FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing-Reference-Cited by-同舟云学术

FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing

Published:2023-08 Issue:8 Volume:72 Page:11086-11091
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Li Bin¹^ORCID,Xie Wancheng¹^ORCID,Ye Yinghui²^ORCID,Liu Lei³^ORCID,Fei Zesong⁴^ORCID

Affiliation:

1. School of Computer Science and the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China

2. Shaanxi Key Laboratory of Information Communication Network and Security, Xi'an University of Posts & Telecommunications, Xi'an, China

3. Guangzhou Institute of Technology, Xidian University, Guangzhou, China

4. School of Information and Electronics, Beijing Institute of Technology, Beijing, China

Funder

National Natural Science Foundation of China

Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu

Natural Science Foundation of Jiangsu Province

Shaanxi Key Laboratory of Information Communication Network and Security

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/25/10230043/10083236.pdf?arnumber=10083236

Reference18 articles.

1. Digital Twin-Aided Intelligent Offloading With Edge Selection in Mobile Edge Computing

2. Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks

3. Digital Twin-Driven Vehicular Task Offloading and IRS Configuration in the Internet of Vehicles

4. Edge Intelligence-Based Ultra-Reliable and Low-Latency Communications for Digital Twin-Enabled Metaverse

5. Adaptive Digital Twin and Multiagent Deep Reinforcement Learning for Vehicular Edge Computing and Networks

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