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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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Proceedings ArticleDOI

Unsupervised indoor localization based on Smartphone Sensors, iBeacon and Wi-Fi

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Experience: pushing indoor localization from laboratory to the wild

TL;DR: In this article , the authors share their 5-year experience on the design, development and evaluation of a large-scale WiFi indoor localization system and address practical challenges encountered to bridge the gap between indoor localization research in the laboratory and system deployment in the wild.
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TL;DR: A new deep learning inspired model to predict locational distance/similarity between two points based on their RSSI measurement is presented, which combines the best features from all three reference models and generates the best locationaldistance estimation.
Book ChapterDOI

DE-auth of the Blue! Transparent De-authentication Using Bluetooth Low Energy Beacon

TL;DR: The so-called “lunchtime attack”, whereby a nearby attacker gains access to the casually departed user’s active log-in session, is a serious security risk that stems from lack of proper de-authentication.
Book ChapterDOI

Adapted WLAN Fingerprint Indoor Positioning System (IPS) Based on User Orientations

TL;DR: A new method is proposed to overcome the effect of user orientation on the accuracy of IPS by introducing an adaptation of the signal strength values based on user orientation and decreasing the required RM database memory with more additions of simple computations.
References
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Journal ArticleDOI

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

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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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