Publication:
IoT-based COVID-19 diagnosing and monitoring systems: a survey

Consultable a partir de

Date

2022

Authors

Anjum, Nasreen
Alibakhshikenari, Mohammad
Rashid, Junaid
Jabeen, Fouzia
Asif, Amna
Mohamed, Ehab Mahmoud

Director

Publisher

IEEE
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión publicada / Argitaratu den bertsioa

Project identifier

European Commission/Horizon 2020 Framework Programme/801538openaire
AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095499-B-C31/ES/

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.

Keywords

Artificial intelligence (AI), Coronavirus, COVID-19 pandemic, Internet of Things (IoTs), Machine learning algorithms

Department

Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren / Institute of Smart Cities - ISC / Ingeniería Eléctrica, Electrónica y de Comunicación

Faculty/School

Degree

Doctorate program

Editor version

Funding entities

Dr. Mohammad Alibakhshikenari acknowledges support from the CONEX-Plus programme funded by Universidad Carlos III de Madrid and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 801538. Additionally, this work was supported by Project RTI2018-095499-B-C31, funded by the Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (MCIU/AEI/FEDER, UE).

This work is licensed under a Creative Commons Attribution 4.0 License.

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