NeTS: Small: Collaborative Research: The Ontology of Inter-Vehicle Networking with Spatio-Temporal Correlation and Spectrum Cognition

NetS：小型：协作研究：具有时空相关性和频谱认知的车辆间网络本体

• 批准号: 1841491
• 负责人: Min Song
• 金额: $ 8.33万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841491&HistoricalAwards=false
• 关键词: NeTS; Small; Collaborative; Research; Ontology

It is predicted that there will be over 50 million self-driving cars on the road by 2035; the sheer number and density of vehicles have provided non-negligible resources for computing and communication in vehicular environments. In addition, vehicular communications are also driven by the demands and enforcement of intelligent transportation system (ITS) and standardization activities on DSRC and IEEE 802.11p/WAVE. Many applications, either time-sensitive or delay-tolerant have been proposed and explored, such as cooperative traffic monitoring and control, and recently extended for blind crossing, prevention of collision, real-time detour routes computation, and many others as defined by Car2Car Communication Consortium (C2CCC). Along with the popularity of (smart) mobile devices, there is also an explosion of mobile applications in various categories, including terrestrial navigation, mobile games, and social networking, through Apple's App store, Google Play, and Windows phonestore etc. Each aforementioned application seemingly is well-suited for either vehicle-to-vehicle (V2V) ad hoc networks or vehicle-to-infrastructure (V2I) communications. Therefore, vehicular networks have been playing an increasingly important role in promoting mobile applications, driving safety, network economy, and people's daily life.In this project, a systematic investigation of vehicular networking properties, which is so called ontology of inter-vehicle communications, will be carried out to acquire in-depth scientific understanding and engineering guidelines that are critical to achieving theoretical performance limits and desirable services. This research includes four key innovative contributions: (i) the discovery of inter-vehicle networks composition by using spectrum cognition in finite and large-scale of V2V and V2I networks, (ii) the space and time domains correlations of vehicles on the move, and development of a set of dissemination strategies using a new constrained mobility model, (iii) detection and identification algorithms to achieve fast neighbor discovery using reinforcement learning, and case-based reasoning scheme; and (iv) theoretical limits of the coverage of messages by following the message trajectory in vehicle networks and schemes to achieve the maximum message coverage in both V2V and V2I networks. The results will advance the knowledge of opportunistic communications and facilitate engineering practice for much-needed applications in vehicular environments.

据预测，到2035年将有超过5000万辆自动驾驶汽车上路;车辆的数量和密度为车辆环境中的计算和通信提供了不可忽视的资源。此外，智能交通系统（ITS）的需求和实施以及DSRC和IEEE 802.11p/WAVE的标准化活动也推动了车辆通信。许多应用程序，无论是时间敏感或延迟容忍已被提出和探索，如合作的交通监测和控制，最近扩展到盲目穿越，预防碰撞，实时绕行路线计算，以及许多其他定义的车对车通信联盟（C2CCC）。沿着（智能）移动的设备的普及，还存在各种类别的移动的应用的爆炸式增长，包括地面导航、手机游戏和社交网络，通过苹果的App store、Google Play和Windows phonestore等。每个前述应用似乎都非常适合于车辆到车辆（V2 V）自组织网络或车辆到基础设施（V2 I）通信。因此，车联网在推动移动的应用、行车安全、网络经济以及人们的日常生活中发挥着越来越重要的作用。将进行收购-深入的科学理解和工程指导方针，对于实现理论性能极限和理想服务至关重要。该研究包括四个关键的创新贡献：（i）通过在有限和大规模的V2 V和V2 I网络中使用频谱认知来发现车辆间网络组成，（ii）移动中车辆的空间和时间域相关性，以及使用新的约束移动模型开发一套传播策略，（iii）使用强化学习和基于案例的推理方案来实现快速邻居发现的检测和识别算法;和（四）通过遵循车辆网络中的消息轨迹以及在V2 V和V2 I中实现最大消息覆盖的方案来确定消息覆盖的理论限制网络.研究结果将促进机会通信的知识，并促进车辆环境中急需的应用程序的工程实践。