Energy-Efficient UAV Scheduling and Probabilistic Task Offloading for Digital Twin-Empowered Consumer Electronics Industry-Reference-Cited by-同舟云学术

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Energy-Efficient UAV Scheduling and Probabilistic Task Offloading for Digital Twin-Empowered Consumer Electronics Industry

Published:2024-02 Issue:1 Volume:70 Page:2145-2154
ISSN:0098-3063
Container-title:IEEE Transactions on Consumer Electronics
language:
Short-container-title:IEEE Trans. Consumer Electron.

Author:

Huang Xumin¹,Zhang Yang¹,Qi Yuanhang²^ORCID,Huang Caishi³,Hossain M. Shamim⁴^ORCID

Affiliation:

1. School of Automation, Guangdong University of Technology, Guangzhou, China

2. School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, China

3. Department of Biomedical Science, Faulty of Health Science, University of Macau, Macau, China

4. Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Funder

Researchers Supporting Project

King Saud University, Riyadh, Saudi Arabia

Guangzhou Basic Research Program

Key Project in Higher Education of Guangdong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/30/10510045/10458879.pdf?arnumber=10458879

Reference28 articles.

1. State-of-the-Art and Research Opportunities for Next-Generation Consumer Electronics

2. Digital twin-driven smart manufacturing: Connotation, reference model, applications and research issues;Lu;Robot. Comput. Integr. Manuf.,2020

3. Digital Twin Envisioned Secure Air-Ground Integrated Networks: A Blockchain-Based Approach

4. Federated Deep Reinforcement Learning for Task Offloading in Digital Twin Edge Networks

5. UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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