Mid-Scale RI-1: SAGE: A Software-Defined Sensor Network

能够收集和分析数据的分布式智能传感器网络对于科学家来说至关重要，他们试图了解全球城市化、洪水和野火等自然灾害以及气候变化对自然生态系统和城市基础设施的影响。SAGE是一个试点项目，它组装传感器节点以支持机器学习框架，并将其部署在加利福尼亚州、科罗拉多州和堪萨斯州的环境试验台以及伊利诺伊州和德克萨斯州的城市环境中进行严格测试。在这些试验床上运行的可重复使用的网络基础设施将为气候、交通和生态系统科学家提供新的数据，用于建立研究这些耦合系统的模型。在Sage中开发的软件组件是开源的，并提供了一个开放的体系结构，使来自广泛领域的科学家能够构建自己的智能传感器网络。该工具包还扩展了芝加哥目前使用的教育课程，并将通过为学生提供一个平台，让他们探索与自然和建筑环境相关的基于测量的科学问题，从而激励年轻人--重点是女性和少数族裔--追求科学、技术和数学事业。Sage项目设计和构建新的可重复使用的软件组件和网络基础设施服务，以实现智能环境传感器的部署。地理上分布的传感器系统，包括摄像机、麦克风以及天气和空气质量站，可以产生如此大量的数据，以至于直接连接到传感器的嵌入式计算机最适合执行快速高效的分析。该项目探索了将机器学习算法应用于来自此类智能传感器的数据的新技术，并构建了可重复使用的软件，这些软件可以在嵌入式计算机内运行程序，并通过网络将结果传输到中央计算机服务器。SAGE项目维护指向计算机源代码、开放硬件设计文档和传感器规格的链接，以及从网站http://wa8.gl.上的所有试验台节点收集的原始和校准传感器数据数据还托管在云中，以便于数据分析。所有项目数据在项目结束后保留五年。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Distributed, intelligent sensor networks that can collect and analyze data are essential to scientists seeking to understand the impacts of global urbanization, natural disasters such as flooding and wildfires, and climate change on natural ecosystems and city infrastructure. Sage is a pilot project that assembles sensor nodes to support machine learning frameworks and deploy them for rigorous testing in environmental testbeds in California, Colorado, and Kansas and in urban environments in Illinois and Texas. The reusable cyberinfrastructure running on these testbeds will give climate, traffic, and ecosystem scientists new data for building models to study these coupled systems. The software components developed in Sage are open source and provide an open architecture that will enable scientists from a wide range of fields to build their own intelligent sensor networks. The toolkit also extends the current educational curriculum used in Chicago and will inspire young people - with an emphasis on women and minorities -- to pursue science, technology, and mathematics careers by providing a platform for students to explore measurement-based science questions related to the natural and built environments. The Sage project designs and builds new reusable software components and cyberinfrastructure services to enable deployment of intelligent environmental sensors. Geographically distributed sensor systems that include cameras, microphones, and weather and air quality stations can generate such large volumes of data that fast and efficient analysis is best performed by an embedded computer connected directly to the sensor. This project explores new techniques for applying machine learning algorithms to data from such intelligent sensors and builds reusable software that can run programs within the embedded computer and transmit the results over the network to central computer servers. The Sage project maintains links to computer source code, open hardware design documents, and sensor specifications, as well as both the raw and calibrated sensor data collected from all the testbed nodes at the website http://wa8.gl. The data is also be hosted in the cloud to facilitate easy data analysis. All project data is maintained for five years after the project ends.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.