Positioning optimisation based on particle quality prediction in wireless sensor networks. Issue 2 (1st March 2019)

• Record Type: Journal Article
• Authors: Zhang, Chunjiong; Xie, Tao; Yang, Kai; Ma, Hui; Xie, Yuxia; Xu, Yueyao; Luo, Pan
• Is Part Of: IET networks. Volume 8:Issue 2(2019)
• Page Start: 107
• Page End: 113
• Publication Date: 2019-03-01
• Subjects: particle swarm optimisation -- particle filtering (numerical methods) -- wireless sensor networks -- probability; nonlinear variations -- reference distribution function -- star flags -- maximum inclusive distance -- maximum probability -- high‐quality particle swarm -- Euclidean distance -- positioning accuracy -- target nodes -- positioning optimisation -- particle quality prediction -- wireless sensor networks -- particle degradation problem -- particle filter algorithm -- positioning error reduction -- particle weights reduction -- computational complexity; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2018.5072
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract : The particle degradation problem of particle filter (PF) algorithm caused by reduction of particle weights significantly influences the positioning accuracy of target nodes in wireless sensor networks. This study presents a predictor to obtain the particle swarm of high quality by calculating non‐linear variations of ranging between particles and flags and modifying the reference distribution function. To this end, probability variations of distances between particles and star flags are calculated and the maximum inclusive distance using the maximum probability of high‐quality particle swarm is obtained. The quality of particles is valued by the Euclidean distance between the predicted and real observations, and hereafter particles of high quality are contained in spherical coordinate system using the distance as diameter. The simulation results show that the proposed algorithm is robust and the computational complexity is low. The method can effectively improve the positioning accuracy and reduce the positioning error of target nodes.