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Journal ArticleDOI

Location Fingerprinting With Bluetooth Low Energy Beacons

Ramsey Faragher, +1 more
- 06 May 2015 - 
- Vol. 33, Iss: 11, pp 2418-2428
TLDR
This work provides a detailed study of BLE fingerprinting using 19 beacons distributed around a ~600 m2 testbed to position a consumer device, and investigates the choice of key parameters in a BLE positioning system, including beacon density, transmit power, and transmit frequency.
Abstract
The complexity of indoor radio propagation has resulted in location-awareness being derived from empirical fingerprinting techniques, where positioning is performed via a previously-constructed radio map, usually of WiFi signals. The recent introduction of the Bluetooth Low Energy (BLE) radio protocol provides new opportunities for indoor location. It supports portable battery-powered beacons that can be easily distributed at low cost, giving it distinct advantages over WiFi. However, its differing use of the radio band brings new challenges too. In this work, we provide a detailed study of BLE fingerprinting using 19 beacons distributed around a $\sim\! 600\ \mbox{m}^2$ testbed to position a consumer device. We demonstrate the high susceptibility of BLE to fast fading, show how to mitigate this, and quantify the true power cost of continuous BLE scanning. We further investigate the choice of key parameters in a BLE positioning system, including beacon density, transmit power, and transmit frequency. We also provide quantitative comparison with WiFi fingerprinting. Our results show advantages to the use of BLE beacons for positioning. For one-shot (push-to-fix) positioning we achieve $30\ \mbox{m}^2$ ), compared to $100\ \mbox{m}^2$ ) and < 8.5 m for an established WiFi network in the same area.

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Citations
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Indoor 3D visible light positioning system based on adaptive parameter particle swarm optimisation

Abstract: Visible light positioning has become a new research hotspot in recent years. In this study, a new Gaussian model of hybrid noise and multipath reflection is established for noise interference in line-of-sight (LOS) communication and multipath reflection in non-LOS (NLOS) communication. First, the likelihood function of the ranging error is established according to the Gaussian hybrid model, and the analytical expression of Cramer–Rao lower bound is derived. Second, the novel adaptive parameter particle swarm optimisation (AP-PSO) algorithm is proposed to calculate the three-dimensional (3D) coordinates of the target. In order to avoid falling into the local optimal solution, a novel particle adaptive mutation algorithm, namely AP-PSO-M algorithm is also proposed. Simulation results show that if the NLOS propagation probability is 0.3 and the swarm size is 20, compared with Newton–Raphson (NR), particle swarm optimisation (PSO), dissipative PSO (DPSO) and simulated annealing PSO (SA-PSO) algorithms, the average positioning error based on AP-PSO-M algorithm can be reduced by 45.43, 38.98, 25.37 and 26.42%, respectively. If similar positioning accuracy is obtained, compared with PSO, DPSO and SA-PSO algorithms, the average calculation time required by the AP-PSO-M algorithm can be reduced by 48.16, 30.24 and 26.14%, respectively.
Journal ArticleDOI

Performance of Fingerprinting-Based Indoor Positioning with Measured and Simulated RSSI Reference Maps

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From Conception to Retirement: A Lifetime Story of a 3-Year-Old Wireless Beacon System in the Wild

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