Name
Abstract | ||
|---|---|---|
Predictive monitoring-making predictions about future states and monitoring if the predicted states satisfy requirements-offers a promising paradigm in supporting the decision making of Cyber-Physical Systems (CPS). Existing works of predictive monitoring mostly focus on monitoring individual predictions rather than sequential predictions. We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our study of real-world CPS datasets. We propose a new logic named Signal Temporal Logic with Uncertainty (STL-U) to monitor a flowpipe containing an infinite set of uncertain sequences predicted by Bayesian RNNs. We define STL-U strong and weak satisfaction semantics based on whether all or some sequences contained in a flowpipe satisfy the requirement. We also develop methods to compute the range of confidence levels under which a flowpipe is guaranteed to strongly (weakly) satisfy an STL-U formula. Furthermore, we develop novel criteria that leverage STL-U monitoring results to calibrate the uncertainty estimation in Bayesian RNNs. Finally, we evaluate the proposed approach via experiments with real-world CPS datasets and a simulated smart city case study, which show very encouraging results of STL-U based predictive monitoring approach outperforming baselines. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3477032 | ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS |
Keywords | DocType | Volume |
Predictive monitoring, uncertainty | Journal | 20 |
Issue | ISSN | Citations |
5 | 1539-9087 | 3 |
PageRank | References | Authors |
0.46 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meiyi Ma | 1 | 39 | 9.12 |
| Stankavic, J. | 2 | 13705 | 1386.42 |
| Ezio Bartocci | 3 | 733 | 57.55 |
| Feng Lu | 4 | 139 | 22.18 |