摘要
随着智能出行的推广,车载自组织网络(vehicular ad hoc network,VANET)在数据采集上应用得到越来越多的关注.然而,由于车辆的高速移动和轨迹难以预测,传统的基于位置的贪婪转发策略难以适应于高动态VANET下数据传递的需求.为解决这一问题,提出一种历史交通数据驱动的VANET智能路由算法.首先,通过离线学习方法基于网络的历史交通流信息,获取用于最优路径选择的转发表;其次,在路径上,利用基于Markov预测的在线V2V传输机制,通过考虑车辆的运动状态等因素选择可靠的下一中继车辆.最后,与3种路由算法进行了对比,实验结果表明,该算法在数据包投递率、平均端到端时延、网络收益率、平均成功发包开销和在线计算时间复杂度这5个性能上均表现优越.
With the widespread promotion of smart mobility,there has been increasing attention on the application of vehicular ad hoc network(VANET)in data collection.However,due to the high-speed movement of vehicles and the unpredictability of their trajectories,traditional position-based greedy forwarding strategies struggle to meet the data transmission demands of highly dynamic VANET.To address this issue,an intelligent routing algorithm driven by historical traffic data for VANET(HTD-IR)is proposed.First,an optimal forwarding table for path selection is obtained through an offline learning method based on historical traffic flow information.Then,using an online V2V transmission mechanism based on Markov prediction,the next reliable vehicle is selected according to the vehicle’s motion state.Finally,this study compares HTD-IR with other routing protocols in simulations.The results demonstrate that HTD-IR outperforms in terms of packet delivery ratio,average end-to-end delay,network yield,average successful packet transmission cost,and online computation time complexity.
作者
李洁
陈青
陈侃松
LI Jie;CHEN Qing;CHEN Kan-Song(School of Computer Science,Hubei University,Wuhan 430062,China)
出处
《软件学报》
北大核心
2025年第12期5780-5800,共21页
Journal of Software
基金
国家自然科学基金(62377009)
湖北省自然科学基金青年项目(2023AFB313)
湖北省重点研发计划(2022BAA045)。
关键词
车载自组织网络
强化学习
马尔可夫预测
路由空洞
vehicular ad hoc network(VANET)
reinforcement learning
Markov prediction
routing hole
