Improving BLE Distance Estimation and Classification Using TX Power and Machine Learning-Reference-Cited by-同舟云学术

Improving BLE Distance Estimation and Classification Using TX Power and Machine Learning

Published:2017-11-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems
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Author:

Al Qathrady Mimonah¹,Helmy Ahmed¹

Affiliation:

1. University of Florida, Gainesville, FL, USA

Funder

NSF

Najran University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3127540.3127577

Reference19 articles.

1. Reliability of Bluetooth Smart Technology for Indoor Localization System

2. M. S. Aman H. Jiang C. Quint K. Yelamarthi and A. Abdelgawad. 2016. Reliability evaluation of iBeacon for micro-localization. In (UEMCON). IEEE 1--5. 10.1109/UEMCON.2016.7777904 M. S. Aman H. Jiang C. Quint K. Yelamarthi and A. Abdelgawad. 2016. Reliability evaluation of iBeacon for micro-localization. In (UEMCON). IEEE 1--5. 10.1109/UEMCON.2016.7777904

3. Indoor distance estimated from Bluetooth Low Energy signal strength: Comparison of regression models

4. Accurate Distance Estimation between Things: A Self-correcting Approach;Cho H.;Open Journal of Internet Of Things (OJIOT),2015

5. Bluetooth Low Energy. https://www.bluetooth.com/what-is-bluetoothtechnology/ how-it-works/low-energy. Bluetooth Low Energy. https://www.bluetooth.com/what-is-bluetoothtechnology/ how-it-works/low-energy.

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