Endogenous Security-Aware Resource Management for Digital Twin and 6G Edge Intelligence Integrated Smart Park

作     者：Sunxuan Zhang Zijia Yao Haijun Liao Zhenyu Zhou Yilong Chen Zhaoyang You Sunxuan Zhang;Zijia Yao;Haijun Liao;Zhenyu Zhou;Yilong Chen;Zhaoyang You

作者机构：State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University)Beijing 102206China Power Dispatching and Control Center of State Grid Shanghai Municipal Electric Power CompanyShanghai 200122China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2023年第20卷第2期

页      面：46-60页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the Science and Technology Project of State Grid Corporation of China under Grant Number 52094021N010 (5400-202199534A-05-ZN) 

主　　题：smart park digital twin(DT) 6G edge intelligence resource management endogenous security awareness 

摘      要：The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart ***,the adverse impact of model poisoning attacks on DT model training cannot be *** address this issue,we firstly construct the models of DT model training and model poisoning *** optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training ***,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack *** adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and *** actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security *** results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.