NDLI: Deep learning approach for device-free localisation based on internet of things

Content Provider	IET Digital Library
Author	Yang, Menghuan Wu, Hong
Abstract	The device-free localisation, which neither requires the targets to carry any electronic device nor participate in the position process proactively, is an innovated and prospective technique. Traditional device-free techniques include computer vision, infrared sensor, indoor radar etc. Since there is a rapid development of the internet of things (IoT) brings massive wireless radio links, it is possible to achieve the device-free localisation by investigating the link quality indicator of wireless links. In this Letter, the authors propose a device-free localisation procedure to position the target by acquiring all the wireless link indictors in the IoT network, utilising deep learning to train the deep neural network. The experiment shows the average positioning error can be <0.2 m in an experimental environment of 160 square metres.
Starting Page	575
Ending Page	577
Page Count	3
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 11, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2019.3995
Journal	Electronics Letters
Publisher Date	2020-03-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision Deep Learning Approach Deep Neural Network Device-free Localisation Procedure Electronic Device Emergency Services Internet of Things IoT Network Knowledge Engineering Technique Learning in AI Link Quality Indicator Massive Wireless Radio Link Neural Computing Technique Neural Nets Pervasive Computing Radio Link Radio Link And Equipment Traditional Device-free Technique Ubiquitous
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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