Algorithmic and Graph-Theoretic Approaches to Optimal Sensor Placement in Complex Dynamical Systems
Principal Investigator: Shreyas Sundaram
Modern technology has led to the creation of new types of sensors that provide system operators with unique abilities to monitor or estimate the state of large-scale complex systems. Once sensors are in place, state estimates can be obtained by analyzing data gathered from the deployed sensors together with mathematical models of the system. However, as systems increase in scale and complexity, the deployment of sensors for high quality state estimation remains a bottleneck in a broad spectrum of applications ranging from microprocessors to power distribution networks and societal-scale Internet-of-Things. This project supports the creation of new sensor placement (deployment) algorithms with rigorous performance guarantees. The research will produce a new understanding of the fundamental limitations and achievable performance of sensor placement algorithms, and formulate efficient placement algorithms that perform well in the presence of sensor faults and external attacks.
Students: Lintao Ye, Nathaniel Woodford
H. Zhang, R. Ayoub and S. Sundaram, "Sensor Selection for Kalman Filtering of Linear Dynamical Systems: Complexity, Limitations and Greedy Algorithms." Automatica, vol. 78, pp. 202 - 210, April 2017.
A. Mitra and S. Sundaram, "Distributed Functional Observers for LTI Systems." Proceedings of the IEEE Conference on Decision and Control, Melbourne, Australia, 2017.
L. Ye, S. Roy and S. Sundaram, "On the Complexity and Approximability of Optimal Sensor Selection for Kalman Filtering." Submitted to the American Control Conference, 2018.
Keywords: optimization, resilient monitoring, Sensor placement, sensor selection, system monitoring