  The unprecedented worldwide spread of coronavirus disease has significantly
sped up the development of technology-based solutions to prevent, combat,
monitor, or predict pandemics and/or its evolution. The omnipresence of smart
Internet-of-things (IoT) devices can play a predominant role in designing
advanced techniques helping in minimizing the risk of contamination. In this
paper, we propose a practical framework that uses the Social IoT (SIoT) concept
to improve pedestrians safely navigate through a real-wold map of a smart city.
The objective is to mitigate the risks of exposure to the virus in high-dense
areas where social distancing might not be well-practiced. The proposed routing
approach recommends pedestrians' route in a real-time manner while considering
other devices' mobility. First, the IoT devices are clustered into communities
according to two SIoT relations that consider the devices' locations and the
friendship levels among their owners. Accordingly, the city map roads are
assigned weights representing their safety levels. Afterward, a navigation
algorithm, namely the Dijkstra algorithm, is applied to recommend the safest
route to follow. Simulation results applied on a real-world IoT data set have
shown the ability of the proposed approach in achieving trade-offs between both
safest and shortest paths according to the pedestrian preference.
