基于地图导航的车用网络路由算法研究

发布时间：2018-02-20 00:56

本文关键词： 车用网络位置服务电子地图车辆密度位置预测　出处：《电子科技大学》2014年硕士论文　论文类型：学位论文

【摘要】：随着人们生活水平的改善,在道路上行驶的车辆越来越多,驾驶员在驾车的过程中需要越来越多的应用,比如预约停车位、道路拥堵检测、道路湿滑预警等。在城市环境中,网络分割、密度分布不均匀等特点导致路由协议的性能有所下降。大部分的路由协议都假设目标节点是静止的,但在车用网络中,车辆的高速移动性导致拓扑结构变化非常频繁,因而设计一种满足移动车辆通信需求的路由协议变得尤为重要。根据以上的情况,本文提出了一种新的基于地图导航的车用网络路由算法DMBA。在路由算法中,每个路段独立统计车流密度,当车辆行驶到指定路段时,实时地获取路段的车辆密度调整报文的转发策略。在路由算法中,利用电子地图的导航功能可以知道车辆的运动路径、速度、加速度等信息,利用这些信息可以预测出车辆的位置。本文主要的研究成果如下:(1)在车辆通信过程中,路段车流密度是决定报文转发策略的关键因素,本文提出了一种统计路段车流密度的方法,路段上的车辆周期性的向同路段的车辆广播自己的速度等信息,当车辆进入此路段时可以实时的获取车流密度来决定相应的转发策略。(2)车辆虽然是快速移动的,但车辆受限于道路,因而车辆的位置是可以预测的,通过预测目的节点的位置,可以提高数据传输的成功率。本文提出了一种基于元胞自动机的车辆位置预测方法,当前车辆根据周围节点的状态推测出车辆在不同时刻的位置的离散状态,然后通过线性回归的方法得出车辆运动轨迹曲线,利用此曲线可以大致预测出车辆在未来时刻的位置。(3)本文提出了一种根据路段的车流密度动态调整报文转发策略的方法。在路由协议中,当报文处于高密度路段时,报文在传输路径上快速转发,当报文处于低密度路段时,中间节点判断邻居节点的运动路径和报文的传输路径的重合度来选择中继节点,当报文离开传输路径时,主要通过预测邻居节点与目标节点在未来时间能够到达的最短距离和最短延时来判断报文是由当前节点继续携带还是转发。(4)通过NS3仿真工具仿真DMBA和GPSR路由算法,分析了两者的性能。DMBA在数据传输成功率和端到端延时等方面均优于GPSR。
[Abstract]:With the improvement of people's living standards, more and more vehicles are driving on the road, and drivers need more and more applications in the driving process, such as parking reservation, road congestion detection, road slippery warning and so on. Most routing protocols assume that the target nodes are static, but in the vehicle network, most of the routing protocols assume that the target nodes are static. The high speed mobility of vehicles leads to frequent changes in topology, so it is very important to design a routing protocol to meet the communication requirements of mobile vehicles. In this paper, a new vehicle network routing algorithm based on map navigation, DMBA, is proposed. In the routing algorithm, traffic density is counted independently for each section. In the routing algorithm, we can use the navigation function of electronic map to know the information of the vehicle's moving path, speed, acceleration and so on. The main research results of this paper are as follows: 1) in the process of vehicle communication, the traffic density is the key factor to determine the transmission strategy. In this paper, a method of counting the traffic density of road sections is presented. The vehicles on the road sections periodically broadcast their own speed information to the vehicles on the same road sections. When the vehicle enters this section, the vehicle density can be obtained in real time to determine the corresponding forwarding strategy. Although the vehicle is moving fast, the vehicle is restricted by the road, so the position of the vehicle can be predicted, and the location of the destination node can be predicted by predicting the location of the destination node. In this paper, a vehicle position prediction method based on cellular automata is proposed, in which the discrete states of vehicle positions at different times are inferred according to the status of surrounding nodes. Then through the linear regression method to obtain the vehicle trajectory curve, Using this curve, we can roughly predict the position of the vehicle in the future. (3) this paper proposes a method to dynamically adjust the packet forwarding strategy according to the traffic density of the road section. In the routing protocol, when the message is in a high-density section, When the message is in a low density section, the intermediate node determines the coincidence between the neighbor node's motion path and the message's transmission path to select the relay node, and when the message leaves the transmission path, the intermediate node selects the relay node. Mainly by predicting the shortest distance and the shortest delay that the neighbor node and the target node can reach in the future, to judge whether the message is carried or forwarded by the current node.) DMBA and GPSR routing algorithm are simulated by NS3 simulation tool. The performance. DMBA is superior to GPSRs in data transmission success rate and end-to-end delay.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U495

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