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Secure and Latency-Aware Digital Twin Assisted Resource Scheduling for 5G Edge Computing-Empowered Distribution Grids
IEEE Transactions on Industrial Informatics
  • Zhenyu Zhou, North China Electric Power University, Beijing, China
  • Zehan Jia, North China Electric Power University, Beijing, China
  • Haijun Liao, North China Electric Power University, Beijing, China
  • Wenbing Lu, North China Electric Power University, Beijing, China
  • Shahid Mumtaz, University of Aveiro, Portugal
  • Mohsen Guizani, Mohamed bin Zayed University of Artificial Intelligence
  • Muhammad Tariq, National University of Computer and Emerging Sciences, Pakistan
Document Type
Article
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.

DOI
10.1109/TII.2021.3137349
Publication Date
7-1-2022
Keywords
  • Processor scheduling,
  • Computational modeling,
  • Servers,
  • Scheduling,
  • Job shop scheduling,
  • Energy consumption,
  • Delays,
  • 5G edge computing,
  • digital twin (DT),
  • distribution grid,
  • federated learning (FL),
  • security and latency awareness
Comments

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Citation Information
Z. Zhou, et al, "Secure and Latency-Aware Digital Twin Assisted Resource Scheduling for 5G Edge Computing-Empowered Distribution Grids", IEEE Transactions on Industrial Informatics, vol. 18 , no. 7, p. 4933-4943, doi: 10.1109/TII.2021.3137349