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A framework for incident detection and notification in vehicular ad-hoc networks.

机译:车辆自组织网络中事件检测和通知的框架。

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摘要

The US Department of Transportation (US-DOT) estimates that over half of all congestion events are caused by highway incidents rather than by rush-hour traffic in big cities. The US-DOT also notes that in a single year, congested highways due to traffic incidents cost over ;Recently, Vehicular Ad-hoc Networks (VANET) employing a combination of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communication have been proposed to alert drivers to traffic events including accidents, lane closures, slowdowns, and other traffic-safety issues.;In this thesis, we propose a novel framework for incident detection and notification dissemination in VANETs. This framework consists of three main components: a system architecture, a traffic incident detection engine and a notification dissemination mechanism. The basic idea of our framework is to collect and aggregate traffic-related data from passing cars and to use the aggregated information to detect traffic anomalies. Finally, the suitably filtered aggregated information is disseminated to alert drivers about traffic delays and incidents.;The first contribution of this thesis is an architecture for the notification of traffic incidents, NOTICE for short. In NOTICE, sensor belts are embedded in the road at regular intervals, every mile or so. Each belt consists of a collection of pressure sensors, a simple aggregation and fusion engine, and a few small transceivers. The pressure sensors in each belt allow every message to be associated with a physical vehicle passing over that belt. Thus, no one vehicle can pretend to be multiple vehicles and then, is no need for an ID to be assigned to vehicles.;Vehicles in NOTICE are fitted with a tamper-resistant Event Data Recorder (EDR), very much like the well-known black-boxes onboard commercial aircraft. EDRs are responsible for storing vehicles behavior between belts such as acceleration, deceleration and lane changes. Importantly, drivers can provide input to the EDR, using a simple menu, either through a dashboard console or through verbal input.;The second contribution of this thesis is to develop incident detection techniques that use the information provided by cars in detecting possible incidents and traffic anomalies using intelligent inference techniques. For this purpose, we developed deterministic and probabilistic techniques to detect both blocking incidents, accidents for examples, as well as non-blocking ones such as potholes. To the best of our knowledge, our probabilistic technique is the first VANET based automatic incident detection technique that is capable of detecting both blocking and non blocking incidents.;Our third contribution is to provide an analysis for vehicular traffic proving that VANETs tend to be disconnected in many highway scenarios, consisting of a collection of disjoint clusters. We also provide an analytical way to compute the expected cluster size and we show that clusters are quite stable over time. To the best of our knowledge, we are the first in the VANET community to prove analytically that disconnection is the norm rather than the exceptions in VANETs.;Our fourth contribution is to develop data dissemination techniques specifically adapted to VANETs. With VANETs disconnection in mind, we developed data dissemination approaches that efficiently propagate messages between cars and belts on the road. We proposed two data dissemination techniques, one for divided roads and another one for undivided roads. We also proposed a probabilistic technique used by belts to determine how far should an incident notification be sent to alert approaching drivers.;Our fifth contribution is to propose a security technique to avoid possible attacks from malicious drivers as well as preserving driver's privacy in data dissemination and notification delivery in NOTICE. We also proposed a belt clustering scheme to reduce the probability of having a black-hole in the message dissemination while reducing also the operational burden if a belt is compromised.
机译:美国运输部(US-DOT)估计,所有拥堵事件中有一半以上是高速公路事故造成的,而不是大城市的高峰时间交通造成的。美国交通部还指出,在一年内,由于交通事故造成的交通拥堵成本超过;最近,采用车辆对车辆(V2V)和车辆对基础设施相结合的车辆专用网络(VANET)(已经提出了V2I)无线通信的功能,以警告驾驶员注意交通事件,包括事故,车道关闭,减速以及其他交通安全问题。;在本文中,我们提出了一种用于VANET中事件检测和通知分发的新颖框架。该框架由三个主要组件组成:系统体系结构,交通事件检测引擎和通知分发机制。我们框架的基本思想是从经过的汽车中收集和汇总与交通相关的数据,并使用汇总的信息来检测交通异常。最后,将经过适当过滤的聚合信息进行分发,以向驾驶员发出有关交通延误和事件的警报。本论文的第一个贡献是一种交通事件通知的体系结构,简称NOTICE。在NOTICE中,传感器带每隔一英里左右的时间间隔定期地嵌入道路中。每个传送带都包含一组压力传感器,一个简单的聚合和融合引擎以及一些小型收发器。每个皮带中的压力传感器允许每条消息与经过该皮带的物理车辆相关联。因此,没有人可以假装是多辆车,因此不需要为每个车分配一个ID。;NOTICE中的车辆装有防篡改的事件数据记录器(EDR),非常类似于已知的商用飞机黑匣子。 EDR负责存储皮带之间的车辆行为,例如加速,减速和换道。重要的是,驾驶员可以使用简单的菜单通过仪表板控制台或通过语音输入向EDR提供输入。本论文的第二个贡献是开发了事件检测技术,该技术使用汽车提供的信息来检测可能的事件并使用智能推理技术的流量异常。为此,我们开发了确定性和概率技术来检测阻塞事件,事故和非阻塞事件(例如坑洞)。据我们所知,我们的概率技术是第一个基于VANET的自动事件检测技术,能够检测阻塞事件和非阻塞事件。;我们的第三项贡献是对车辆流量进行分析,以证明VANET倾向于断开连接在许多高速公路场景中,由不相交的群集组成。我们还提供了一种分析方法来计算预期的群集大小,并且表明群集随时间推移非常稳定。据我们所知,我们是VANET社区中第一个通过分析证明断开是VANET的规范而不是例外的人。我们的第四项贡献是开发专门适用于VANET的数据分发技术。考虑到VANET的断开连接,我们开发了数据分发方法,可以在道路上的汽车和皮带之间有效地传播消息。我们提出了两种数据传播技术,一种用于分割的道路,另一种用于未分割的道路。我们还提出了一种安全带技术,用于确定皮带应将事件通知发送给正在接近的驾驶员的距离。;我们的第五个贡献是提出一种安全技术,以避免可能受到恶意驾驶员的攻击并在数据分发中保护驾驶员的隐私通知中的通知。我们还提出了一种带束聚类方案,以减少消息传播中出现黑洞的可能性,同时还降低了如果带束受到损害的操作负担。

著录项

  • 作者

    Abuelela, Mahmoud.;

  • 作者单位

    Old Dominion University.;

  • 授予单位 Old Dominion University.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 99 p.
  • 总页数 99
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 古生物学;
  • 关键词

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