Vehicular Edge Computing

Vehicular Edge Computing (VEC) aims to improve the performance of computation-intensive tasks in vehicles by offloading them to nearby edge servers, addressing limitations in onboard processing power. Current research heavily focuses on optimizing task offloading and resource allocation strategies using multi-agent reinforcement learning (MARL), often incorporating techniques like federated learning to protect user privacy and digital twin modeling for improved prediction and resource management. These advancements are significant for enabling real-time applications in autonomous driving, intelligent transportation systems, and other areas requiring low-latency processing of large datasets from mobile devices.

Papers

November 11, 2024

Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks
Lu Yu, Zheng Chang, Yunjian Jia, Geyong Min
Resource Allocation Split Learning Autonomous Driving Technology Vehicular Network Edge Network Ground Vehicle Vehicular Edge Computing Model Partition

October 14, 2024

Content Caching-Assisted Vehicular Edge Computing Using Multi-Agent Graph Attention Reinforcement Learning
Jinjin Shen, Yan Lin, Yijin Zhang, Weibin Zhang, Feng Shu, Jun Li
Reinforcement Learning Multi Agent Edge Computing Graph Convolutional Network Network Topology Vehicular Edge Computing

July 18, 2024

Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation
Kangwei Qi, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Khaled B. Letaief
Deep Reinforcement Learning Power Allocation Reconfigurable Intelligent Surface Phase Shift Vehicular Communication Vehicular Edge Computing

July 16, 2024

Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocation
Yu Xie, Qiong Wu, Pingyi Fan
Multi Agent Reinforcement Learning Resource Allocation Task Offloading Vehicular Edge Computing

July 10, 2024

Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network
Yu Xie, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Jiangzhou Wang, Khaled B. Letaief
Resource Allocation Identical Twin Data Processing Vehicular Edge Computing

July 1, 2024

Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
Wenhua Wang, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Jiangzhou Wang, Khaled B. Letaief
Multi Agent Reinforcement Learning Full Information Speech Based Age Intelligent Transportation System Intelligent Vehicle Federated Graph Neural Network Vehicular Edge Computing

June 17, 2024

Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning
Kangwei Qi, Qiong Wu, Pingyi Fan, Nan Cheng, Qiang Fan, Jiangzhou Wang
Deep Reinforcement Learning Multi Agent Reinforcement Learning Power Allocation Vehicular Edge Computing Vehicle Communication

June 11, 2024

DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
Zhang Liu, Hongyang Du, Junzhe Lin, Zhibin Gao, Lianfen Huang, Seyyedali Hosseinalipour, Dusit Niyato
Deep Neural Network Deep Reinforcement Learning Resource Allocation Vehicular Network Diffusion Policy Task Offloading Vehicular Edge Computing

May 29, 2024

Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing
Xianke Qiang, Zheng Chang, Yun Hu, Lei Liu, Timo Hamalainen
Adaptive Importance Edge Cloud Split Federated Learning Vehicular Edge Computing Vehicle Edge Computing

April 12, 2024

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing
Cui Zhang, Xiao Xu, Qiong Wu, Pingyi Fan, Qiang Fan, Huiling Zhu, Jiangzhou Wang
Deep Reinforcement Learning Vehicle Plan Asynchronous Federated Learning Byzantine Attack Vehicle Data Global Aggregation Vehicular Edge Computing Vehicle Edge Computing

April 6, 2023

Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing
Qiong Wu, Siyuan Wang, Pingyi Fan, Qiang Fan
Vehicular Network Asynchronous Federated Learning Vehicular Edge Computing

August 3, 2022

Asynchronous Federated Learning for Edge-assisted Vehicular Networks
Siyuan Wang, Qiong Wu, Qiang Fan, Pingyi Fan, Jiangzhou Wang
Vehicular Network Local Model Asynchronous Federated Learning Vehicular Edge Computing

August 2, 2022

Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning
Qiong Wu, Yu Zhao, Qiang Fan, Pingyi Fan, Jiangzhou Wang, Cui Zhang
Deep Reinforcement Learning Edge Caching Vehicular Edge Computing

Vehicular Edge Computing

Papers

Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks

Content Caching-Assisted Vehicular Edge Computing Using Multi-Agent Graph Attention Reinforcement Learning

Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation

Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocation

Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network

Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning

Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning

DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach

Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing

Asynchronous Federated Learning for Edge-assisted Vehicular Networks

Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning