A Social IoT-Driven Pedestrian Routing Approach during Epidemic Time

Abdullah Khanfor, Hamdi Friji, Hakim Ghazzai, Yehia Massoud

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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.
Original languageEnglish (US)
Title of host publication2020 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781728184203
DOIs
StatePublished - Dec 12 2020
Externally publishedYes

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