计算机工程2025，Vol.51Issue(9)：25-37,13.DOI:10.19678/j.issn.1000-3428.0069836
基于空地协同的动态车载边缘任务卸载方法
Dynamic Vehicle Edge Task Offloading Method Based on Air-Ground Collaboration
摘要
Abstract
To optimize Quality of Service(QoS),Mobile Edge Computing(MEC)has been deeply integrated into the Internet of Vehicle(IoV)to provide geographically proximal computing resources for vehicles,thereby reducing task processing latency and energy consumption.However,traditional MEC server deployment relies primarily on terrestrial Base Stations(BSs),resulting in high deployment costs and limited coverage,making it difficult to ensure uninterrupted services for all vehicles.Air-ground collaborative IoV technology has emerged as a solution to these challenges.Unmanned Aerial Vehicles(UAVs)can dynamically assist Road-Side Units(RSUs)using their flexibility in line-of-sight links,providing more flexible computing resources for vehicular users,thereby ensuring the continuity and efficiency of in-vehicle services.Therefore,this study proposes a Dynamic Vehicular Edge Task Offloading Method(DVETOM)based on air-ground collaboration.This method adopts a vehicle-road-air architecture,establishing Vehicle-to-RSU(V2R)and Vehicle-to-UAV(V2U)links.Transmission and computation models are constructed for three modes:local execution of vehicular tasks,offloading tasks to the RSU,and offloading tasks to the UAV.An objective function is established with the joint optimization goal of minimizing system latency and energy consumption.DVETOM transforms the task offloading problem into a Markov Decision Process(MDP)and optimizes the task offloading strategy by using the Distributed Deep Deterministic Policy Gradient(D4PG)algorithm based on Deep Reinforcement Learning(DRL).Compared with 5 benchmark methods,experimental results show that DVETOM outperforms existing methods by 3.45％-23.7％in terms of reducing system latency and 5.8％-23.47％in terms of reducing system energy consumption while improving QoS for vehicular users.In conclusion,DVETOM enhances the offloading of vehicular edge computing tasks within the IoV effectively.It offers IoV users a more efficient and energy-conserving solution,showcasing its extensive potential for application in intelligent transportation systems.关键词
车联网/边缘计算/空地协同/任务卸载/深度强化学习Key words
Internet of Vehicle(IoV)/edge computing/air-ground collaboration/task offloading/Deep Reinforcement Learning(DRL)分类
信息技术与安全科学引用本文复制引用
崔萌萌,施静燕,项昊龙..基于空地协同的动态车载边缘任务卸载方法[J].计算机工程,2025,51(9):25-37,13.基金项目
国家自然科学基金(62372242) （62372242）
江苏省自然科学基金(BK20211284). （BK20211284）
