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

NetS：小：协作研究：时空关联和频谱认知的车联网本体论

• 批准号: 1527696
• 负责人: Wenye Wang
• 金额: $ 27.81万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527696&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，车辆通信还受到智能交通系统（ITS）的需求和实施以及DSRC和IEEE 802.11p/WAVE标准化活动的推动。许多时间敏感或延迟容忍的应用已经被提出和探索，例如合作交通监控和控制，最近扩展到盲过马路，防止碰撞，实时绕行路线计算，以及由Car2Car通信联盟（C2CCC）定义的许多其他应用。随着（智能）移动设备的普及，各种类型的移动应用程序也出现了爆炸式增长，包括地面导航、手机游戏和社交网络，如苹果的App store、b谷歌Play和Windows phonestore等。上述每个应用程序似乎都非常适合于车对车（V2V）自组织网络或车对基础设施（V2I）通信。因此，车联网在推动移动应用、驾驶安全、网络经济以及人们日常生活等方面发挥着越来越重要的作用。在本项目中，将对车辆网络特性进行系统调查，即所谓的车辆间通信本体，以获得深入的科学理解和工程指导，这对于实现理论性能限制和理想服务至关重要。本研究包括四个关键的创新贡献：(i)通过在有限和大规模的V2V和V2I网络中使用频谱认知来发现车辆间网络的组成；（ii）移动中的车辆的空间和时间域相关性，并使用新的受限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实现快速邻居发现的检测和识别算法；（iv）根据车辆网络中的消息轨迹对消息覆盖范围的理论限制，以及在V2V和V2I网络中实现最大消息覆盖的方案。结果将推进机会通信的知识，并促进在车辆环境中急需应用的工程实践。