车载网络中基于移动轨迹预测的快速邻居发现算法  

Fast neighbor discovery scheme based on mobility prediction in vehicular networks

作　　者：贺然[1] 张钢[1] 刘春凤[1] 舒炎泰[1] 

机构地区：[1]天津大学计算机科学与技术学院天津市认知计算与应用重点实验室,天津300072

出　　处：《计算机应用研究》2015年第9期2737-2741,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(61363081);天津大学自主创新基金资助项目(60305007)

摘　　要：车辆网络中节点的快速移动导致网络拓扑频繁变化,快速的邻居发现算法成为影响网络协议性能的重要因素。针对该问题,提出了一种新型的基于卡尔曼滤波器移动轨迹预测的Hello协议,即KFH(Kalman filterbased Hello protocol)。每个节点使用一个基于自适应卡尔曼滤波器的预测模型来预测自己的运动轨迹,当节点预测下一个时隙的位置时,同时也对邻居表中的每个邻居进行预测。如果节点的位置预测精度大于一定的阈值,将广播一个包含自己真实位置的hello消息,接收到该探测信息的节点将更新自己邻居表中相应的模型参数。仿真结果表明,KFH可以实现高效率的邻居发现,提高Hello协议的性能。在同样网络开销情况下,KFH具有最低的邻居发现错误率(只有2%)及邻居发现延迟。In vehicular Ad hoc networks, the rapid moving of nodes leads to the frequent changes of network topology. A fast neighbor discovery algorithm has become an important factor influencing the performance of network protocols. This paper pro- posed a novel mobility prediction Hello protocol based on adaptive Kalman filter, named KFH （ Kalman filter-based Hello pro- tocol）. Each node used a predict model based on adaptive Kalman filter to make mobility prediction for itself. When a node predicted its own position of the next slot, it also made position prediction for each neighbors in the neighbor table. If the pre- diction error was bigger than a preset threshold, a hello message contained the real position of the node would be broadcasted and neighbors received the message would update the corresponding parameters of predict model. Simulation results show that KFH achieves a higher efficient neighbor discovery and improves the performance of Hello protocol. KFI-I reaches the lowest neighbor error rate （ only about 2% ） and the shortest neighbor discovery delay under the same network overhead.

关 键 词：车辆自组织网络 邻居发现 移动预测 卡尔曼滤波 

分 类 号：TP399[自动化与计算机技术—计算机应用技术] TP301.6[自动化与计算机技术—计算机科学与技术]