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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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%. |
format | Online Article Text |
id | pubmed-7230537 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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