182 citations · 1k across the 67 of their papers we have counts for
9 papers · 1 filter
Structure-Enhanced DRL for Optimal Transmission Scheduling
Jiazheng Chen, Wanchun Liu, Daniel E. Quevedo +3
Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, we focus on the transmission scheduling pr…
Innovation-Based Remote State Estimation Secrecy with no Acknowledgments
Justin M. Kennedy, Jason J. Ford, Daniel E. Quevedo +1
Secrecy encoding for remote state estimation in the presence of adversarial eavesdroppers is a well studied problem. Typical existing secrecy encoding schemes rely on the transmitt…
Structure-Enhanced Deep Reinforcement Learning for Optimal Transmission Scheduling
Jiazheng Chen, Wanchun Liu, Daniel E. Quevedo +2
Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, by leveraging the theoretical results of s…
Remote State Estimation with Privacy Against Eavesdroppers
Matthew Crimson, Justin M. Kennedy, Daniel E. Quevedo
We study the problem of remote state estimation in the presence of a passive eavesdropper, under the challenging network environment of no packet receipt acknowledgments. A remote…
Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach
Zihuai Zhao, Wanchun Liu, Daniel E. Quevedo +2
Wireless networked control system (WNCS) connecting sensors, controllers, and actuators via wireless communications is a key enabling technology for highly scalable and low-cost de…
Bayesian Quickest Change Detection of an Intruder in Acknowledgments for Private Remote State Estimation
Justin M. Kennedy, Jason J. Ford, Daniel E. Quevedo
For geographically separated cyber-physical systems, state estimation at a remote monitoring or control site is important to ensure stability and reliability of the system. Often f…