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Research Projects - From Real-Time Sensor Data Streams to Continuous Field Data Models: Formal Foundations and Computational Challenges

Graduates students deploy soil moisture sensors in the blueberry barrens.

Graduates students deploy soil moisture sensors in the blueberry barrens.

Silvia Nittel and  Max Egenhofer were awarded  a $500,000 grant from the National Science Foundation to investigate a computational framework for synthesizing and analyzing fields based on very large numbers of high throughput, real-time sensor data streams, and for creating continuous representations on-the-fly. This project aims to advance the analytical potential of live-streamed data, historical data streams, and model simulations by creating an overarching representation in the form of the field data model with a set of operators that establish the field algebra. Extending sensor data streams to fields allows scientists to work with high-level abstractions which can facilitate  their analytical tasks such as finding insights about changes, trends, or unexpected events happening in the real world. The project will integrate fields and data streams mathematically so that mappings between both are well-defined.

A prototype system was tested at Cherryfield Farms. A soil moisture sensor network was deployed in the blueberry barrens to collect real-time data to support the automated irrigation system.


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