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) <em>fingerprints</em>. 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 <em>offline</em> and <em>online</em> 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.
Highlights
Satellite navigation systems, such as the global positioning satellite system (GPS) [1], GLONASS [2], Galileo [3], and BeiDou [4], are able to provide positioning coordinates with great accuracy for various outdoor applications
The work presented in this paper focused on performance evaluation of machine learning (ML) classification algorithms in a tag-based Bluetooth Low Energy (BLE) Indoor Positioning (IP) system to identify optimal performers for a real-life practical deployment
Data collection campaigns were conducted at a designated commercial office facility, which was fitted with a number of BLE gateways that allowed the successful aggregation of Received Signal Strength Indicator (RSSI) fingerprints of the BLE-tag
Summary
Satellite navigation systems, such as the global positioning satellite system (GPS) [1], GLONASS [2], Galileo [3], and BeiDou [4], are able to provide positioning coordinates with great accuracy for various outdoor applications. For indoor applications, the generated localization estimates are very crude as a consequence of the diminished satellite signals in an indoor environment. Alternate systems were envisaged and researched to furnish the needed more accurate user locations within the interior of buildings or obstructed environments [5]. By the year 2027, it is anticipated that the global indoor positioning and navigation market will grow to reach $ 50.35 billion [6]. Indoor positioning and navigation systems enable a central iJOE ‒ Vol 16, No 8, 2020
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