CPS: Small: Dynamically Managing the Real-time Fabric of a Wireless Sensor-Actuator Network
Lead PI:
Michael Lemmon
Co-PI:
Abstract
The objective of this research is to develop algorithms for wireless sensor-actuator networks (WSAN) that allow control applications and network servers to work together in maximizing control application performance subject to hard real-time service constraints. The approach is a model-based approach in which the WSAN is unfolded into a real-time fabric that captures the interaction between the network's cyber-processes and the application's physical-processes. The project's approach faces a number of challenges when they are applied to wireless control systems. This project addresses these challenges by 1) using network calculus concepts to pose a network utility maximization (NUM) problem that maximizes overall application performance subject to network capacity constraints, 2) using event-triggered message passing schemes to reduce communication overhead, 3) using nonlinear analysis methods to more precisely characterize the problem's utility functions, and 4) using anytime control concepts to assure robustness over wide variations in network connectivity. The project's impact will be broadened through interactions with industrial partner, EmNet LLC. The company will use this project's algorithms on its CSOnet system. CSOnet is a WSAN controlling combined-sewer overflows (CSO), an environmental problem faced by nearly 800 cities in the United States. The project's impact will also be broadened through educational outreach activities that develop a graduate level course on formal methods in cyber-physical systems. The project's impact will be broadened further through collaborations with colleagues working on networked control systems under the European Union's WIDE project.
Michael Lemmon
Michael Lemmon is a professor of electrical engineering at the University of Notre Dame. He received his PhD and MS in EE from Carnegie-Mellon University in 1987 and 1990, respectively. He got his BSEE from Stanford University in 1979. He was an aerospace engineering from 1979-1986. He joined the faculty of electrical engineering at Notre Dame in 1990. His early research was on neural network, hybrid and cyber-physical systems, wireless sensor-actuator networks, and networked control systems. He is currently studying deep learning for robust adaptive control.
Performance Period: 09/01/2009 - 08/31/2014
Institution: University of Notre Dame
Sponsor: National Science Foundation
Award Number: 0931195