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Related Experiment Videos

A Robust Crowdsourcing-Based Indoor Localization System.

Baoding Zhou1,2, Qingquan Li3,4, Qingzhou Mao5

  • 1Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China. bdzhou@szu.edu.cn.

Sensors (Basel, Switzerland)
|April 20, 2017
PubMed
Summary

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This summary is machine-generated.

This study introduces a robust crowdsourcing-based indoor localization system (RCILS) that automatically builds WiFi radio maps. RCILS overcomes Received Signal Strength (RSS) variance for more accurate smartphone-based indoor positioning.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Ubiquitous Computing

Background:

  • WiFi fingerprinting is a popular indoor localization method.
  • Radio map construction is labor-intensive and time-consuming.
  • Received Signal Strength (RSS) variance degrades localization accuracy.

Purpose of the Study:

  • To propose a robust crowdsourcing-based indoor localization system (RCILS).
  • To automatically construct radio maps using smartphone crowdsourcing data.
  • To address the RSS variance problem in WiFi fingerprinting.

Main Methods:

  • RCILS abstracts indoor maps as semantic graphs.
  • Activity detection and pedestrian dead-reckoning extract activity sequences.
  • A trajectory fingerprint model is used for Received Signal Strength (RSS) variance mitigation.
Keywords:
crowdsourcingindoor localizationradio mapsmartphone

Related Experiment Videos

Main Results:

  • RCILS automatically constructs radio maps from crowdsourced data.
  • The system demonstrates efficiency and robustness in an office building.
  • Localization accuracy is improved by mitigating RSS variance.

Conclusions:

  • RCILS offers an efficient and robust solution for WiFi fingerprinting-based indoor localization.
  • Crowdsourcing significantly reduces the effort in radio map construction.
  • The trajectory fingerprint model effectively handles RSS variance for improved accuracy.