路侧装置修正位置预测模型在Vanet混合路由算法中的应用

The application of modified roadside device position prediction model in Vanet hybrid routing algorithm

在Vanet应用场景中,由于车辆高速运动导致车辆节点构成的网络拓扑不断变化,多数路由协议需要及时维护自己的邻居表来选择路由。邻居选择出错会出现数据频繁重发,导致传输时延高且不可靠等现象。为此本文提出了一种基于高速公路应用场景的高效的邻居发现方法NDK(Vanet Neighbor Discovery method By Kalman filter)。该方法利用经典的地理位置路由算法GPSR思想,借助于卡尔曼滤波(Kalman filter)预测模型来预测节点的邻居表,同时周期性的使用路侧装置(RSU,Road Side Unit)修正预测值。通过NS-3的仿真实验表明,该算法较经典的GPSR算法和其他基于时间、移动预测邻居表的算法能更好判断节点的加入和离开,并有更好的邻居正确率和更轻的网络负载。

In the Vanet application scenario, due to the high speed motion of the vehicle, the topology of the network keep changing, and most routing protocols need to maintain their neighbor table in time for routing select. Frequent retransmission of data caused by neighbor selection error will result in high time delay and unreliability. Many Vanet classic protocols cannot be applied to all scenarios. For this reason, this paper proposes a hybrid Vannet routing algorithm based on the highway application scenario NDK （Vanet Neighbor Discovery Method By Kalman Filter）. The algorithm uses the GPSR （Greedy Perimeter Stateless Routing） idea of the classic geographic location routing algorithm, with the help of Kalman filter prediction model to predict the neighbor node table, and at the same time, the predicted values are periodically modified by roadside device （RSU, Road Side Unit）. The result of NS-3 simulation experiments show that,compared with the classical GPSR algorithm and other algorithms based on time and motion, the algorithm has better packet arrival rate and lower transmission delay.