CPS: Synergy: Self-Sustainable Data-Driven Systems In the Field

CPS：协同：现场自我可持续的数据驱动系统

• 批准号: 1239423
• 负责人: Wendi Heinzelman
• 金额: $ 80万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1239423&HistoricalAwards=false
• 关键词: CPS; Synergy; Self; Sustainable; Data

Data-driven intelligence is an essential foundation for physical systems in transportation safety and efficiency, area surveillance and security, as well as environmental sustainability. This project develops new computer system infrastructure and algorithms for self-sustainable data-driven systems in the field. Research outcomes of the project include (a) a low-maintenance, environmentally-friendly hardware platform with solar energy harvesting and super capacitor-based energy storage, (b) virtualization software infrastructure for low-power nodes to enable inter-operability among distributed field nodes and from/to the data center, and (c) new image and data processing approaches for resource-adaptive fidelity adjustment and function partitioning. The synergy between the self-sustainable hardware, system software support, wireless communications management, and application data processing manifests through global coordination for quality-of-service, energy efficiency, and data privacy.In broader impacts, this project enables data-driven intelligence in the field for important physical system domains. Integration of the technologies involved is accomplished through real-world system deployment and experimentation, including an intelligent campus traffic and parking management system and collaborative work with industry collaborators. The results of this project will further enhance the technological competitiveness for US industries in key areas such as intelligent transportation. The education component includes cross-disciplinary curriculum enhancements and the development of a new instructional platform for realistic experiments with cyber-physical systems. Within the scope of this project, the PIs perform mentoring and outreach activities to recruit/retain women and minorities in science and engineering.

数据驱动的智能是运输安全和效率、区域监控和安全以及环境可持续性的物理系统的重要基础。 该项目开发新的计算机系统基础设施和算法，用于该领域的自我可持续数据驱动系统。 该项目的研究成果包括（a）一个低维护、环保的硬件平台，具有太阳能收集和基于超级电容器的能量存储;（B）低功耗节点的虚拟化软件基础设施，以实现分布式现场节点之间的互操作性和来自/到数据中心的互操作性;以及（c）新的图像和数据处理方法，用于资源自适应保真度调整和功能分区。 通过对服务质量、能源效率和数据隐私的全球协调，自我可持续的硬件、系统软件支持、无线通信管理和应用数据处理之间的协同作用得以体现。在更广泛的影响中，该项目在重要的物理系统领域实现了数据驱动的智能。所涉及的技术的集成是通过现实世界的系统部署和实验完成的，包括智能校园交通和停车管理系统以及与行业合作者的协作。 该项目的成果将进一步提升美国产业在智能交通等关键领域的技术竞争力。 教育部分包括跨学科课程的改进和开发一个新的教学平台，用于网络物理系统的现实实验。 在该项目范围内，项目执行人开展辅导和外联活动，以招募/留住科学和工程领域的妇女和少数民族。