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: 0446802
• 负责人: Tony Fountain
• 金额: --
• 依托单位: University of California-San Diego
• 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 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0446802&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.

这个为期三年的奖项是加州大学圣地亚哥分校、威斯康星大学麦迪逊分校和印第安纳州大学的一个合作项目，旨在自动化数据生成网络的可扩展性和可扩展性。 网络基础设施和传感器网络的迅速发展和部署为获得关于生态系统及其在全球环境进程中的作用的新知识创造了机会。 扩展现有网络和开发能够解决重要生态过程（如区域到全球范围的湖泊代谢）的空间和时间变化的新网络，将需要在将数据从传感器传输到数据库的架构中进行新的技术改进，允许动态控制传感器和重新配置网络，解决数据质量保证问题，为网络中的分布式数据以及其他相关数据集提供数据访问和查询，并提供分析工具。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，以帮助自动更新从动态部署的传感器到公开访问的生物数据库的数据流。2.开发一套新的算法和软件，用于分析生物信息，以实现自动检测根据传感器和数据库提供的数据，对事件进行（实时）分析，并将其应用于对生物或物理事件或传感器故障的信号进行分类，从而能够作出快速反应。不断发展的环境观测系统，如国家生态观测网络，网络，机构，国际合作伙伴和K-16教师。 研究生和研究生以下的学生将参与该项目。 该项目的软件工具和基本研究数据将纳入现有和正在进行的教师专业发展资源和6-12年级学生的教学资源。