Sage will deploy sensor nodes that support machine learning frameworks in environmental testbeds in California, Colorado, and Kansas and in urban environments in Illinois and Texas. Distributed, intelligent sensor networks that can collect and analyze data are essential for 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 will explore new techniques for applying machine learning algorithms to data from such intelligent sensors and then build reusable software that can run programs within the embedded computer and transmit the results over the network to central computer servers. 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. The project is led by Northwestern University and leverages open source hardware and software developed by Argonne National Laboratory. Sage is a project funded by the National Science Foundation to design and build a new kind of national-scale reusable cyberinfrastructure to enable AI at the edge.
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