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; Abbasi, Qammer H.

Abstract

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%.

Citation

Aziz Shah, S., Ahmad, J., Tahir, A., Ahmed, F., Russell, G., Shah, S. Y., Buchanan, W., & Abbasi, Q. H. (2020). Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication. Micromachines, 11(4), Article 379. https://doi.org/10.3390/mi11040379

• Journal Article Type: Article
• Acceptance Date: Mar 23, 2020
• Online Publication Date: Apr 3, 2020
• Publication Date: Apr 3, 2020
• Deposit Date: Apr 5, 2020
• Publicly Available Date: Apr 6, 2020
• Journal: Micromachines
• Electronic ISSN: 2072-666X
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 4
• Article Number: 379
• DOI: https://doi.org/10.3390/mi11040379
• Keywords: Wi-Fi; Privacy; Occupancy; Deep Learning; Encryption
• Public URL: http://researchrepository.napier.ac.uk/Output/2651031
• Publisher URL: https://www.mdpi.com/2072-666X/11/4/379