CSR: III: Small: Collaborative Research: A Hybrid Vehicle-Cloud Solution for Robust, Cost-Efficient Road Monitoring

当今的许多车辆都配备了GPS、网络功能、计算资源以及各种环境和车辆性能传感器。这些技术的结合以及此类车辆的不断增长为大规模道路传感应用提供了强大的平台，以帮助驾驶员进行路线规划和安全驾驶。随着参与车辆数量的增长，数据量及其在蜂窝网络中传输的成本使得当前的集中式解决方案不可持续。该项目提出了一种综合方法，以克服算法和系统的障碍，协同道路传感的成本和可扩展性的挑战。该项目开展四项主要研究任务：（1）获得车辆传感器读数和网络连接性的相关测量，（2）通过车辆到车辆（V2 V）消息委托降低车辆到云（V2 C）通信的成本，（3）开发用于协作道路感测的新的通信高效算法方法，（4）在一个仿真框架中评估通信和算法解决方案，该框架将车辆传感器数据、蜂窝和V2 V连接的测量结果以及I-90/94走廊信息系统的交通流量数据汇集在一起。该项目的成果将包括适用于城市和农村环境的可靠联网车辆通信架构，以及理论上合理的分布式传感和估计算法，这些算法将引起更广泛的分布式计算系统社区的兴趣。通信架构和传感算法将作为大规模协作实时道路传感的原型。广泛采用这种方法将使驾驶员更好地了解危险的驾驶条件，支持更安全和更有效的运输。该项目开发的技术和系统还将应用于需要分析分布在大量设备或系统上的大量传感数据的环境中，例如，在飞机的协同空域监测和物联网的环境监测中。

Many of today's vehicles are equipped with GPS, networking capabilities, computational resources, and a variety of environment and vehicle performance sensors. The combination of these technologies and the growing presence of such vehicles offer a powerful platform for large-scale road sensing applications to assist drivers in route planning and safe driving. As the number of participating vehicle grows, the sheer amount of data and the cost of its transmission in cellular networks make current, centralized solutions unsustainable. This project proposes an integrated approach to overcoming algorithmic and systems barriers to the cost and scalability challenges of collaborative road sensing. The project conducts four main research tasks: (1) obtain correlated measurements of vehicle sensor readings and network connectivity, (2) reduce the cost of vehicle-to-cloud (V2C) communication through vehicle-to-vehicle (V2V) message delegation, (3) develop new communication-efficient algorithmic approaches for collaborative road sensing, (4) evaluate the communication and algorithmic solutions in an emulation framework that pulls together vehicle sensor data, measurements of cellular and V2V connectivity, and traffic flow data from a I-90/94 corridor information system. Results of this project will include reliable connected vehicle communication architecture for both urban and rural settings and theoretically sound decentralized algorithms for sensing and estimation that will be of interest to the broader distributed computing systems community. The communication architecture and sensing algorithms will serve as a prototype for large-scale collaborative, real-time road sensing. The widespread adoption of such an approach will provide drivers with better awareness of hazardous driving conditions, supporting safer and more efficient transportation. Techniques and systems developed in this project will also have applications in settings that require analysis of vast amount of sensed data that is distributed over a large number of devices or systems, for example, in collaborative airspace monitoring in airplanes and environment monitoring in the Internet of Things.