Self-driving cars and home assistants provide just a small glimpse of the future cost-costrainted complex cyber-physical-human systems (CPHS) that will integrate engineering systems with the natural word and humans. This project will devise new mathematical tools and methods to systematically describe CPHS and optimize their operation. The application focus is on wireless body area networks, a natural CPHS representative with humans in the loop, heavily resource-constrained operation, and heterogeneous components that are intertwined with and altered by human behavior. The end will result will help understand important factors related to the operation of CPHS and how to optimize their operation. It will advance stochastic modeling, estimation and control theories to collectively address the challenges associated with this problem. It will also expose underrepresented K-12 students in Albany city to STEM fields from the lens of CPHS through project-based school visits and hands-on workshops on simple sensor systems, and prepare UAlbany students to become CPHS innovators by introducing CPHS concepts and activities in existing courses. Women and underrepresented groups will be encouraged to participate in this project by leveraging existing minority and underrepresented groups programs at the University at Albany.
CPHS have the potential to adaptively optimize their operation towards continuous real-time monitoring of an individual's state, environment and related behaviors, while providing real-time recommendations. To unleash the potential of CPHS, unique challenges related to sensing, communication, computation and control need to be jointly addressed in the presence of heterogeneous data, resource constraints and humans-in-the-loop. This fundamental research will advance cyber-physical systems science by devising new mathematical tools and methods, and a theoretical framework that can be used as a building block for various CPHS applications. Specifically, the project will (i) devise a new theoretical stochastic model to describe key CPHS variables and their interactions, (ii) design novel accurate and scalable estimators for CPHS and derive relevant theoretical performance bounds to quantify the fundamental limits of the estimation process in this context, and (iii) devise new controlled sensing, commmunication, and recommendations strategies to optimize the operation of systems with complex dynamics and heterogeneous capabilities.
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.
