Indoor Localization Using Multi-Bluetooth Beacon Deployment in a Sparse Edge Computing Environment

Area of Science:

• Engineering
• Computer Science
• Signal Processing

Background:

• Bluetooth low energy (BLE) offers cost-effective and low-power indoor localization solutions.
• Received Signal Strength Indicator (RSSI) variability due to environmental factors challenges BLE localization accuracy.
• Radio frequency (RF)-dense modern buildings present complex environments for indoor positioning.

Purpose of the Study:

• To present an optimized method for enhancing indoor localization accuracy using multiple BLE beacons.
• To evaluate the impact of beacon quantity on localization performance in RF-dense settings.
• To develop and release an open-source BLE localization framework for wider adoption.

Main Methods:

• Utilized multiple BLE beacons for indoor localization in a modern building environment.
• Implemented a proof-of-concept study to assess localization error reduction.
• Deployed the localization framework on an edge network of Raspberry Pi devices.

Main Results:

• Localization error was significantly reduced from a worst-case distance of 9.09 m to 2.94 m by using three BLE beacons.
• Employing more than three BLE beacons yielded minimal incremental improvements in localization accuracy.
• The developed BLE localization framework was successfully implemented on an edge computing network.

Conclusions:

• Three BLE beacons are optimal for enhancing indoor localization accuracy in RF-dense environments.
• The open-source release of the BLE localization framework facilitates broader research and application.
• This optimized approach addresses RSSI variability challenges for more reliable indoor positioning.