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Empirical Study of a Room-Level Localization System Based on Bluetooth Low Energy Beacons.

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This study presents a low-cost indoor localization system using Bluetooth Low Energy (BLE) beacons and a k-nearest neighbors classifier. The system achieves 70-90% accuracy in identifying a person's room in homes and universities.

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Area of Science:

  • Indoor localization
  • Wireless sensor networks
  • Ubiquitous computing

Background:

  • Accurate indoor positioning is crucial for various applications.
  • Existing solutions can be complex or expensive.
  • Bluetooth Low Energy (BLE) offers a potential low-cost alternative.

Purpose of the Study:

  • To develop a simple, low-cost room-level indoor localization system using BLE beacons.
  • To adapt the fingerprinting technique for indoor positioning.
  • To evaluate the system's accuracy and scalability.

Main Methods:

  • Fingerprinting technique using BLE beacons and a BLE scanner.
  • Formulating room estimation as a k-nearest neighbors (kNN) classification problem.
  • Testing with different hardware (laptop, Raspberry Pi) in real-world scenarios (house, university).

Main Results:

  • Achieved 70-90% accuracy in room estimation with a minimal number of BLE beacons.
  • Demonstrated system stability and repeatability through multi-day testing.
  • Empirically measured room estimation success against the number of BLE beacons.

Conclusions:

  • The proposed BLE-based fingerprinting system offers a viable and cost-effective solution for indoor room-level localization.
  • The kNN classifier effectively estimates room location based on BLE beacon signals.
  • The system shows promise for practical applications requiring indoor positioning.