SDN–IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic
Open accessAbstract
The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.0 architecture. Thus, human-less or less-human operations to keep these IoT-enabled ecosystems running without interruptions have motivated us to design and demonstrate an intelligent automated framework. In this research, we have proposed "EdgeSDN-I4COVID" architecture for intelligent and efficient management during COVID-19 of the smart industry considering the IoT networks. Moreover, the article presents the SDN-enabled layer, such as data, control, and application, to effectively and automatically monitor the IoT data from a remote location. In addition, the proposed convergence between SDN and NFV provides an efficient control mechanism for managing the IoT sensor data. Besides, it offers robust data integration on the surface and the devices required for Industry 4.0 during the COVID-19 pandemic. Finally, the article justified the above contributions through particular performance evaluations upon appropriate simulation setup and environment.
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The complexities of COVID-19 coupled with scarce resources and rising healthcare cost has encouraged many to access remote health management services using computer or similar devices.
“The complexities of COVID-19 coupled with scarce resources and rising healthcare cost has encouraged many to access remote health management services using computer or similar devices [137–140].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [137]
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Self-citation
- Authors
- Rahman, A., Chakraborty, C., Anwar, A., Karim, M. R., Islam, M. J., Kundu, D., Rahman, Z., & Band, S. S.
- Affiliations
- University of Dhaka (BD)
- Birla Institute of Technology, Mesra (IN)
- Deakin University (AU)
- Mawlana Bhashani Science and Technology University (BD)
- Green University of Bangladesh (BD)
- National Yunlin University of Science and Technology (TW)
- Corresponding author
- Chakraborty, Chinmay
- ORCID
- Venue
- Cluster Computing
- Published
- 2021-07-29
- Volume
- 25
- Issue
- 4
- Pages
- 2351-2368
- ISSN
- 1386-7857 (ISSN-L)
- 1573-7543
- PMC
- PMC8318841
- OpenAlex
- W3187666404
- Cited by
- 121
- References
- 65
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 29 · 2023: 29 · 2022: 31 · 2021: 13
- FWCI
- 11.42
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cluster Computing (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Own your potential (DEAKIN) (submittedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- University of Dhaka
- Indian Institute of Technology Kharagpur
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Data and IoT Technologies
- Keywords
- software-defined networking, Internet of Things, Industry 4.0, COVID-19 pandemic, network function virtualization, edge computing, network control plane, data plane, smart industry
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