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Mobile device-based Bluetooth Low Energy Database for range estimation in indoor environments.

Pavel Pascacio1,2, Joaquín Torres-Sospedra3, Antonio R Jiménez4

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This study introduces a new Bluetooth Low Energy (BLE) database for indoor positioning. The dataset aids in developing infrastructure-less ranging and collaborative positioning systems, addressing limitations in Non-Line-of-Sight environments.

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Area of Science:

  • Wireless communication
  • Indoor positioning systems
  • Sensor networks

Background:

  • Growing need for accurate indoor positioning and distance estimation in Non-Line-of-Sight (NLOS) environments.
  • Existing infrastructure-less ranging and collaborative positioning methods require extensive, simultaneous mobile device measurements, for which no public databases exist.
  • Tedious data collection hinders the development and validation of these advanced positioning techniques.

Purpose of the Study:

  • To present a comprehensive Bluetooth Low Energy (BLE) database for indoor positioning and ranging applications.
  • To provide a valuable resource for researchers developing and testing infrastructure-less and collaborative positioning systems.
  • To facilitate advancements in indoor localization accuracy, particularly in challenging NLOS conditions.

Main Methods:

  • Development of a novel Bluetooth Low Energy (BLE) database containing Received-Signal-Strength (RSS) and Ground-Truth (GT) position data.
  • Collection of data using mobile devices as both transmitters and receivers.
  • Structuring the database into three distinct subsets for calibration, NLOS ranging/positioning, and real-world office conditions.

Main Results:

  • A comprehensive BLE dataset is established, comprising RSS and GT positions suitable for indoor positioning.
  • The database is validated through baseline analyses including data visualization, filtering, and collaborative distance estimation.
  • The Levenberg-Marquardt Least Squares Trilateration method was applied for path-loss based distance estimation.

Conclusions:

  • The presented BLE database addresses the critical need for accessible data in indoor positioning research.
  • The dataset supports the development and evaluation of infrastructure-less ranging and collaborative positioning, especially in NLOS scenarios.
  • This resource is expected to accelerate innovation in mobile device-based indoor localization technologies.