An Edge-Based Social Distancing Detection Service to Mitigate COVID-19 Propagation

Abstract

COVID-19 virus has strongly impacted our everyday life. Without the availability of a vaccine or a well-established and efficient treatment, we have to live with it. One way to mitigate the propagation of the virus is to respect social distancing between persons. Indeed, many governments have adopted it as one of the key solutions to reduce the propagation of the virus. However, it is difficult to enforce social distancing among the population. In this article, we propose to combine IoT and multi-access edge computing (MEC) technologies to build a service that checks and warns people in near real time if they are not practicing social distancing. The proposed service is composed of a client application side installed on the users' smartphone, which periodically sends GPS coordinates to remote servers sitting at the edge of the network (i.e., at MEC). The remote servers use a local algorithm to detect and warn users who are not practicing social distancing. The proposed service respects privacy and anonymity by hiding the user identity, and is capable of warning users in near real time thanks to the usage of MEC.