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Related Experiment Video

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XGBLoc: XGBoost-Based Indoor Localization in Multi-Building Multi-Floor Environments.

Navneet Singh1, Sangho Choe1, Rajiv Punmiya1

  • 1Department of Information, Communications, and Electronics Engineering, The Catholic University of Korea, Bucheon-si 14662, Korea.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary

This study introduces XGBLoc, a machine learning approach for accurate indoor localization using WiFi signals. It outperforms existing methods by using structured labels for better performance in complex environments.

Keywords:
RSSI fingerprintsWiFiXGBoostclassificationhyper-parameter tuningindoor localizationlabelingregression

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Area of Science:

  • * Computer Science, Electrical Engineering, and Geosciences.
  • * Focuses on indoor positioning systems and wireless sensor networks.

Background:

  • * High-quality indoor location-based services necessitate reliable and accurate device positioning.
  • * WiFi Received Signal Strength Indicator (RSSI) is widely available but traditional fingerprinting methods struggle with dynamic indoor channel conditions (e.g., multipath fading, non-line-of-sight).
  • * Machine learning (ML) and deep learning (DL) offer alternatives to overcome these limitations.

Purpose of the Study:

  • * To present an extreme gradient boosting-based ML indoor localization scheme, named XGBLoc.
  • * To accurately classify mobile device positions in complex, multi-floor, multi-building indoor environments.
  • * To demonstrate XGBLoc's effectiveness in reducing RSSI dataset dimensionality and utilizing structured synthetic (relational) labels.

Main Methods:

  • * Development of XGBLoc, an extreme gradient boosting (XGBoost) machine learning model.
  • * Implementation of a novel training approach using structured synthetic (relational) labels instead of conventional independent labels.
  • * Dimensionality reduction of RSSI datasets for improved efficiency.
  • * Numerical evaluation on publicly available indoor localization datasets.

Main Results:

  • * XGBLoc achieves accurate classification and regression performance for indoor localization.
  • * The scheme effectively handles complex and hierarchical indoor environments.
  • * Demonstrated superiority over existing ML and DL-based indoor localization schemes.
  • * Significant reduction in RSSI dataset dimensionality was achieved.

Conclusions:

  • * XGBLoc offers a superior ML-based solution for indoor localization compared to existing methods.
  • * The use of structured synthetic labels is key to improving performance in challenging indoor environments.
  • * XGBLoc provides a reliable, accurate, and potentially low-cost approach for location-based services.