Abstract: Along with the adaptation of machine learning techniques for domain sciences, computing advances and new technologies such as edge computing platforms have allowed software-defined sensors which are reprogrammable software programs for processing rich data to be moved to the edge to process the sensor data in situ and provide more comprehensive sensing of the environment. Additionally, using edge computing platforms has an advantage that sensor configurations can be modified automatically in real-time derived from local artificial intelligences.

In this talk, we will present some of the examples of utilizing supervised machine learning techniques on edge computing platforms for providing high-dimensional information that helps domain scientists understand the environment better. Additionally, we will explore the needs of explainable artificial intelligence and the edge to Cloud / High Performance Computing (HPC) continuum of computing paradigm from training and optimizing machine learning models to supporting wide-area analysis based on local information aggregation. Furthermore, we will examine how local artificial intelligence can assist automatic sensor reconfiguration to steer the measurements and improve the acquired data.