NeTS: Small: VC-VANET: A Sustainable Vehicle-Crowd Based Vehicular Ad Hoc Network Supporting Mobile Cloudlet Computing

随着车辆的不断发展，开发智能交通的创新方法是国家的迫切需求。不久之后，我们将在每个州的道路系统上看到更多的自动驾驶汽车和联网汽车。 据预测，到2020年，道路上将有大约2.5亿辆联网汽车。这种具有来自移动车辆和运输系统的通信能力的车辆环境呈现出非常大且复杂的移动的联网场景，其中多种通信技术可用。 本研究的目标是研究一种以车辆人群为中心的网络系统，该系统能够支持高需求的移动的边缘计算应用。其好处包括更迅速和准确的数据输入和结果输出，以及成为基于基础设施的系统的替代品。 这些应用可以包括：智能交通系统应用、交通相关的计算需求、时间关键的情况感知应用以及可视化。 这项工作的结果将传播到多个学科，并用于开发教育和培训材料，为研究生和本科生，以及提供研究机会，为传统上代表性不足的群体和本科生。拟议的研究将推进有关物理车辆网络和运输运营之间的相互依赖关系的知识，以支持高需求的应用。 该项目设想通过收集计算和存储资源，通过车辆网络协议编织在一起的分组车辆之间的协作来支持这些应用程序的能力。 这种网络架构是分层的，因为这些联网车辆组是在各个车辆的车辆自组织网络和车辆延迟容忍网络之上的附加层。 这项研究的独特重点将是发现车辆拥挤系统的特性与信号控制等因素，并开发自适应车辆拥挤计划，在不利条件下使用从当地城市交通系统收集的数据。本研究主要有三个主要的研究工作：（1）研究单个车辆-人群的特性，即在时间域中特定位置处的动态模式，以及期望恢复时的延迟。(2)调查与多个车辆人群相关的空间和时间属性，以及这些事件是如何协调的。(3)研究预测方法，以帮助维持车辆拥挤。

The development of new and innovative methods for smart transportation is a critical need of the nation as vehicles continue to evolve. Not long from now, we will see more autonomous vehicles and connected vehicles on the road systems in each state. It is predicted that by 2020 there will be approximately 250 million connected vehicles on the road. Such a vehicular environment, with communication capabilities from the moving vehicles and the transportation systems, presents an extremely large and complex mobile networking scenario where multiple communication technologies are available. The goal of this research is to investigate a vehicle-crowd centered, networked system that has the capability to support high-demand mobile edge computing applications. The benefits could range from more prompt and accurate data inputs and result outputs, to being an alternative for infrastructure-based systems. The applications could include: intelligent transportation system applications, transportation related compute-hungry, time-critical situation-aware applications, and visualizations. Results of this effort will be disseminated to multiple disciplines, and be used to develop education and training materials for both graduate and undergraduate students as well as provide research opportunity for traditionally under-represented groups and undergraduate students. The proposed research will advance the knowledge about the interdependent relationships between the physical vehicle network and the transportation operation for supporting high-demand applications. This project envisions the capability to support these applications through harvesting the computing and storage resources by collaborations among grouped vehicles which are knitted together via the vehicular networking protocols. Such a network architecture is hierarchical as these groups of connected vehicles are an additional layer on top of vehicle ad hoc networks and vehicle delay tolerant networks of individual vehicles. The unique emphasis of this research will be finding the vehicle-crowd system properties relating to signal control among other factors, and developing adaptive vehicle-crowd schemes in reacting to adverse conditions using data collected from local city transportation systems. This project focuses on three main research tasks: (1) Investigate the properties of an individual vehicle-crowd, namely, the dynamic patterns of its occurrence at a given location in the time domain, and the latency when recovery is desired. (2) Investigate the spatial and temporal properties relating to multiple vehicle-crowds, and how the occurrences coordinate. (3) Investigate prediction methods to help sustain the vehicle-crowds.