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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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Journal ArticleDOI

A study on the cabin indoor localization algorithm based on adaptive K-values

TL;DR: Wang et al. as discussed by the authors proposed an adaptive K-value-based cabin indoor positioning algorithm, which constructs a fingerprint library by collecting Received Signal Strength Indication data and carries out filtering; matches fingerprint information by adaptive Kvalues during the localization process, while using double K-nearest neighbor to reduce the influence of outlier points on the final localization.
Proceedings ArticleDOI

Uma Arquitetura Distribuída IoT para Gestão de Presença e Encontros de Pessoas em Ambientes Internos

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Book ChapterDOI

On Physical Web for Social Networks

TL;DR: The article discusses the use of Physical Web approaches for the expansion of social networks, which implies the presentation of data from social networks in a real (physical) context, as well as the inverse task of using information about a real physical context in querying and analyzing data fromsocial networks.
References
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Journal ArticleDOI

Survey of Wireless Indoor Positioning Techniques and Systems

TL;DR: Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.
Proceedings ArticleDOI

The Horus WLAN location determination system

TL;DR: The Horus system identifies different causes for the wireless channel variations and addresses them and uses location-clustering techniques to reduce the computational requirements of the algorithm and the lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.
Journal ArticleDOI

A Survey of Indoor Inertial Positioning Systems for Pedestrians

TL;DR: It is concluded that PDR techniques alone can offer good short- to medium- term tracking under certain circumstances, but that regular absolute position fixes from partner systems will be needed to ensure long-term operation and to cope with unexpected behaviours.

Enhancements to the RADAR User Location and Tracking System

TL;DR: This paper analyzes shortcomings of the basic system, develops and evaluates solutions to address these shortcomings, and describes several new enhancements, including a novel access point-based environmental profiling scheme, and a Viterbi-like algorithm for continuous user tracking and disambiguation of candidate user locations.

Network Time Protocol Version 4: Protocol and Algorithms Specification

TL;DR: NTP version 4 (NTPv4), which is backwards compatible with NTP version 3 (N TPv3), described in RFC 1305, as well as previous versions of the protocol, are described.
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