CSR:Small: Enabling Sensor-Rich Vehicular Applications with Edge Computing

该项目正在开发方法和系统，通过将计算卸载到位于路边和嵌入蜂窝网络的边缘计算资源，支持传感器丰富的车辆应用。现代汽车有摄像头、麦克风、位置和定位传感器，所有这些都补充了对车辆运动和运行的详细监控。这些传感器可以为高级应用提供数据，例如个人驾驶助理、自动道路危险检测和驾驶数据的增强显示（例如，在车辆挡风玻璃上突出显示转弯方向或可用停车位）。然而，传感器丰富的应用也需要大量的计算能力，这在车辆中是不可用的。这些应用程序无法利用云资源，因为与远程数据中心通信的延迟太高，而广域网上可用的带宽太有限。相反，该项目通过将其计算卸载到位于网络边缘的附近服务器来支持传感器丰富的应用程序。这项工作有可能实现具有巨大社会效益的应用。传感器丰富的汽车应用有望提高驾驶效率；例如，通过引导司机直接到可用的停车位来减少拥堵和空转时间。这些应用还可以大大改善道路安全；例如，通过提供自动化、众包的道路危险检测。该项目正在对这些应用程序进行原型设计，并开发部署所需的基础设施。该项目开发的研究结果、车辆应用程序和环境的痕迹以及开源软件工件可在以下网站获得：http://pervasive.eecs.umich.edu/vehicular-edge.html.Since车辆将仅在有限的时间内保持近边缘资源，该项目正在创建从一个边缘资源到另一个边缘资源的状态和计算快速切换的方法。车辆移动性在网络和服务性能方面引入了相当大的可变性，因此该项目正在研究如何原则地使用冗余来改善平均和最坏情况的响应时间。最后，为了支持时间限制非常严格的应用，该项目使用投机执行来假设依赖于车辆速度和方向的多种可能的未来，并根据这些未来计算结果，然后根据计算期间的实际车辆运动选择适当的响应。

This project is developing methodologies and systems that support sensor-rich vehicular applications by offloading computation to edge compute resources located at roadside and embedded within cellular networks. The modern vehicle has cameras, microphones, location and positioning sensors that all supplement detailed monitoring of vehicle motion and operation. These sensors can supply data for advanced applications, such as personal driving assistants, automated road hazard detection, and augmented display of driving data (e.g., highlighting turn-by-turn directions or available parking on the vehicle windshield). However, sensor-rich applications also require substantial computing power that is unavailable in the vehicle. These applications cannot leverage cloud resources because the latency of communicating with remote data centers is too high and the bandwidth available over wide-area networks is too limited. Instead, this project is supporting sensor-rich applications by offloading their computation to nearby servers located at the network edge. This work has the potential to enable applications with great societal benefit. Sensor-rich vehicular applications promise to improve driving efficiency; for example, by reducing congestion and idling time by guiding drivers directly to available parking spots. These applications can also substantially improve road safety; for example, by providing automated, crowd-sourced road hazard detection. The project is prototyping these applications and developing the infrastructure required for their deployment. The research results, traces of vehicular applications and environments, and open-source software artifacts developed by this project are available at: http://pervasive.eecs.umich.edu/vehicular-edge.html.Since vehicles will remain near edge resources for only limited periods of time, the project is creating methods for fast handover of state and computation from one edge resource to another. Vehicle mobility introduces considerable variability in network and service performance, so the project is investigating how the principled use of redundancy can improve average and worst-case response time. Finally, to support applications with extremely tight time bounds, the project is using speculative execution to hypothesize multiple possible futures that depend on vehicle speed and direction, calculate results based on those futures, and then select the appropriate response based upon actual vehicle motion during the time when calculations were made.