Ultra-Low AoI Digital Twin-Assisted Resource Allocation for Multi-Mode Power IoT in Distribution Grid Energy Management-Reference-Cited by-同舟云学术

Ultra-Low AoI Digital Twin-Assisted Resource Allocation for Multi-Mode Power IoT in Distribution Grid Energy Management

Published:2023-10 Issue:10 Volume:41 Page:3122-3132
ISSN:0733-8716
Container-title:IEEE Journal on Selected Areas in Communications
language:
Short-container-title:IEEE J. Select. Areas Commun.

Author:

Liao Haijun¹^ORCID,Zhou Zhenyu¹^ORCID,Jia Zehan¹,Shu Yiling¹,Tariq Muhammad²^ORCID,Rodriguez Jonathan³^ORCID,Frascolla Valerio⁴^ORCID

Affiliation:

1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China

2. Electrical Engineering Department, National University of Computer and Emerging Sciences, Islamabad, Pakistan

3. Instituto de Telecomunicações, Campus Universitàrio de Santiago, Aveiro, Portugal

4. Intel Deutschland GmbH, Neubiberg, Germany

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Science and Technology Project of State Grid Corporation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/49/10297956/10234392.pdf?arnumber=10234392

Reference30 articles.

1. Adaptive Digital Twin for Vehicular Edge Computing and Networks

2. LFT-Net: Local feature transformer network for point clouds analysis;gao;IEEE Trans Intell Transp Syst,2023

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4. AoI-Minimal Trajectory Planning and Data Collection in UAV-Assisted Wireless Powered IoT Networks

5. Age-aware Communication Strategy in Federated Learning with Energy Harvesting Devices

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