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Software Defined Networking Based On-Demand Routing Protocol in Vehicle Ad-Hoc Networks

机译:车辆自组网中基于软件定义网络的按需路由协议

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摘要

This paper comes up with a SDN Based Vehicle Ad-Hoc On-Demand Routing Protocol (SVAO), which separates the data forward-ing layer and network control layer, as in software defined networking (SDN), to enhance data transmission efficiency within vehi-cle ad-hoc networks (VANETs). The roadside service unit plays the role of local controller and is in charge of selecting vehicles to forward packets within a road segment. All the vehicles state in the road. Correspondingly, a two-level design is used. The global level is distributed and adopts a ranked query scheme to collect vehicle information and determine the road segments along which a message should be forwarded. On the other hand, the local level is in charge of selecting forwarding vehicles in each road seg-ment determined by the global level. We implement two routing algorithms of SVAO, and compare their performance in our simu-lation. We compare SVAO with popular ad-hoc network routing protocols, including Optimized Link State Routing (OLSR), Dy-namic Source Routing (DSR), Destination Sequence Distance Vector (DSDV), and distance-based routing protocol (DB) via simula-tions. We consider the impact of vehicle density, speed on data transmission rate and average packet delay. The simulation results show that SVAO performs better than the others in large-scale networks or with high vehicle speeds.
机译:本文提出了基于SDN的车辆ad-hoc按需路由协议(SVAO),其将数据转发层和网络控制层分开,如软件定义的网络(SDN)中,以增强车辆内的数据传输效率-cle ad-hoc网络(vanets)。路边服务单元扮演本地控制器的作用,负责选择车辆以在道路段内转发包。所有车辆在路上。相应地,使用两级设计。全局层面分布并采用排名的查询方案来收集车辆信息并确定应转发消息的道路段。另一方面,本地层面负责通过全球水平确定的每条道路SEG的转发车辆。我们实施了两个SVAO的路由算法,并在我们的SIMU-Lation中比较了它们的性能。我们将SVAO与流行的ad-hoc网络路由协议进行比较,包括优化链路状态路由(OLSR),Dy-Namic源路由(DSR),目的地序列距离矢量(DSDV)和通过Simula的基于距离的路由协议(DB) t。我们考虑车辆密度,速度对数据传输速率和平均数据包延迟的影响。仿真结果表明,SVAO比大规模网络中的其他人表现优于其他网络或高速公路。

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  • 来源
    《中兴通讯技术(英文版)》 |2017年第2期|11-18|共8页
  • 作者单位

    Guangdong Province Key Lab. of Big Data Analysis and Processing, School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, China;

    Guangdong Province Key Lab. of Big Data Analysis and Processing, School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, China;

    Guangdong Province Key Lab. of Big Data Analysis and Processing, School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, China;

    Guangdong Province Key Lab. of Big Data Analysis and Processing, School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, China;

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