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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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Real-time and Precise Indoor Localization System in Multi-Floor Buildings for Pedestrian using Cloud Platform

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Hybrid Learning for Mobile Ad-hoc Distancing/Positioning Using Bluetooth Low Energy

TL;DR: In this paper , a hybrid learning method was proposed to support mobile ad-hoc distance estimation using BLE-enabled smartphones, which combines unsupervised learning, supervised learning and genetic algorithms for enhancing distance estimation accuracy.

Localization at room resolution with Bluegiga bluetooth low energy

TL;DR: In this article, the location of a test object or person can be determined at room resolution using Bluetooth Low Energy (BLE), where the test objects or persons are equipped with a BLE node and approximately central in each room a low-cost and low-power BLE sensor is placed.
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A Framework for Collaborative All-Source Navigation With Fault Detection and Exclusion

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References
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Proceedings ArticleDOI

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