An Improved MCB Localization Algorithm Based on Weighted RSSI and Motion Prediction

Chunyue Zhou1, Hui Tian2 and Baitong Zhong3

  1. Laboratory of Communication Engineering
    Beijing Jiaotong University, China
    zhouchunyue@hotmail.com
  2. School of Information and Communication Technology
    Griffith University, Australia
    hui.tian@griffith.edu.au
  3. Hunan Electronic Technology Vocational College
    Hunan Province, China
    471727138@qq.com

Abstract

Aiming at the problem of low sampling efficiency and high demand for anchor node density of traditional Monte Carlo Localization Boxed algorithm, an improved algorithm based on historical anchor node information and the received signal strength indicator (RSSI) ranging weight is proposed which can effectively constrain sampling area of the node to be located. Moreover, the RSSI ranging of the surrounding anchors and the neighbor nodes is used to provide references for the position sampling weights of the nodes to be located, an improved motion model is proposed to further restrict the sampling area in direction. The simulation results show that the improved Monte Carlo Localization Boxed (IMCB) algorithm effectively improves the accuracy and efficiency of localization.

Key words

Wireless sensor networks, Localization, Monte Carlo Boxed, RSSI, Motion prediction

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS200204020Z

Publication information

Volume 17, Issue 3 (October 2020)
Year of Publication: 2020
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Zhou, C., Tian, H., Zhong, B.: An Improved MCB Localization Algorithm Based on Weighted RSSI and Motion Prediction. Computer Science and Information Systems, Vol. 17, No. 3, 779–794. (2020), https://doi.org/10.2298/CSIS200204020Z