Cloud-edge-device collaborative reliable and communication-efficient digital twin for low-carbon electrical equipment management

Liao, H, Zhou, Z, Liu, N, Zhang, Y, Xu, G, Wang, Z and Mumtaz, S ORCID: https://orcid.org/0000-0001-6364-6149, 2023. Cloud-edge-device collaborative reliable and communication-efficient digital twin for low-carbon electrical equipment management. IEEE Transactions on Industrial Informatics, 19 (2), 1715 - 1724. ISSN 1551-3203

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Abstract

The real-time electrical equipment management, such as renewable energy, controllable loads, and storage units, plays a key role in low-carbon operation of smart industrial park. Digital twin (DT), which explores cloud-edge-device collaboration and artificial intelligence to establish accurate digital representation of physical equipment, is a cutting-edge technology to realize intelligent optimization of electrical equipment management. However, the practical implementation still faces reliability and communication efficiency problems, such as adverse impact of electromagnetic interference on DT reliability, high communication cost of DT model training, and uncoordinated resource allocation among cloud, edge, and device layers. We propose a Cloud-edge-device Collaborative reliable and Communication-efficient DT for lOW-carbon electrical equipment management named C3 -FLOW. It minimizes the long-term global loss function and time-average communication cost by jointly optimizing device scheduling, channel allocation, and computational resource allocation. Simulation results verify that C3 -FLOW performs superior in loss function, communication efficiency, and carbon emission reduction.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Industrial Informatics
Creators:	Liao, H., Zhou, Z., Liu, N., Zhang, Y., Xu, G., Wang, Z. and Mumtaz, S.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	February 2023
Volume:	19
Number:	2
ISSN:	1551-3203
Identifiers:	Number Type 10.1109/tii.2022.3194840 DOI 1607038 Other
Rights:	© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	21 Dec 2022 16:29
Last Modified:	21 Dec 2022 16:29
URI:	https://irep.ntu.ac.uk/id/eprint/47690

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