190 citations · 276 across the 11 of their papers we have counts for
12 papers
Joint Optimization of Trajectory Control, Resource Allocation, and Task Offloading for Multi-UAV-Assisted IoV
Maoxin Ji, Qiong Wu, Pingyi Fan +4
This paper investigates a multi-Unmanned Aerial Vehicle (UAV) joint base station-assisted Internet of Vehicles (IoV) task offloading system in dense urban environments. To minimize…
Single-Step Six-Dimensional Movable Antenna Reconfiguration for High-Mobility IoV: Modeling, Analysis, and Optimization
Maoxin Ji, Qiong Wu, Pingyi Fan +4
The Six-Dimensional Movable Antenna (6DMA) system has emerged as a promising technology to enhance wireless capacity by fully exploiting spatial degrees of freedom. However, applyi…
Personalized Federated Distillation Assisted Vehicle Edge Caching Strategy
Xun Li, Qiong Wu, Pingyi Fan +3
Vehicle edge caching is a promising technology that can significantly reduce the latency for vehicle users (VUs) to access content by pre-caching user-interested content at edge no…
Optimizing System Latency for Blockchain-Encrypted Edge Computing in Internet of Vehicles
Cui Zhang, Maoxin Ji, Qiong Wu +2
As Internet of Vehicles (IoV) technology continues to advance, edge computing has become an important tool for assisting vehicles in handling complex tasks. However, the process of…
Optimizing Age of Information in Internet of Vehicles Over Error-Prone Channels
Cui Zhang, Maoxin Ji, Qiong Wu +2
In the Internet of Vehicles (IoV), Age of Information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although man…
Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehicles
Cui Zhang, Wenjun Zhang, Qiong Wu +4
The Internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of…
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing
Cui Zhang, Wenjun Zhang, Qiong Wu +4
Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead…
Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing
Cui Zhang, Xiao Xu, Qiong Wu +4
In vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the edge receives a local model and updates the global model, effectively reducing the global…
Asynchronous Federated Learning Based Mobility-aware Caching in Vehicular Edge Computing
Wenhua Wang, Yu Zhao, Qiong Wu +3
Vehicular edge computing (VEC) is a promising technology to support real-time applications through caching the contents in the roadside units (RSUs), thus vehicles can fetch the co…
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning
Qiong Wu, Yu Zhao, Qiang Fan +3
The vehicular edge computing (VEC) can cache contents in different RSUs at the network edge to support the real-time vehicular applications. In VEC, owing to the high-mobility char…
High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks
Qiong Wu, Xiaobo Wang, Qiang Fan +3
Federated edge learning (FEEL) technology for vehicular networks is considered as a promising technology to reduce the computation workload while keeping the privacy of users. In t…
Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method
Qiong Wu, Shuai Shi, Ziyang Wan +3
Vehicles on the road exchange data with base station (BS) frequently through vehicle to infrastructure (V2I) communications to ensure the normal use of vehicular applications, wher…