现代信息科技2026，Vol.10Issue(1)：36-40,46,6.DOI:10.19850/j.cnki.2096-4706.2026.01.008
基于深度强化学习的车联网任务卸载和资源分配策略
Task Offloading and Resource Allocation Strategy in Internet of Vehicles Based on Deep Reinforcement Learning
葛高腾1
作者信息
- 1. 西安工程大学 电子信息学院,陕西 西安 710048
- 折叠
摘要
Abstract
Aiming at the problems of large task data volume caused by excessive vehicle service applications,which leads to high latency,high energy consumption,and failure to process tasks in time,this paper proposes a task offloading and resource allocation strategy based on Deep Reinforcement Learning(DRL).Firstly,this paper designs the Internet of Vehicles(IoV)system framework and proposes a task offloading scheme based on cloud-edge-local collaboration.Secondly,it determines the offloading strategy according to task priority,and constructs mathematical models of edge and local task computing in terms of latency and energy consumption.Finally,taking energy consumption and latency as decision objectives,it constructs an objective function to minimize the total user cost.Experimental results show that under the IoV environment simulation experiment,this method performs better than traditional task offloading strategies,significantly reduces system latency,and improves resource utilization rate.关键词
车联网/深度强化学习/任务卸载/资源分配Key words
IoV/DRL/task offloading/resource allocation分类
信息技术与安全科学引用本文复制引用
葛高腾..基于深度强化学习的车联网任务卸载和资源分配策略[J].现代信息科技,2026,10(1):36-40,46,6.
