Secure and latency-aware digital twin assisted resource scheduling for 5G edge computing-empowered distribution grids
Abstract
Digital twin (DT) provides accurate guidance for multidimensional resource scheduling in 5G edge computing-empowered distribution grids by establishing a digital representation of the physical entities. In this article, we address the critical challenges of DT construction and DT-assisted resource scheduling such as low accuracy, large iteration delay, and security threats. We propose a federated learning-based DT framework and present a Secure and lAtency-aware dIgital twin assisted resource scheduliNg algoriThm (SAINT). SAINT achieves low-latency, accurate, and secure DT by jointly optimizing its total iteration delay and loss function, and leveraging abnormal model recognition (AMR). SAINT enables intelligent resource scheduling by using DT to improve the learning performance of deep Q-learning. SAINT supports access priority and energy consumption awareness due to the consideration of long-term constraints. Compared with state-of-the-art algorithms, SAINT has superior performance in cumulative iteration delay, DT loss function, energy consumption, and access priority deficit.
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Robust data integration frameworks and blockchain technologies provide effective solutions to data consistency issues.
“Robust data integration frameworks that standardize and harmonize information from multiple sources, alongside blockchain technologies to ensure data immutability, provide effective solutions [129].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [129]
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“Robust data integration frameworks that standardize and harmonize information from multiple sources, alongside blockchain technologies to ensure data immutability, provide effective solutions [129].”
- Authors
- Zhou, Z., Jia, Z., Liao, H., Lu, W., Mumtaz, S., Guizani, M., & Tariq, M.
- Affiliations
- North China Electric Power University (CN)
- University of Aveiro (PT)
- Mohamed bin Zayed University of Artificial Intelligence (AE)
- National University of Computer and Emerging Sciences (PK)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2022-07
- Volume
- 18
- Issue
- 7
- Pages
- 4933-4943
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W4205228899
- Cited by
- 169
- References
- 27
- Citations by year
- 2026: 19 · 2025: 24 · 2024: 58 · 2023: 48 · 2022: 20
- FWCI
- 14.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- State Grid Corporation of China (KJ21-1-56)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- digital twin, resource scheduling, 5G edge computing, distribution networks, federated learning, deep Q-learning, energy consumption, security threats, saints
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