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，道路上将有超过5000万辆自动驾驶汽车。车辆的庞大数量和密度为车辆环境中的计算和通信提供了不可忽略的资源。此外，在DSRC和IEEE 802.11p/wave上的智能运输系统（ITS）和标准化活动的需求和执行还驱动了车辆通信。已经提出和探索了许多应用程序，包括时间敏感或延迟耐受性，例如合作交通监控和控制，最近扩展了用于盲人穿越，预防碰撞，实时绕道路线计算以及CAR2CAR定义的许多其他应用程序。通信财团（C2CCC）。除了（智能）移动设备的受欢迎程度外，通过Apple的App Store，Google Play和Windows Phonestore等各种类别的移动应用程序爆炸式爆炸。似乎非常适合车辆到车辆（V2V）临时网络或车辆到基础设施（V2I）通信。因此，车辆网络在促进移动应用程序，推动安全性，网络经济和人们的日常生活中发挥了越来越重要的作用。在该项目中，对车辆网络属性进行了系统的调查，这就是所谓的车间通信的本体论，将执行以获取深入的科学理解和工程指南，这些指南对于获得理论绩效限制和理想的服务至关重要。这项研究包括四个关键的创新贡献：（i）通过使用有限和大规模的V2V和V2I网络中的频谱认知来发现车间网络组成，（ii）车辆移动中的时空相关性，并使用新的约束移动性模型，（iii）检测和识别算法来开发一组传播策略，以使用强化学习和基于病例的推理方案实现快速的邻居发现； （iv）通过遵循车辆网络和方案中的消息轨迹来实现V2V和V2I网络中的最大消息覆盖的理论范围。结果将提高机会主义通信的知识，并促进在车辆环境中急需的应用程序的工程实践。