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Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network.

Zhenghuan Wang1, Heng Liu2, Shengxin Xu3

  • 1School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China. wangzhenghuan@bit.edu.cn.

Sensors (Basel, Switzerland)
|April 28, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a binary work mode for device-free localization, significantly reducing energy consumption by transmitting link states instead of raw RSS data. This approach maintains comparable localization accuracy while improving energy efficiency.

Keywords:
RSSdevice-free localization (DFL)energy efficiencymaximum likelihoodparticle filter

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Area of Science:

  • Wireless Sensor Networks
  • Localization and Tracking Technologies
  • Energy-Efficient Systems

Background:

  • Received-signal-strength-based (RSS-based) device-free localization (DFL) enables tracking without electronic tags.
  • Traditional DFL methods are energy-intensive due to high data transmission from RF sensors.
  • Power constraints on RF sensors limit the scalability of traditional DFL.

Purpose of the Study:

  • To propose an energy-efficient binary work mode for RSS-based DFL.
  • To develop novel localization methods tailored for the binary work mode.
  • To evaluate the performance and energy efficiency of the proposed approach.

Main Methods:

  • Implemented a binary work mode where RF sensors transmit link states, not raw RSS measurements.
  • Developed a grid-based maximum likelihood (GML) method for stationary target localization.
  • Utilized a particle filter (PF) for tracking moving targets in the binary work mode.

Main Results:

  • The binary work mode substantially reduces data delivery compared to traditional methods.
  • Localization and tracking performance in binary mode is comparable to traditional mode.
  • Significant improvements in energy efficiency were observed with the binary work mode.

Conclusions:

  • The proposed binary work mode offers a viable and energy-efficient alternative for RSS-based DFL.
  • GML and PF methods provide effective localization and tracking capabilities for the new mode.
  • This approach enhances the practicality of DFL for power-constrained sensor networks.