Vol.27, No.2, 2021, pp.441-452, doi:10.32604/iasc.2021.015198
Live Data Analytics with IoT Intelligence-Sensing System in Public Transportation for COVID-19 Pandemic
  • Abdullah Alamri1,*, Sultan Alamri2
1 College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
2 College of Computing and Informatics, Saudi Electronic University, Saudi Arabia
* Corresponding Author: Abdullah Alamri. Email:
(This article belongs to this Special Issue: Soft Computing Technologies for COVID 19 Assessment, Analysis and Control)
Received 09 November 2020; Accepted 16 December 2020; Issue published 18 January 2021
The COVID-19 pandemic has presented an unprecedented challenge to the entire world. It is a humanitarian crisis on a global scale. The virus continues to spread throughout nations, putting health systems under enormous pressure in the battle to save lives. With this growing crisis, companies and researchers worldwide are searching for ways to overcome the challenges associated with this virus. Also, the transport sector will play a critical role in revitalizing economies while simultaneously containing the spread of COVID-19. As the virus is still circulating, the only solution is to redesign public transportation to make people feel safe. In this paper, we have proposed a system based on computer vision and IoT sensor infrared technology with live data analytics. The proposed system is capable of gathering, storing, analyzing, processing, and handling the vast amount of data produced by the IoT sensors, and provides the users with real-time information on potential events affecting public transport, thereby enabling users to make well-informed and timely decisions. The evaluation showed that, despite the complexity of the system, it performs well.
IoT; big data; data analytics; cloud computing; fog computing; COVID-19
Cite This Article
A. Alamri and S. Alamri, "Live data analytics with iot intelligence-sensing system in public transportation for covid-19 pandemic," Intelligent Automation & Soft Computing, vol. 27, no.2, pp. 441–452, 2021.
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