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Related Experiment Videos

Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization.

Loizos Kanaris1, Akis Kokkinis2, Antonio Liotta3

  • 1Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands. l.kanaris@tue.nl.

Sensors (Basel, Switzerland)
|April 11, 2017
PubMed
Summary

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This summary is machine-generated.

This study introduces i-KNN, a hybrid indoor localization method using Bluetooth Low Energy (BLE) and Wi-Fi. It enhances accuracy and speed by filtering Received Signal Strength (RSS) data for better positioning.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Ubiquitous Computing

Background:

  • Indoor localization is crucial for Internet of Things (IoT), Body Sensor Networks (BSN), and Ambient Assisted Living (AAL).
  • Existing Wi-Fi Received Signal Strength (RSS) based methods like K-Nearest Neighbour (KNN), Maximum A Posteriori (MAP), and Minimum Mean Square Error (MMSE) have limitations.
  • IEEE 802.11 infrastructure is widely available, making Wi-Fi a common basis for localization platforms.

Purpose of the Study:

  • To improve the accuracy and efficiency of indoor localization platforms.
  • To introduce a hybrid method combining Bluetooth Low Energy (BLE) and Wi-Fi (IEEE 802.11).
  • To develop a novel positioning algorithm, i-KNN, that refines the fingerprint dataset.

Main Methods:

Keywords:
Body Sensor Networks (BSN)Internet of Things (IoT)bluetooth low energy (BLE)fingerprintindoor localizationindoor positioningpositioning algorithms

Related Experiment Videos

  • A hybrid approach integrating BLE and Wi-Fi RSS data.
  • Development of the i-KNN algorithm, a modification of KNN.
  • Filtering of the RSS fingerprint dataset (radiomap) based on BLE device proximity.
  • Estimation of user position using an optimized subset of the fingerprint data.

    • Main Results:

    • The i-KNN algorithm filters the initial fingerprint dataset effectively.
    • Achieves faster positioning estimation by using a reduced dataset.
    • Enhances positioning accuracy by minimizing calculation errors.
    • Demonstrates the benefits of combining BLE and Wi-Fi for indoor localization.

    Conclusions:

    • The proposed i-KNN method offers a significant improvement in indoor localization accuracy and speed.
    • Hybrid BLE and Wi-Fi approaches are promising for future IoT and AAL applications.
    • The filtering mechanism of i-KNN optimizes the localization process, reducing computational load and errors.