Abstract
The Kingdom of Saudi Arabia has suffered from COVID-19 disease as part of the global pandemic due to severe acute respiratory syndrome coronavirus 2. The economy of Saudi Arabia also suffered a heavy impact. Several measures were taken to help mitigate its impact and stimulate the economy. In this context, we present a safe and secure WiFi-sensing-based COVID-19 monitoring system exploiting commercially available low-cost wireless devices that can be deployed in different indoor settings within Saudi Arabia. We extracted different activities of daily living and respiratory rates from ubiquitous WiFi signals in terms of channel state information (CSI) and secured them from unauthorized access through permutation and diffusion with multiple substitution boxes using chaos theory. The experiments were performed on healthy participants. We used the variances of the amplitude information of the CSI data and evaluated their security using several security parameters such as the correlation coefficient, mean-squared error (MSE), peak-signal-to-noise ratio (PSNR), entropy, number of pixel change rate (NPCR), and unified average change intensity (UACI). These security metrics, for example, lower correlation and higher entropy, indicate stronger security of the proposed encryption method. Moreover, the NPCR and UACI values were higher than 99% and 30, respectively, which also confirmed the security strength of the encrypted information.
Highlights
College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia; RIADI Laboratory, National School of Computer Science, University of Manouba, Manouba 2010, Tunisia
This system does not need a dedicated device and exploits radio frequency signals available almost everywhere; This system can efficiently monitor large-scale body movements along with tiny chest movements. It primarily exploits various parameters extracted from WiFi signals, such as the variances of the amplitude and phase information, time–frequency spectrograms, and 3D signatures containing time–frequency–amplitude information; WiFi technology is highly susceptible to being accessed by unwanted users; we propose a novel privacy-preserving algorithm for securing the channel state information (CSI) data containing vital signatures such as ADLs and respiratory rates to counter the false alarms generated
The data were previously collected for a range of applications; due to the ongoing pandemic, we argue that this system is applicable for monitoring COVID-19 patients since no device needs to interact with the sensing system
Summary
The COVID-19 pandemic has spread significantly in the past year and has infected more than 118 million people globally since its inception. We present a noncontact safe and secure WiFi sensing-based COVID19 patient monitoring technique using low-cost ubiquitous small wireless devices such as commercially available WiFi routers, network interface cards, and traditional dipole antennas. This system does not need a dedicated device and exploits radio frequency signals available almost everywhere; This system can efficiently monitor large-scale body movements along with tiny chest movements It primarily exploits various parameters extracted from WiFi signals, such as the variances of the amplitude and phase information, time–frequency spectrograms, and 3D signatures containing time–frequency–amplitude information; WiFi technology is highly susceptible to being accessed by unwanted users; we propose a novel privacy-preserving algorithm for securing the CSI data containing vital signatures such as ADLs and respiratory rates to counter the false alarms generated. That is one of major reasons why many cryptographic designers prefer chaos instead of complex formulae with complex properties [23]
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