EPtask: Deep Reinforcement Learning based Energy-efficient and Priority-aware Task Scheduling for Dynamic Vehicular Edge Computing-Reference-Cited by-同舟云学术

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EPtask: Deep Reinforcement Learning based Energy-efficient and Priority-aware Task Scheduling for Dynamic Vehicular Edge Computing

Published:2023 Issue: Volume: Page:1-17
ISSN:2379-8904
Container-title:IEEE Transactions on Intelligent Vehicles
language:
Short-container-title:IEEE Trans. Intell. Veh.

Author:

Li Peisong¹^ORCID,Xiao Ziren¹,Wang Xinheng¹^ORCID,Huang Kaizhu²^ORCID,Huang Yi³^ORCID,Gao Honghao⁴^ORCID

Affiliation:

1. School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China

2. Data Science Research Center & Division of Natural and Applied Sciences, Duke Kunshan University, Suzhou, China

3. Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, U.K.

4. School of Computer Engineering and Science, Shanghai University, Shanghai, China

Funder

Key Program Special Fund in XJTLU

XJTLU Research Development Funding

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/7274857/7448921/10269672.pdf?arnumber=10269672

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2. Multi-path serial tasks offloading strategy and dynamic scheduling optimization in vehicular edge computing networks;Vehicular Communications;2024-10

3. Deep Reinforcement Learning Method for Task Offloading in Mobile Edge Computing Networks Based on Parallel Exploration with Asynchronous Training;Mobile Networks and Applications;2024-08-28

4. Efficient Scheduling of Energy-Constrained Tasks in Internet of Things Edge Computing Networks;International Journal of Swarm Intelligence Research;2024-08-07

5. Ets-ddpg: an energy-efficient and QoS-guaranteed edge task scheduling approach based on deep reinforcement learning;Wireless Networks;2024-07-30

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