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Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication

Nano-scaled structures, wireless sensing, wearable devices, and wireless communications systems are anticipated to support the development of new next-generation technologies in the near future. Exponential rise in future Radio-Frequency (RF) sensing systems have demonstrated its applications in are...

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Autores principales: Aziz Shah, Syed, Ahmad, Jawad, Tahir, Ahsen, Ahmed, Fawad, Russell, Gordon, Shah, Syed Yaseen, Buchanan, William J., Abbasi, Qammer H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7230537/
https://www.ncbi.nlm.nih.gov/pubmed/32260149
http://dx.doi.org/10.3390/mi11040379
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author Aziz Shah, Syed
Ahmad, Jawad
Tahir, Ahsen
Ahmed, Fawad
Russell, Gordon
Shah, Syed Yaseen
Buchanan, William J.
Abbasi, Qammer H.
author_facet Aziz Shah, Syed
Ahmad, Jawad
Tahir, Ahsen
Ahmed, Fawad
Russell, Gordon
Shah, Syed Yaseen
Buchanan, William J.
Abbasi, Qammer H.
author_sort Aziz Shah, Syed
collection PubMed
description Nano-scaled structures, wireless sensing, wearable devices, and wireless communications systems are anticipated to support the development of new next-generation technologies in the near future. Exponential rise in future Radio-Frequency (RF) sensing systems have demonstrated its applications in areas such as wearable consumer electronics, remote healthcare monitoring, wireless implants, and smart buildings. In this paper, we propose a novel, non-wearable, device-free, privacy-preserving Wi-Fi imaging-based occupancy detection system for future smart buildings. The proposed system is developed using off-the-shelf non-wearable devices such as Wi-Fi router, network interface card, and an omnidirectional antenna for future body centric communication. The core idea is to detect presence of person along its activities of daily living without deploying a device on person’s body. The Wi-Fi signals received using non-wearable devices are converted into time–frequency scalograms. The occupancy is detected by classifying the scalogram images using an auto-encoder neural network. In addition to occupancy detection, the deep neural network also identifies the activity performed by the occupant. Moreover, a novel encryption algorithm using Chirikov and Intertwining map-based is also proposed to encrypt the scalogram images. This feature enables secure storage of scalogram images in a database for future analysis. The classification accuracy of the proposed scheme is 91.1%.
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spelling pubmed-72305372020-05-22 Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication Aziz Shah, Syed Ahmad, Jawad Tahir, Ahsen Ahmed, Fawad Russell, Gordon Shah, Syed Yaseen Buchanan, William J. Abbasi, Qammer H. Micromachines (Basel) Article Nano-scaled structures, wireless sensing, wearable devices, and wireless communications systems are anticipated to support the development of new next-generation technologies in the near future. Exponential rise in future Radio-Frequency (RF) sensing systems have demonstrated its applications in areas such as wearable consumer electronics, remote healthcare monitoring, wireless implants, and smart buildings. In this paper, we propose a novel, non-wearable, device-free, privacy-preserving Wi-Fi imaging-based occupancy detection system for future smart buildings. The proposed system is developed using off-the-shelf non-wearable devices such as Wi-Fi router, network interface card, and an omnidirectional antenna for future body centric communication. The core idea is to detect presence of person along its activities of daily living without deploying a device on person’s body. The Wi-Fi signals received using non-wearable devices are converted into time–frequency scalograms. The occupancy is detected by classifying the scalogram images using an auto-encoder neural network. In addition to occupancy detection, the deep neural network also identifies the activity performed by the occupant. Moreover, a novel encryption algorithm using Chirikov and Intertwining map-based is also proposed to encrypt the scalogram images. This feature enables secure storage of scalogram images in a database for future analysis. The classification accuracy of the proposed scheme is 91.1%. MDPI 2020-04-03 /pmc/articles/PMC7230537/ /pubmed/32260149 http://dx.doi.org/10.3390/mi11040379 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Aziz Shah, Syed
Ahmad, Jawad
Tahir, Ahsen
Ahmed, Fawad
Russell, Gordon
Shah, Syed Yaseen
Buchanan, William J.
Abbasi, Qammer H.
Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication
title Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication
title_full Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication
title_fullStr Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication
title_full_unstemmed Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication
title_short Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication
title_sort privacy-preserving non-wearable occupancy monitoring system exploiting wi-fi imaging for next-generation body centric communication
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7230537/
https://www.ncbi.nlm.nih.gov/pubmed/32260149
http://dx.doi.org/10.3390/mi11040379
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