A Social IoT-driven Pedestrian Routing Approach during Epidemic Time

TL;DR

This paper introduces a Social IoT-based pedestrian routing system during epidemics that dynamically recommends safe routes by analyzing IoT device communities and real-time mobility data to minimize virus exposure.

Contribution

It presents a novel framework integrating Social IoT concepts with real-time routing algorithms to enhance pedestrian safety in smart cities during pandemics.

Findings

Effective in balancing safety and shortest path preferences

Utilizes real-world IoT data for realistic simulation

Clusters IoT devices based on location and social relations

Abstract

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.