Dynamic User-Centric Clustered Workplaces for COVID-19 Control Measures Based on Geofencing and Deep Learning

Abstract

Control measures have been applied in recent years due to the COVID-19 pandemic. Different technologies including artificial intelligence (AI) and geofencing are required to be exploited for developing efficient techniques to deal with this crisis. Workplaces are the most dangerous areas that can lead to the infection of the pandemic. This is due to the increased density of people and transactions in limited places. In this paper, an efficient approach is proposed to monitor and impose COVID-19 control measures in workplaces. The workplace environment is clustered based on a dynamic user-centric clustering scheme, where each person in the workplace is assigned to a set of associated geofences that form its cluster. For each geofence, different wireless and network metrics are used for generating its digital signature. An efficient technique based on deep learning is proposed to generate the geofence digital signature and detect whether the person is inside his associated cluster or not. Experimental results show the effectiveness of the proposed technique for different locations in a real workplace. Specifically, an accuracy of 92.86% is achieved in a workplace environment by the proposed approach.