VANET环境下基于道路受限的路由方案研究

发布时间：2018-06-27 13:06

本文选题：VANET + 路由协议　；参考：《吉林大学》2017年硕士论文

【摘要】：VANET(Vehicular Ad-hoc Network)作为无线网络领域的热门话题,目标是在车辆与车辆或者车辆与路边设施之间完成通信,而完成通信的路径则是由路由算法决定的。由于车辆通常是高速移动的状态,传统静态网络的路由算法已经完全不适用于VANET。因此,在VANET中提出新算法以完成数据的有效传输是解决问题的关键。基于当前研究现状提出一个适合复杂交通环境的路由算法仍面临许多挑战。首先,车辆移动速度快,导致车辆之间的通信链路可靠性较差。其次,在VANET中,由于车辆的移动而导致网络拓扑结构的不断变化,使得车辆之间的单跳通信链路无法维持远距离多跳数据的传输。第三,车辆之间的通信必须在其通信范围内,车辆间这种机会性的通信使得远距离数据传输得不到可靠保障并且时延较长。针对VANET的特点,基于地理位置的路由比较适用于VANET环境。然而目前的路由方案主要考虑车辆的地理位置和运行方向等,忽视了交通道路对于车辆行驶的限制,以及驾驶员对于路径选择的偏好等因素。由于在实际交通环境中,车辆并不是漫无目的的随机运动,而是沿着交通道路从一个始发地到目的地。因此,道路受限对发掘车辆轨迹信息有关键的作用。如果获得车辆行驶轨迹,便等同于得到了VANET节点之间数据转发的时机。本文结合道路受限因素提出一种基于GPSR的VANET路由改进方案,用来更有效地找到数据转发的路径。本文通过分析数据转发过程中通信链路断裂的原因,利用路径约束提出路由算法的启发式规则,总结车辆轨迹的规律性并建立运动模型,通过判断下一跳转发车辆是否位于交通路口处提出不同的路由选择策略。本文利用NS2对实验结果进行验证。首先在NS2中基于GPSR源码进行改进,利用C++代码实现新路由策略的逻辑机制,将改进后的代码在NS2目录下重新编译。然后利用SUMO模拟车流信息,最后将车流信息嵌入到NS2的Tcl脚本中,对生成的trace文件进行整理分析。针对分组投递率、端到端时延及吞吐量等指标对本文方案与GPSR协议进行比较,结果显示本方案的实效性和高效性均有提高。
[Abstract]:As a hot topic in wireless network field, VANET (vehicle Ad-hoc Network) aims to complete communication between vehicle and vehicle or vehicle and roadside facilities, and the path to complete communication is determined by routing algorithm. Because the vehicle is usually moving at high speed, the traditional routing algorithm of static network is no longer suitable for VANET. Therefore, the key to solve the problem is to propose a new algorithm in VANET to complete the effective transmission of data. Based on the current research situation, it is still facing many challenges to propose a routing algorithm suitable for complex traffic environment. First of all, the fast moving speed of vehicles leads to poor reliability of communication links between vehicles. Secondly, in VANET, because of the moving of vehicles, the network topology changes continuously, which makes the single hop communication link between vehicles unable to maintain the transmission of long-distance multi-hop data. Thirdly, the communication between vehicles must be within the range of their communication. This kind of opportunistic communication between vehicles makes the long-distance data transmission not reliable and the delay is longer. In view of the characteristics of VANET, geographical location based routing comparison is suitable for VANET environment. However, the current routing scheme mainly considers the location and direction of the vehicle, neglects the restriction of the traffic road to the vehicle, and the preference of the driver for the route choice and so on. Because in the actual traffic environment, the vehicle is not aimless random motion, but along the traffic road from a place of origin to the destination. Therefore, road restriction plays a key role in excavating vehicle track information. If the vehicle track is obtained, it is equal to the time of data forwarding between VANET nodes. In this paper, we propose an improved VANET routing scheme based on GPSR, which is used to find the path of data forwarding more effectively. In this paper, by analyzing the reason of communication link breakage in the process of data forwarding, the heuristic rules of routing algorithm are proposed by using path constraints, and the regularity of vehicle trajectory is summarized and the motion model is established. Different routing strategies are proposed to determine whether the next hop forwarding vehicle is located at a traffic junction. NS2 is used to verify the experimental results. In NS2, the source code is improved based on GPSR, and C code is used to implement the logic mechanism of the new routing strategy. The improved code is recompiled in the NS2 directory. Then, the traffic information is simulated by sumo, and the traffic information is embedded into the TCL script of NS2, and the generated trace file is analyzed. The proposed scheme is compared with the GPSR protocol in terms of packet delivery rate, end-to-end delay and throughput. The results show that the effectiveness and efficiency of the proposed scheme are improved.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U495;TN929.5

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