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

CSR：Small：通过边缘计算实现传感器丰富的车辆应用

• 批准号: 1717064
• 负责人: Brian Noble
• 金额: $ 49.94万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717064&HistoricalAwards=false
• 关键词: CSR; Small; Enabling; Sensor; Rich

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.

该项目正在开发支持传感器丰富的车辆应用的方法和系统，方法是将计算卸载到位于路边并嵌入蜂窝网络的边缘计算资源。现代车辆配备了摄像头、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操作的详细监控。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增强的驾驶数据显示(例如，突出显示车辆挡风玻璃上的逐个转弯方向或可用停车位)。然而，传感器丰富的应用程序也需要大量的计算能力，这是车辆无法获得的。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，并且广域网络上可用的带宽太有限。相反，该项目通过将传感器丰富的应用程序的计算负载转移到位于网络边缘的附近服务器来支持这些应用程序。这项工作有可能使应用程序具有巨大的社会效益。传感器丰富的车辆应用程序有望提高驾驶效率；例如，通过直接将司机引导到可用的停车点来减少拥堵和空转时间。这些应用程序还可以显著提高道路安全；例如，通过提供自动、群众来源的道路危险检测。该项目正在制作这些应用程序的原型，并开发部署它们所需的基础设施。该项目开发的研究成果、车辆应用和环境的痕迹以及开源软件构件可在以下位置获得：http://pervasive.eecs.umich.edu/vehicular-edge.html.Since车辆将仅在有限的时间段内保持在边缘资源附近，该项目正在创建从一个边缘资源到另一个边缘资源的快速状态切换和计算方法。车辆移动性在网络和服务性能中引入了相当大的变异性，因此该项目正在调查冗余的原则使用如何能够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紧迫的应用程序，该项目使用投机性执行来假设依赖于车辆速度和方向的多个可能的未来，基于这些未来计算结果，然后根据计算期间的实际车辆运动选择适当的响应。

项目成果

RAVEN: Improving Interactive Latency for the Connected Car

• DOI: 10.1145/3241539.3241571
• 发表时间: 2018-10
• 期刊: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking
• 作者: Hyunjong Lee;J. Flinn;Basavaraj Tonshal

Gremlin: scheduling interactions in vehicular computing

Gremlin：车辆计算中的交互调度

• DOI: 10.1145/3132211.3134450
• 发表时间: 2017
• 期刊: ACM/IEEE Symposium on Edge Computing
• 作者: Lee, Kyungmin;Flinn, Jason;Noble, Brian D.
• 通讯作者: Noble, Brian D.