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| Title | Passive Ble Sensing For Indoor Pattern Recognition And Tracking |
|---|---|
| ID_Doc | 41415 |
| Authors | Afyouni I.; Musleh M.; Basalamah A.; Tariq Z.B. |
| Year | 2021 |
| Published | Procedia Computer Science, 191 |
| DOI | http://dx.doi.org/10.1016/j.procs.2021.07.028 |
| Abstract | Crowd-centric sensing using smart phones enables a diverse range of applications evolving from large outdoor environments (e.g., smart cities) to small-scale indoor environments (e.g., smart homes, smart buildings). Tracking users' patterns in indoor environments is a valuable and challenging aspect that is not yet fully addressed. Active indoor localization systems are generally energy-inefficient and cannot be applied to crowd monitoring applications. This paper focuses on the development of a passive and energy-efficient indoor tracking and pattern recognition technique on top of a managed Bluetooth Low-Energy (BLE) network. Particularly, our system model is based on a passive monitoring of a network of BLE tags, which continuously broadcast their unique identifiers, and the current timestamp. Multiple protocols were implemented to extract moving objects' locations in indoors. The trajectory building process consists of different phases: 1) data sampling, 2) outlier detection and removal, 3) location estimation with a weighted centroid approach, 4) spatio-temporal map matching, and finally 5) trajectory smoothing. A series of experiments was conducted to demonstrate the efficiency and accuracy of the proposed approach, with respect to active triggering approaches and BLE-based localization systems. © 2021 Elsevier B.V.. All rights reserved. |
| Author Keywords | BLE-based positioning; Indoor tracking; pattern recognition; power consumption |
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