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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
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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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Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons.

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TL;DR: A technological perspective of indoor positioning systems, comprising a wide range of technologies and approaches is provided, and the existing approaches are classified in a structure in order to guide the review and discussion of the different approaches.
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References
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Proceedings ArticleDOI

A comparative survey of WLAN location fingerprinting methods

TL;DR: A unified mathematical formulation of radio map database and location estimation is presented, point out the equivalence of some methods from the literature, and present some new variants.
Journal ArticleDOI

Implementing a sentient computing system

TL;DR: An enhanced version of AT&T Laboratories Cambridge's sentient computing system, which uses sensors to update a model of the real world, is installed throughout an office building.
Proceedings Article

WiFi-SLAM using Gaussian process latent variable models

TL;DR: This paper proposes a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data and shows how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence.
Proceedings Article

WiFi-SLAM Using G aussian Process Latent Variable Models

TL;DR: In this paper, the Gaussian Process Latent Variable Model (GPLVM) is used to reconstruct a topological connectivity graph from a signal strength sequence, which can be used to perform efficient WiFi SLAM.

An Analysis of the Accuracy of Bluetooth Low Energy for Indoor Positioning Applications

TL;DR: It is determined that the optimal positioning performance is provided by 10Hz beaconing and a 1 second multipath mitigation processing window size, and above this there is no clear benefit to extra beacon coverage.
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