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Raza, Mohsin, Singh, Nishant, Khalid, Muhammad, Khan, Suleman, Awais, Muhammad 
ORCID: https://orcid.org/0000-0001-6421-9245, Hadi, Muhammad Usman, Imran, Muhammad, ul Islam, Saif and Rodrigues, Joel J. P. C.
(2021)
Challenges and limitations of Internet of Things enabled healthcare in COVID-19.
IEEE Internet of Things Magazine, 4 (3).
pp. 60-65.
ISSN 2576-3180
Abstract
The emerging challenges in healthcare, especially with the coronavirus (COVID-19) crisis, has changed the way healthcare operates. Countries where the pandemic has hit hard have brought healthcare institutes to the verge of collapsing, where the capabilities of healthcare departments and hospitals are tested over and over. In these challenging circumstances, the technology alternatives are stressed as never before, and the need for transformation of healthcare from traditional techniques to technology-driven healthcare solutions is advocated. While the Internet of Things (IoT) and other healthcare technologies (machine learning, cloud, edge computing, security) have been under development for years, none of the developments were planned to sustain immense pressure, such as that experienced during pandemics and special circumstances. Therefore, a suitable transformation in healthcare technologies is very desirable to cope with the exacerbating world healthcare infrastructure. This article discusses the role of IoT and intelligent healthcare services in emerging health-related threats and challenges. It discusses the limitations, challenges, and future of IoT in health crises. The article also stresses extensive healthcare infrastructure, which benefits from IoT, artificial intelligence, distributed control, cognitive decision support services, security and privacy, blockchain, and cloud and edge services. We use the heuristic bargaining algorithm to maximize the profit of the participants of the task migration in order to increase their enthusiasm.
| Item Type: | Article |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 25 Nov 2023 03:18 |
| Last Modified: | 10 Dec 2024 01:42 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/93755 |
| DOI: | 10.1109/iotm.0001.2000176 |
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