重庆邮电大学学报（自然科学版）2026，Vol.38Issue(1)：20-29,10.DOI:10.3979/j.issn.1673-825X.202412230317
面向高移动性车联网场景的V2X卸载决策算法
Predictive V2X offloading decision algorithm for high mobility vehicular network scenarios
摘要
Abstract
Aiming at the problem of insufficient computing resources of road side units(RSU)in offloading hotspots,we propose a new V2X offloading decision algorithm.Firstly,the computing model of local and surrounding vehicle resources,edge server and cloud server is constructed.Based on the constraints such as maximum tolerance delay and available resources,the offloading mode of tasks is pre-determined.Secondly,according to the offloading mode of edge server and V2V,the vehicle position prediction model is constructed by combining the long-short term memory(LSTM)network and Kalman filter,and the set of edge server and removable vehicle can be generated for offloading.Finally,Q-learning algo-rithm is used to achieve the optimal allocation of uninstallation tasks among multiple nodes.Simulation results demonstrate that the proposed algorithm significantly reduces the weighted sum of offloading latency and energy consumption by approxi-mately 11.4%.关键词
车联网/边缘计算卸载/位置预测/长短期记忆(LSTM)网络/卡尔曼滤波/强化学习Key words
vehicular networks/edge computing offloading/location prediction/long short-term memory(LSTM)network/Kalman filtering/reinforcement learning分类
信息技术与安全科学引用本文复制引用
彭维平,蒋崟梦,王戈,宋成..面向高移动性车联网场景的V2X卸载决策算法[J].重庆邮电大学学报（自然科学版）,2026,38(1):20-29,10.基金项目
国家自然科学基金项目(62162009) National Natural Science Foundation of China(62162009) （62162009）
