电子学报2025，Vol.53Issue(2)：329-343,15.DOI:10.12263/DZXB.20240609
车联网边缘计算环境下基于流量预测的高效任务卸载策略研究
Efficient Task Offloading Based on Traffic Prediction in IoV-Enabled Edge Computing
摘要
Abstract
Vehicle edge computing combines mobile edge computing and the internet of vehicles(IoV)to offload the vehicle computing tasks from the cloud servers to edge servers,which effectively reduces the response time of IoV services.However,the irregular spatiotemporal distributions of traffic flows in vehicle networking will lead to the imbalance of com-puting load on the edge servers,which impacts real-time responsiveness of vehicle networking services.To address this is-sue,this paper proposes an efficient task offloading strategy based on traffic prediction in the vehicle edge computing.Spe-cifically,a chebyshev graph weighted network(ChebWN)is designed to forecast traffic flow by fully leveraging connectivi-ty and distance information between road segments.Next,a deep reinforcement learning-based binary task offloading algo-rithm(DBOA)is designed,which divides the binary task offloading decision process into two stages.Initially,a deep rein-forcement learning approach is employed to derive the offloading strategies.Subsequently,a one-dimensional bi-end search algorithm is utilized to determine the time slot allocation scheme that maximizes the overall computation rate,thereby reduc-ing the complexity of the decision-making process.Finally,a large number of comparative experiments demonstrate the accu-racy of ChebWN in predicting traffic flow and the superiority of DBOA in improving the response speed of vehicle services.关键词
移动边缘计算/深度强化学习/车联网/图神经网络(GNN)/任务卸载Key words
mobile edge computing/deep reinforcement learning/internet of vehicles/graph neural network/task offloading分类
电子信息工程引用本文复制引用
许小龙,杨威,杨辰翊,程勇,齐连永,项昊龙,窦万春..车联网边缘计算环境下基于流量预测的高效任务卸载策略研究[J].电子学报,2025,53(2):329-343,15.基金项目
国家自然科学基金(No.62372242,No.92267104) National Natural Science Foundation of China(No.62372242,No.92267104) （No.62372242,No.92267104）
