Secure and Latency-Aware Digital Twin Assisted Resource Scheduling for 5G Edge Computing-Empowered Distribution Grids

Document Type

Article

Publication Title

IEEE Transactions on Industrial Informatics

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.

First Page

4933

Last Page

4943

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

IR Conditions:

OA version (pathway a) Accepted version

No embargo

When accepted for publication, set statement to accompany deposit (see policy)

Must link to publisher version with DOI

Publisher copyright and source must be acknowledged

Link to Full Text

Share

COinS