车联网广播技术研究
发布时间：2019-06-19 12:06
【摘要】：近年来,随着经济、科技和汽车工业的迅猛发展,安全和效率已经成为交通运输中迫切需要解决的两个关键问题。因此,智能交通系统应运而生。车联网作为智能交通系统中的重要组成部分,对于提高智能交通系统的安全性和高效性具有十分重要的意义。车联网具有节点高速移动、网络拓扑结构频繁变化、连接可靠性差、传输时延大等特点,这些特点使得广播成为车联网交通信息分发的最有效方式。然而道路交通中车辆速度、密度及网络负载等网络状态的变化将会大大影响车联网的广播性能。但是,现有车联网广播技术的研究仍然无法根据这些网络状态的变化自适应地调整媒体接入控制(Media Access Control, MAC)广播机制,导致广播风暴频繁发生、网络资源分配不合理、无法满足不同信息对服务质量(Quality of Service,QoS)需求。为满足车联网广播性能要求,提高广播效率,本文在车辆与车辆(Vehicle-to-Vehicle,V2V)和车辆与路边基础设施(Vehicle-to-Infrastructure,V2I)两个场景下基于不同网络状态重点研究了车联网中的广播技术,尤其针对车联网中的广播性能从以下三方面进行了重点分析。一、QoS和队列管理是车联网中广播机制的关键问题。缺乏MAC层中缓存队列的建模及分析将无法准确获得广播机制的重要QoS性能,进而将无法全面了解此协议性能。本文在V2V场景下提出了一个二维马尔科夫链模型用于分析不同网络负载及规模下带有有限缓存的车联网系统中广播机制的QoS性能。此外,使用一个简单的方法来求解二维马尔科夫链模型的稳态概率。通过分析表明,繁重的流量负载下,尤其当网络规模较大时,MAC中二元指数回退和重传的缺失导致了糟糕的QoS性能。通过合适的流量控制可以避免这样的性能退化,为车联网维持良好的QoS性能提供了流量控制指南。二、在Drive-Thru网络中,根据距离路边单元(Road-Side Unit, RSU)的远近不同,车辆节点会有不同的信噪比,进而产生不同的传输速率。如果车辆节点无法根据传输速率的改变而自适应地改变发包频率,最小传输速率将会限制系统的性能,导致系统吞吐量低。对于此问题,由于现在还没有一个有效的MAC广播机制解决此问题,本文在V2I场景下提出了一个新的MAC机制用于稀疏高速公路环境中Drive-thru网络;并设计了一个马尔科夫链模型用于分析车辆速度和密度对新广播机制性能的影响。通过分析可以得出,当车辆数较小时,此MAC广播机制能够获得较高的吞吐量并能缓和车辆移动对于系统吞吐量的影响。大量的仿真实验验证了分析模型的精确性和MAC广播机制的有效性。三、在实际的无线传输中,捕获效应是影响无线网络性能的一个主要因素。缺乏捕获效应的建模及分析将无法获得捕获效应对于无线网络性能影响的评价,尤其在考虑了车辆节点移动的情况下。本文在V2I场景下提出了一个车辆网络性能预测模型用于分析不同移动速度及网络规模下基于捕获效应的Drive-Thru网络的性能。通过引进一个车辆交通流模型,建模了道路上车辆的移动,描述了车辆速度、密度和到达率的关系,更准确地模拟了实际道路上车辆的行驶情况。通过分析可以得出,使用此性能预测模型可以得到最优竞争窗口值。基于此最优竞争窗口值,车联网可以达到最大的系统吞吐量而不过多浪费竞争时间。
[Abstract]:In recent years, with the rapid development of economy, science and technology and automobile industry, safety and efficiency have become two key problems in transportation. Therefore, the intelligent traffic system comes into being. As an important part of the intelligent traffic system, the vehicle networking is of great significance to improve the security and efficiency of the intelligent traffic system. The vehicle networking has the characteristics of high-speed movement of nodes, frequent changes of network topology, poor connection reliability and large transmission time delay. However, the change of vehicle speed, density and network load in road traffic will greatly affect the broadcasting performance of the vehicle. However, the research of the prior art of the prior art of the prior art is still unable to adjust the media access control (MAC) broadcast mechanism adaptively according to the change of the state of the network, resulting in the frequent occurrence of the broadcast storm and the unreasonable allocation of the network resources, The quality of service (QoS) requirements for different information cannot be met. In order to meet the requirements of the vehicle-to-network broadcasting performance and improve the broadcasting efficiency, the broadcasting technology in the vehicle-to-network is mainly studied in two scenarios of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2V) and vehicle-to-infrastructure (V2I). In particular, the broadcast performance in the vehicle networking is analyzed from the following three aspects. First, QoS and queue management are the key problems of the broadcasting mechanism in the vehicle networking. The lack of the modeling and analysis of the cache queue in the MAC layer will not be able to accurately obtain the important QoS performance of the broadcast mechanism, and the performance of this protocol will not be fully understood. In this paper, a two-dimensional Markov chain model is proposed to analyze the QoS performance of the broadcast mechanism in a vehicle-of-the-network system with limited cache under different network loads and scale. In addition, a simple method is used to solve the steady-state probability of the two-dimensional Markov chain model. The analysis shows that under the heavy traffic load, especially when the network size is large, the loss of the binary exponential back-off and retransmission in the MAC results in poor QoS performance. Such performance degradation can be avoided by suitable flow control, providing a flow control guide for maintaining good QoS performance for the vehicle networking. 2. In the Drive-Thru network, according to the distance from the road-side unit (RSU), the vehicle nodes will have different signal-to-noise ratios, thus generating different transmission rates. If the vehicle node is unable to adaptively change the contracted frequency according to the change of the transmission rate, the minimum transmission rate will limit the performance of the system, resulting in a low system throughput. In this paper, a new MAC mechanism is proposed in the V2I scenario for the drive-thru network in the sparse highway environment. And a Markov chain model is designed to analyze the effect of vehicle speed and density on the performance of the new broadcasting mechanism. By analysis, it can be concluded that, when the number of vehicles is small, this MAC broadcast mechanism can achieve higher throughput and mitigate the impact of vehicle movement on system throughput. A large number of simulation experiments verify the accuracy of the analytical model and the effectiveness of the MAC broadcast mechanism. In the actual wireless transmission, the capture effect is one of the main factors that affect the performance of the wireless network. The lack of modeling and analysis of the capture effect will not be able to obtain an evaluation of the impact of the capture effect on the performance of the wireless network, especially in the case of the movement of the vehicle node. In this paper, a vehicle network performance prediction model is presented to analyze the performance of the Drive-Thru network based on the capture effect at different speed and network scale. Through the introduction of a vehicle traffic flow model, the movement of the vehicle on the road is modeled, the relationship between the vehicle speed, the density and the arrival rate is described, and the running condition of the vehicle on the actual road is more accurately simulated. Through the analysis it can be concluded that the optimal competition window value can be obtained by using the performance prediction model. Based on this optimal competition window value, the vehicle networking can achieve the maximum system throughput without wasting the competition time.
