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

中型 RI-1：SAGE：软件定义的传感器网络

• 批准号: 1935984
• 负责人: Peter Beckman
• 金额: $ 902.69万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935984&HistoricalAwards=false
• 关键词: Mid; Scale; RI; SAGE; Software

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.

能够收集和分析数据的分布式智能传感器网络对于寻求了解全球城市化、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的科学家来说至关重要。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，并将其部署在加州、科罗拉多和堪萨斯的环境测试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。在这些测试平台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，用于构建模型来研究这些耦合系统。 Sage开发的软件组件是开源的，并提供了一个开放的架构，使来自各个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，并将激励年轻人-重点是妇女和少数民族-通过为学生提供一个平台来探索与自然和建筑环境相关的基于测量的科学问题，从而追求科学，技术和数学事业。Sage项目设计和构建新的可重用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头、麦克风以及天气和空气质量站，它们可以生成大量数据，因此最好通过直接连接到传感器的嵌入式计算机进行快速有效的分析。该项目探索了将机器学习算法应用于此类智能传感器数据的新技术，并构建了可重复使用的软件，该软件可以在嵌入式计算机中运行程序，并将结果通过网络传输到中央计算机服务器。Sage项目维护了计算机源代码、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上的所有测试台节点收集的原始和校准传感器数据。 数据也被托管在云中，以方便数据分析。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Optimizing cloud motion estimation on the edge with phase correlation and optical flow

• DOI: 10.5194/amt-16-1195-2023
• 发表时间: 2023-03
• 期刊: Atmospheric Measurement Techniques
• 影响因子: 3.8
• 作者: B. Raut;P. Muradyan;R. Sankaran;R. Jackson;Seongha Park;Sean Shahkarami;Dario Dematties;Yongho Kim;Joseph Swantek;Neal Conrad;Wolfgang Gerlach;S. Shemyakin;P. Beckman;N. Ferrier;S. Collis

Goal-driven scheduling model in edge computing for smart city applications

• DOI: 10.1016/j.jpdc.2022.04.024
• 发表时间: 2022-05-11
• 期刊: JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
• 影响因子: 3.8
• 作者: Kim, Yongho;Park, Seongha;Beckman, Pete
• 通讯作者: Beckman, Pete

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis

• DOI: 10.1175/aies-d-22-0063.1
• 发表时间: 2023-03
• 期刊: Artificial Intelligence for the Earth Systems
• 作者: Dario Dematties;B. Raut;Seongha Park;Robert C. Jackson;Sean Shahkarami;Yongho Kim;R. Sankaran;P. Beckman;S. Collis;N. Ferrier

Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods

• DOI: 10.3390/atmos12030395
• 发表时间: 2021-03-01
• 期刊: ATMOSPHERE
• 影响因子: 2.9
• 作者: Park, Seongha;Kim, Yongho;Beckman, Pete H.
• 通讯作者: Beckman, Pete H.