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M. Meiyi, E. Bartocci, J. Stankovic, L. Feng:
"Predictive monitoring with uncertainty for deep learning enabled smart cities: poster abstract";
Vortrag: SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems, Virtual Event Japan; 16.11.2020 - 19.11.2020; in: "SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems", (2020), S. 711 - 712.

In order to prevent safety violations, predictive monitoring with uncertainty is crucial for deep learning-enabled services in smart cities. We develop a novel predictive monitoring system for smart city applications, which consists of an RNN-based predictor with uncertainty estimation and a new specification language, named Signal Temporal Logic with Uncertainty. The solution first predicts a sequence of distributions representing city's future states with uncertainty estimation and then checks the predicted results against STL-U specified safety and performance requirements. The system supports decision making by providing a quantitative satisfaction degree with confidence guarantees. We receive promising results from evaluations on two large-scale city datasets, and on a case study on real-time predictive monitoring in a simulated smart city.

http://dx.doi.org/10.1145/3384419.3430445