Collaborative Research: Automating Scaling and Data Processing in a Network of Sensors: Towards a Global Network for Lake Metabolism Research and Education

合作研究：传感器网络中的自动化缩放和数据处理：迈向湖泊代谢研究和教育的全球网络

• 批准号: 0446017
• 负责人: Paul Hanson
• 金额: --
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-03-01 至 2011-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0446017&HistoricalAwards=false
• 关键词: Collaborative; Research; Automating; Scaling; Data

This three-year award is a collaborative project by the University of California-San Diego, University of Wisconsin-Madison, and Indiana University to automate the extensibility and scalability of data-generating networks. Rapid advances and deployment of cyberinfrastructure and sensor networks have created opportunities for new knowledge of ecological systems and their role in global environmental processes. Expanding current networks and developing new networks capable of addressing the spatial and temporal variability of important ecological processes such as lake metabolism at regional to global scales will require novel technical improvements in an architecture that transports data from sensors to databases, allows dynamic control of sensors and reconfiguration of the network, addresses data quality assurance, provides data access and query to distributed data in the network as well as to other relevant datasets, and provides tools for analysis. Specifically this project has two major technical goals:1. Develop new methods and tools to help automate the updating of data flows from dynamically deployed sensors to publicly accessible biological databases.2. Develop a suite of new algorithms and software for analysis of biological information to automate detection (real-time) of events based on data from sensors and databases, with applications to classification of signals to biological or physical events or to sensor failure, allowing rapid response.The dissemination of analytical tools and framework developed will be useful to existing research projects, evolving environmental observing systems such as National Ecological Observatory Network, networks, agencies, international partners, and K-16 teachers. Graduate and under graduate students will participate in the project. Both software tools and basic research data from this project will be incorporated into existing and ongoing professional development resources for teachers and instructional resources for students in grades 6-12.

这项为期三年的奖项是加州大学迭戈大学，威斯康星大学麦迪逊分校和印第安纳大学的一个合作项目，以使数据生成网络的可扩展性和可扩展性自动化。 网络基础设施和传感器网络的快速进步和部署为生态系统的新知识及其在全球环境过程中的作用创造了机会。 扩大当前网络并开发能够解决重要生态过程的空间和时间变化的新网络，例如在区域到全球尺度的湖泊代谢，将需要将数据从传感器传达到数据库中的架构中进行新颖的技术改进，允许对传感器的动态控制和重新配置网络，以解决数据质量访问和质量数据，并在数据质量访问方面，及其在质量上分配质量的数据，并在质量上分配质量的数据。 分析。具体而言，该项目有两个主要的技术目标：1。开发新的方法和工具，以帮助自动化从动态部署传感器到公共可访问的生物数据库的数据流的更新。2。开发一套新的算法​​和软件，以分析生物学信息，以根据传感器和数据库的数据自动化事件的检测（实时），并将其应用于对生物或物理事件或传感器失败的信号分类或传感器失败，从而允许快速响应。对分析工具和框架的传播将对现有的研究项目，诸如现有的研究项目，诸如现有的环境环境，诸如现有的环境观察，诸如现有网络，诸如进化的环境观察。和K-16老师。 研究生及研究生将参加该项目。 该项目的软件工具和基础研究数据都将纳入6至12年级学生的教师和教学资源的现有和正在进行的专业发展资源中。