An IoT model for supporting global governmental lockdown scenarios: investigating comparative lockdown strategies and assessing generic perception of pandemic response

Abstract

We propose an integrated IoT model to blend IoT technologies, neutrosophic theory and AHP to handle uncertain conditions of real-life situations and aid decision-makers with systematic and optimum decisions. In our case study, four ranked scenarios are assigned the appropriate IoT technology generated to support the government and competent authorities in the pandemic outbreak to prevent growing risks. Our study is based on the decision-makers’ judgments that need to be expanded with more experts in the various aspects of government and competent authorities. The integrated IoT model provides a balance between the restart of economic life and COVID-19 outbreaks.

Publication DOI: https://doi.org/10.1080/17517575.2023.2300991
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Funding Information: The work was supported by the VC Research [VCR 0000125].
Additional Information: Copyright © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Uncontrolled Keywords: COVID-19,Internet of Things,healthcare,analytic hierarchy process,integrated IoT model
Publication ISSN: 1751-7583
Last Modified: 07 Oct 2024 07:55
Date Deposited: 10 Jan 2024 17:35
Full Text Link:
Related URLs: https://www.tan ... 75.2023.2300991 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-01-31
Published Online Date: 2024-01-09
Accepted Date: 2023-12-28
Authors: Gamal, Abduallah
Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Nabeeh, Nada A.
Abdel-Basset, Mohamed

Download

[img]

Version: Published Version

License: Creative Commons Attribution

| Preview

Export / Share Citation


Statistics

Additional statistics for this record