车载网络中基于移动轨迹预测的快速邻居发现算法研究

发布时间：2018-01-11 01:32

本文关键词：车载网络中基于移动轨迹预测的快速邻居发现算法研究　出处：《天津大学》2014年硕士论文　论文类型：学位论文

【摘要】：车载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。传统移动网络中,有很多可以用来提高数据传输性能和效率的协议,如路由协议、HELLO协议等。HELLO协议用于邻居发现和邻居维护,常与路由协议相结合以提高路由协议的性能。在车载自组织网络中,如果每个节点的邻居表越精确,那么节点间的数据包投递率就会越高。本文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,KFH(Kalman Filter-based HELLO protocol)。该协议将时间域划分为相同长度的时隙,每一个节点拥有一个基于卡尔曼滤波器的预测模型,根据网络环境中车辆移动的时间和空间特性来预测节点的运动轨迹。当节点运用该模型预测自己下一个时隙的位置时,也同时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hello探测信息,同时把自己的真实位置信息发送给邻居车辆,接收到该探测信息的节点将使用最新的数据更新自己邻居表中相应的模型参数。在每个时隙中,节点使用自己对邻居节点的位置预测数据,计算自己与每个邻居之间的距离,超出节点通信范围的邻居将会被删除。因此,每个节点始终维护着一个最新最精确的邻居表。通过仿真,将KFH与自回归HELLO协议(Autoregressive Hello protocol,ARH),以及广泛使用的基于一定时间间隔的HELLO协议进行了对比。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。
[Abstract]:Fast moving nodes in vehicular networks leads to frequent change of network topology, fast neighbor discovery algorithm has become an important factor affecting the performance of network protocols. The traditional mobile network, there are many can be used to improve the performance and efficiency of data transmission protocols, such as routing protocol, HELLO protocol and.HELLO protocol for neighbor neighbor discovery and maintenance, often in combination in order to improve the performance of routing protocol and routing protocol. In thevanet, if each node's neighbor table more accurate, so inter node packet delivery ratio will be higher. This paper proposes a method based on Calman filter trajectory prediction HELLO protocol model, KFH (Kalman Filter-based HELLO protocol). The agreement will in time domain is divided into time slots with the same length, each node has a prediction model based on Calman filter, according to the network environment in China The characteristics of time and space vehicles to predict the trajectory of mobile nodes. When a node using the model to predict his position for the next slot, but also to each neighbor table are predicted. If the node position accuracy is greater than a certain threshold value will broadcast a hello detection information. At the same time their true position information is sent to the neighbor node receives the vehicle detection information will use the latest data to update the model parameters of their neighbor tables. In each time slot, the nodes use their neighbor nodes to forecast the location of data, the calculation between himself and the distance to each of its neighbors, the neighbors will be beyond the node communication range deleted. Therefore, each node always maintains a most accurate neighbor table. Through the simulation, KFH and autoregressive (Autoregressive Hello protocol HELLO, ARH), and Compared with widely used a time interval based on the HELLO protocol. The results show that KFH can achieve high efficiency of neighbor discovery, improve the performance of HELLO protocol. The network overhead in the same case, KFH has the lowest neighbor discovery error rate (only 2%) and neighbor discovery delay.

【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U495;TN929.5

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