【学位授予单位】：北京邮电大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：U495
本文编号：2502333
[Abstract]:In recent years, with the rapid development of economy, science and technology and automobile industry, safety and efficiency have become two key problems in transportation. Therefore, the intelligent traffic system comes into being. As an important part of the intelligent traffic system, the vehicle networking is of great significance to improve the security and efficiency of the intelligent traffic system. The vehicle networking has the characteristics of high-speed movement of nodes, frequent changes of network topology, poor connection reliability and large transmission time delay. However, the change of vehicle speed, density and network load in road traffic will greatly affect the broadcasting performance of the vehicle. However, the research of the prior art of the prior art of the prior art is still unable to adjust the media access control (MAC) broadcast mechanism adaptively according to the change of the state of the network, resulting in the frequent occurrence of the broadcast storm and the unreasonable allocation of the network resources, The quality of service (QoS) requirements for different information cannot be met. In order to meet the requirements of the vehicle-to-network broadcasting performance and improve the broadcasting efficiency, the broadcasting technology in the vehicle-to-network is mainly studied in two scenarios of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2V) and vehicle-to-infrastructure (V2I). In particular, the broadcast performance in the vehicle networking is analyzed from the following three aspects. First, QoS and queue management are the key problems of the broadcasting mechanism in the vehicle networking. The lack of the modeling and analysis of the cache queue in the MAC layer will not be able to accurately obtain the important QoS performance of the broadcast mechanism, and the performance of this protocol will not be fully understood. In this paper, a two-dimensional Markov chain model is proposed to analyze the QoS performance of the broadcast mechanism in a vehicle-of-the-network system with limited cache under different network loads and scale. In addition, a simple method is used to solve the steady-state probability of the two-dimensional Markov chain model. The analysis shows that under the heavy traffic load, especially when the network size is large, the loss of the binary exponential back-off and retransmission in the MAC results in poor QoS performance. Such performance degradation can be avoided by suitable flow control, providing a flow control guide for maintaining good QoS performance for the vehicle networking. 2. In the Drive-Thru network, according to the distance from the road-side unit (RSU), the vehicle nodes will have different signal-to-noise ratios, thus generating different transmission rates. If the vehicle node is unable to adaptively change the contracted frequency according to the change of the transmission rate, the minimum transmission rate will limit the performance of the system, resulting in a low system throughput. In this paper, a new MAC mechanism is proposed in the V2I scenario for the drive-thru network in the sparse highway environment. And a Markov chain model is designed to analyze the effect of vehicle speed and density on the performance of the new broadcasting mechanism. By analysis, it can be concluded that, when the number of vehicles is small, this MAC broadcast mechanism can achieve higher throughput and mitigate the impact of vehicle movement on system throughput. A large number of simulation experiments verify the accuracy of the analytical model and the effectiveness of the MAC broadcast mechanism. In the actual wireless transmission, the capture effect is one of the main factors that affect the performance of the wireless network. The lack of modeling and analysis of the capture effect will not be able to obtain an evaluation of the impact of the capture effect on the performance of the wireless network, especially in the case of the movement of the vehicle node. In this paper, a vehicle network performance prediction model is presented to analyze the performance of the Drive-Thru network based on the capture effect at different speed and network scale. Through the introduction of a vehicle traffic flow model, the movement of the vehicle on the road is modeled, the relationship between the vehicle speed, the density and the arrival rate is described, and the running condition of the vehicle on the actual road is more accurately simulated. Through the analysis it can be concluded that the optimal competition window value can be obtained by using the performance prediction model. Based on this optimal competition window value, the vehicle networking can achieve the maximum system throughput without wasting the competition time.
【学位授予单位】：北京邮电大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：U495
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