IoT-Based COVID-19 Diagnosing and Monitoring Systems: A Survey

Anjum, Nasreen ORCID: 0000-0002-7126-2177, Alibakhshikenari, Mohammad, Rashid, Junaid, Jabeen, Fouzia, Asif, Amna, Mohamed, Ehab Mahmoud and Falcone, Francisco (2022) IoT-Based COVID-19 Diagnosing and Monitoring Systems: A Survey. IEEE Access, 10. pp. 87168-87181. doi:10.1109/ACCESS.2022.3197164

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Text (Peer Reviewed Version) 11526 Anjum, Alibakhshikenari, Rashid, Jabeen, Asif, Mohamed, Falcone (2022) IoT-Based_COVID-19_Diagnosing_and_Monitoring_Systems_A_Survey.pdf - Accepted Version Available under License Creative Commons Attribution 4.0. Download (7MB) | Preview

Abstract

To date, the novel Coronavirus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning (ML) algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent IoT-based COVID-19 diagnosing, and monitoring systems have been proposed to tackle the pandemic. In this article we have analyzed a wide range of IoTs technologies which can be used in diagnosing and monitoring the infected individuals and hotspot areas. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	COVID-19 Pandemic; Coronavirus; Machine Learning Algorithms; Artificial Intelligence (AI); Internet of Things (IoTs) (AI), Internet of Things (IoTs).
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Kate Greenaway
Date Deposited:	14 Sep 2022 10:01
Last Modified:	31 Oct 2023 11:16
URI:	https://eprints.glos.ac.uk/id/eprint/11526

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