An edge-based social distancing detection service to mitigate COVID-19 propagation

Ksenitni, Adlen; Brik; Bouziane
IEEE Internet of Things Magazine, Vol.3, N°3, September 2020

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 paper, we propose to combine Internet of Things (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 respecting the 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
that are not respecting the social distancing. The proposed service respects privacy and anonymity, by hiding the user identity, and is capable to warn in near real-time users thanks to the usage of MEC.

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