Research Output

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 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%.

  • Type:

    Article

  • Date:

    03 April 2020

  • Publication Status:

    Published

  • DOI:

    10.3390/mi11040379

  • Cross Ref:

    mi11040379

  • Funders:

    Edinburgh Napier Funded

Citation

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

Authors

Keywords

Wi-Fi; Privacy; Occupancy; Deep Learning; Encryption

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