS) environment and non-line-of-sight environment in wireless positioning as a Markov process with two interactive models. In the NLOS model, we propose a modified probabilistic data association (MPDA) algorithm to reduce the NLOS errors in position estimation. After the NLOS recognition, if the number of correct positions is zero continuously, it will lead to inaccurate localization. In this paper, the NLOS tracer method is proposed to solve this problem to improve the robustness of the probabilistic data association algorithm. The simulation and experimental results show that the proposed algorithm can mitigate the influence of NLOS errors and achieve a higher localization accuracy when compared with the existing methods.

Research on RSSI and Triangles Deformation Based APIT Localization Algorithm for Wireless Sensor Networks

2014 ◽  
Vol 998-999 ◽  
pp. 1305-1310
Author(s):  
Fei Liu ◽  
Guang Zeng Feng
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Localization Error ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Sparse Network ◽  
Unknown Node

The localization accuracy of traditional APIT localization algorithm for wireless sensor network depends on the Approximate Perfect Point-In-Triangulation Test (APIT), and the localization error can be promoted in sparse network. We design one improved localization algorithm (RTD-APIT) based on APIT by using the RSSI and the triangles deformation. RTD-APIT uses the RSSI to improve the APIT for achieving the preliminary location of unknown node, and expand or deform the triangles for solving the Point-In-Triangulation (PIT) problem well and enhancing the localization. Simulation shows RTD-APIT can reduce the localization error effectively, and it also promote the localization coverage.

Sign in / Sign up

Export Citation Format

Share Document