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Cost-Efficient RSSI-Based Indoor Proximity Positioning, for Large/Complex Museum Exhibition Spaces.

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This study tested Bluetooth Low Energy (BLE) and Received Signal Strength Indication (RSSI) for indoor localization in a museum. Simple methods achieved 81.53% accuracy, while machine learning improved it to 87.24%.

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Area of Science:

  • Indoor localization
  • Wireless sensor networks
  • Machine learning applications

Background:

  • Received Signal Strength Indication (RSSI) offers simple, cost-effective indoor positioning but struggles with accuracy due to non-line-of-sight (NLOS) conditions, noise, and multipath fading.
  • Bluetooth Low Energy (BLE) technology presents a scalable, energy-efficient, and market-available solution for indoor localization, particularly suitable for large, complex environments like museums with high visitor traffic.

Purpose of the Study:

  • To evaluate the accuracy and feasibility of a BLE/RSSI-based indoor localization system in a real-world museum setting.
  • To develop and test a prototype tool (VTT) integrating various data processing and prediction methods for visitor cell-level positioning.
  • To compare the performance of traditional signal processing techniques against machine learning algorithms for improving localization accuracy.

Main Methods:

  • A pilot study was conducted at the Museum of Modern Greek Culture using wearable BLE beacons for visitor tracking across 47 halls.
  • The VTT prototype incorporated Kalman filters for RSSI smoothing, hybrid positioning, temporal/spatial filtering, and machine learning classifiers.
  • Visitor movement was modeled, with the "ant" behavioral model selected for experimentation, and 15 methods/algorithms were evaluated across 20 RSSI datasets.

Main Results:

  • Simple data smoothing and management methods achieved an average prediction accuracy of 81.53% across diverse datasets.
  • Machine learning algorithms, specifically Random Forest, demonstrated superior performance, reaching an average prediction accuracy of 87.24%.
  • The implemented infrastructure achieved a cost-efficiency of 8 Euro per square meter.

Conclusions:

  • BLE/RSSI-based localization is a viable and cost-effective solution for indoor positioning in large public spaces like museums.
  • Machine learning algorithms significantly enhance localization accuracy compared to traditional signal processing methods.
  • The VTT prototype provides a scalable and adaptable architecture for improving indoor localization in complex environments.