A Practical Evaluation of ML Algorithms for a Tag-Based BLE Indoor Positioning System

Abstract

In this paper, we evaluate the performance of machine learning (ML) algorithms employed in a commercial Bluetooth Low Energy (BLE) Indoor Positioning (IP) solution relying on practical measurements in a commercial office space setting.  The BLE IP system utilizing tags presents an ideal economic approach for large facilities with a limited number of tracking elements (gateways).  In this investigation, data collection campaigns were conducted in an indoor facility fitted with BLE gateways to aggregate Received Signal Strength Indicator (RSSI) fingerprints.  Performance of a collection of well-known ML algorithms in terms of accuracy of positioning of the desired objects, in addition to training complexity and online tracking speed were evaluated.  ML algorithms of increased accuracy and efficiency were identified and tabulated in both of the offline and online phases.  It is also envisaged that as part of this practical study, the results will serve to identify proper economical topologies and configuration in real-life installations for tag-based BLE IP systems.