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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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/11/4/379 |
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| author | Syed Aziz Shah Jawad Ahmad Ahsen Tahir Fawad Ahmed Gordon Russell Syed Yaseen Shah William J. Buchanan Qammer H. Abbasi |
| author_facet | Syed Aziz Shah Jawad Ahmad Ahsen Tahir Fawad Ahmed Gordon Russell Syed Yaseen Shah William J. Buchanan Qammer H. Abbasi |
| author_sort | Syed Aziz Shah |
| collection | DOAJ |
| 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%. |
| first_indexed | 2024-03-10T20:42:23Z |
| format | Article |
| id | doaj.art-d33c0babc794485ab3a344bf4e3fad27 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-10T20:42:23Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-d33c0babc794485ab3a344bf4e3fad272023-11-19T20:37:13ZengMDPI AGMicromachines2072-666X2020-04-0111437910.3390/mi11040379Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric CommunicationSyed Aziz Shah0Jawad Ahmad1Ahsen Tahir2Fawad Ahmed3Gordon Russell4Syed Yaseen Shah5William J. Buchanan6Qammer H. Abbasi7School of Computing and Mathematics, Manchester Metropolitan University, Manchester M13 9PL, UKSchool of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UKDepartment of Electrical Engineering, University of Engineering and Technology, Lahore, Punjab 54890, PakistanDepartment of Electrical Engineering, HITEC University Taxila, Punjab 47080, PakistanSchool of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UKSchool of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UKSchool of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UKSchool of Engineering, University of Glasgow, Glasgow G12 8QQ, UKNano-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%.https://www.mdpi.com/2072-666X/11/4/379Wi-Fiprivacyoccupancydeep learningencryption |
| spellingShingle | Syed Aziz Shah Jawad Ahmad Ahsen Tahir Fawad Ahmed Gordon Russell Syed Yaseen Shah William J. Buchanan Qammer H. Abbasi Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication Micromachines Wi-Fi privacy occupancy deep learning encryption |
| 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 | Wi-Fi privacy occupancy deep learning encryption |
| url | https://www.mdpi.com/2072-666X/11/4/379 |
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