The goal of this project is to investigate a new generation of machine learning methods for networks comprised of a large number of inexpensive, lightweight, powerful nodes that integrate computation, sensing, and communication. Embedded in the physical world, these nodes will self-assemble into environmentally-aware networks to assist with environmental monitoring, safety, workflows, education, and entertainment. To achieve these goals, such ubiquitous computing networks need to be able to integrate diverse streams of sensor information into coherent views of the environment. This project seeks to create a new generation of machine learning methods to address the resulting data fusion and environmental awareness challenges. The project will develop topology-aware machine learning methods that (a) learn and exploit the topological structure of the environment, and (b) enable collaborative learning among distributed learning agents. This project will test these new machine learning methods on live sensor networks currently installed at two active volcanos: Kilauea (Hawaii) and Mt. Erebus (Antarctica). This project will involve undergraduate and graduate students via research assistantships. Further, this project will involve pre-college students as investigators through middle-school student workshops at the Sally Ride Festival. This work seeks to transform both machine learning and ubiquitous computing by opening up a vast space of novel machine learning problems that are beyond current techniques, and by inspiring development of new capabilities for ubiquitous computing systems.
