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Combining RSSI and Accelerometer Features for Room-Level Localization.

Athina Tsanousa1, Vasileios-Rafail Xefteris1, Georgios Meditskos1

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Summary
This summary is machine-generated.

This study explored using accelerometer data with Received Signal Strength Indication (RSSI) for indoor localization. Results show RSSI features perform better than accelerometer data for room-level identification.

Keywords:
RSSIfusioninertial sensorsroom-level localization

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Ubiquitous Computing

Background:

  • Internet-of-Things (IoT) systems enable remote solutions in various domains, including healthcare and security.
  • Indoor localization using Received Signal Strength Indication (RSSI) is a common IoT service, often treated as a classification problem.
  • Accurate indoor positioning is crucial for applications like locating individuals in crowded environments.

Purpose of the Study:

  • To evaluate the added value of integrating accelerometer data with RSSI for room-level localization.
  • To assess the performance of ensemble learning methods for indoor positioning.
  • To investigate the effectiveness of feature extraction from RSSI values.

Main Methods:

  • A preliminary approach combining early and late fusion of RSSI and accelerometer features was implemented.
  • Ensemble learning algorithms were employed for room-level classification.
  • Performance was evaluated using different protocols on a public dataset.

Main Results:

  • Extracted features from RSSI demonstrated superior performance in room-level localization.
  • The accelerometer's individual contribution to localization accuracy was found to be poor.
  • The poor performance of the accelerometer negatively impacted the fusion results.

Conclusions:

  • RSSI-based features are more effective for room-level localization compared to accelerometer data.
  • Integrating accelerometer data with RSSI did not significantly improve localization accuracy in this study.
  • Further research may be needed to optimize the fusion of sensor data for enhanced indoor positioning.