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Automatic Detection of Missing Access Points in Indoor Positioning System †.

Rafał Górak1, Marcin Luckner2

  • 1Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75 street, 00-662 Warsaw, Poland. R.Gorak@mini.pw.edu.pl.

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This study introduces a Wi-Fi indoor localization system that automatically detects and updates Access Point (AP) data. This improves accuracy, especially during Wi-Fi infrastructure malfunctions, by preventing significant error increases.

Keywords:
fingerprintingindoor localisation systemsystem deployment and maintenance

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Ubiquitous Computing

Background:

  • Accurate indoor localization is crucial for various applications, but Wi-Fi infrastructure degradation can significantly impact system performance.
  • Existing Wi-Fi fingerprinting localization methods often suffer from reduced accuracy when Access Points (APs) become unavailable or change.
  • Manual recalibration of localization models is time-consuming and impractical for dynamic environments.

Purpose of the Study:

  • To develop an automated system for detecting missing Access Points (APs) in Wi-Fi-based indoor localization.
  • To enhance the robustness and accuracy of Wi-Fi localization systems by enabling automatic model updates.
  • To provide a universal solution applicable to any fingerprinting-based Wi-Fi localization model.

Main Methods:

  • Implementation of a Wi-Fi localization system comprising a localization model and an AP detection module.
  • Utilizing received signal strength (RSS) data from multiple mobile terminals to identify relevant APs and trigger model updates.
  • Employing the Random Forest algorithm for the core localization model, with automatic detection of missing APs.

Main Results:

  • Reduced mean horizontal error by 5.5 meters and floor prediction classification error by 0.26 during Wi-Fi infrastructure malfunctions.
  • Demonstrated the system's capability to accurately detect missing and present APs across various occupancy scenarios and AP failure rates.
  • Validated the universal applicability of the automatic AP detection module for fingerprinting-based localization.

Conclusions:

  • The proposed automated AP detection and model rebuilding mechanism significantly enhances the reliability of Wi-Fi indoor localization.
  • The system effectively mitigates accuracy loss caused by dynamic changes or failures in Wi-Fi infrastructure.
  • This approach offers a practical and scalable solution for maintaining high-performance indoor localization systems.