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Wireless Sensor Network Localization via Matrix Completion Based on Bregman Divergence.

Chunsheng Liu1, Hong Shan2, Bin Wang3

  • 1Electronic Engineering Institute, National University of Defense Technology, Hefei 230037, China. liuchunsheng17a@nudt.edu.cn.

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|September 12, 2018
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Summary
This summary is machine-generated.

This study introduces a new algorithm for wireless sensor network (WSN) localization that significantly improves positioning accuracy. The LBDMC algorithm effectively handles noise, achieving up to ten times lower localization error than existing methods.

Keywords:
Bregman divergencelocalizationmatrix completionpulse noise

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Wireless Sensor Network (WSN) localization faces challenges with positioning accuracy due to ubiquitous pulse noise.
  • Existing localization algorithms struggle with noise, leading to reduced accuracy in practical WSN deployments.

Purpose of the Study:

  • To propose a novel Wireless Sensor Network localization algorithm named LBDMC (Localization by Bregman divergence and Matrix Completion).
  • To address the limitations of current algorithms in handling pulse noise for improved WSN node positioning.

Main Methods:

  • Formulating WSN localization as a matrix completion problem based on the low-rank property of the Euclidean Distance Matrix (EDM).
  • Developing a regularized matrix completion model using L1,2-norm to smooth pulse noise.
  • Defining a multivariate function Bregman divergence to solve the matrix completion model and obtain an EDM estimator.
  • Employing Multi-Dimensional Scaling (MDS) for node localization using the recovered EDM.

Main Results:

  • The proposed LBDMC algorithm demonstrates superior positioning accuracy and robustness compared to existing methods under various noise conditions.
  • LBDMC achieves a mean localization error approximately ten times smaller than other algorithms at sampling rates above 30%.
  • The algorithm maintains high efficiency while enhancing localization performance.

Conclusions:

  • LBDMC offers a significant advancement in WSN localization by effectively mitigating pulse noise.
  • The algorithm provides a robust and highly accurate solution for WSN node positioning, outperforming current state-of-the-art methods.
  • The use of Bregman divergence and matrix completion presents a promising approach for noisy WSN localization problems.