摘要
在高速公路车联网场景下,由于车辆节点运行速度快,导致传统车联网网络拓扑结构不稳定,容易造成数据传输中断或重传。使用隐马尔科夫模型预测目的车辆节点下一时刻的位置信息,以便精准规划路由;同时选取行驶较为缓慢的卡车作为移动路侧装置(RSU),以增大信号覆盖范围。集中管理下发路由算法(CMR-SDVN)是指收集各RSU覆盖范围内的所有车辆节点信息,并将其传输至控制器,由控制器创建网络拓扑。车辆节点将传输请求发送给控制器,控制器计算出安全链路并下发至车辆节点,车辆节点根据安全链路进行数据传输。经仿真实验证明,即使在RSU信号未完全覆盖的场景下,CMR-SDVN算法比GPSR算法和JRRS算法更优。
In the scenario of Expressway Internet of vehicles,the fast running speed of vehicle nodes leads to the unstable topology of the traditional Internet of vehicles,which is easy to cause data transmission interruption or retransmission.The hidden Markov model is used to predict the location information of the destination vehicle node at the next time,so as to accurately plan the route.At the same time,the slow-moving truck is selected as the mobile RSU to increase the signal coverage.The centralized management and distribution routing algorithm(CMR-SDVN)collects the status information of all vehicle nodes within the coverage of each road side device(RSU)and transmits it to the controller.The controller creates the network topology.When the vehicle node needs to transmit information,it sends the request to the controller.The controller calculates a security link and sends it to the vehicle node.The node transmits data according to the link.The simulation results show that CMR-SDVN algorithm still has lower end-to-end transmission delay and packet loss rate than GPSR algorithm and JRRS algorithm even in the scenario where RSU signals are not fully covered.
作者
袁学松
YUAN Xuesong(College of Internet and Communication,Anhui Technical College of Mechanical and Electrical Engineering,Wuhu Anhui 241001,China;Technological University of the Philippines,Manila 0900,Philippines)
出处
《重庆科技学院学报（自然科学版）》
CAS
2022年第4期36-41,共6页
Journal of Chongqing University of Science and Technology：Natural Sciences Edition
基金
2019年安徽省高校优秀青年人才支持计划“UHF RFID关键技术在医患关系系统中的应用研究”(2019GXYQ332)
2021年安徽省高校自然科学重点项目“面向隐私保护的移动群智感知智能绿波控制方法研究”(KJ2021A1521)。
