VeSoNet: Traffic-Aware Content Caching for Vehicular Social Networks Using Deep Reinforcement Learning-Reference-Cited by-同舟云学术

VeSoNet: Traffic-Aware Content Caching for Vehicular Social Networks Using Deep Reinforcement Learning

Published:2023-08 Issue:8 Volume:24 Page:8638-8649
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Aung Nyothiri¹^ORCID,Dhelim Sahraoui¹^ORCID,Chen Liming²^ORCID,Lakas Abderrahmane³^ORCID,Zhang Wenyin⁴^ORCID,Ning Huansheng⁵^ORCID,Chaib Souleyman⁶^ORCID,Kechadi Mohand Tahar¹^ORCID

Affiliation:

1. School of Computer Science, University College Dublin, Dublin 4, Ireland

2. School of Computing, Ulster University, Newtownabbey, U.K.

3. College of Information Technology (CIT), United Arab Emirates University, Al Ain, United Arab Emirates

4. School of Information Science and Engineering, Linyi University, Shandong, China

5. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

6. LabRi Laboratory, Ecole Supèrieure en Informatique, Sidi Bel Abbès, Algeria

Funder

Insight Centre for Data Analytics funded by the Science Foundation Ireland

CONSUS Project funded by the SFI Strategic Partnerships Programme

Origin Enterprises Plc., National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10202228/10070376.pdf?arnumber=10070376

Reference32 articles.

1. Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks

2. Digital Twin Empowered Content Caching in Social-Aware Vehicular Edge Networks

3. EdgeVCD: Intelligent Algorithm-Inspired Content Distribution in Vehicular Edge Computing Network

4. Toward Dynamic Link Utilization for Efficient Vehicular Edge Content Distribution

5. Adaptive Learning Algorithms for Simulation-Based Dynamic Traffic User Equilibrium

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