Journal ArticleDOI
Location Fingerprinting With Bluetooth Low Energy Beacons
Ramsey Faragher,Robert Harle +1 more
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.read more
Citations
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Proceedings ArticleDOI
A deep learning and RSSI based approach for indoor positioning
TL;DR: A method that combines Deep Learning and BLE (Bluetooth Low Energy) Fingerprinting method to get better accurate results is explained.
Dissertation
Context-aware dynamic designs for energy efficient mobile sensing -
TL;DR: This dissertation presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of computer programming called “hierarchical programming”.
Proceedings ArticleDOI
An Improved Pedestrian Motion Tracking System Assisted by Machine Learning
Yuming Chen,Wei Li,Zhuoyin Si +2 more
TL;DR: A novel Zero Velocity Update (ZUPT) method based on the strong tracking KF (STKF) is presented and results show that the proposed method has better positioning accuracy and robustness than traditional indoor positioning method.
Journal ArticleDOI
Detection of Security Attack in IoT using Received Signal Strength Indicator
TL;DR: RSSI values are used to find the location of sensor nodes in given various topologies using Contiki OS and its inbuilt Cooja simulator to achieve small-size and cost-effective solution.
Proceedings ArticleDOI
ReliaBLE: Towards Reliable Communication via Bluetooth Low Energy Advertisement Networks
TL;DR: This paper designs and deploys advertisement networks that incorporate analytical models, demonstrating that they can predict average network performance, that advertisements can be used as the base for a reliable network, and that BLE advertisement networks may prove to be a valuable tool for the sensor network community.
References
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Proceedings ArticleDOI
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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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