ABSTRACT
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.
- Yarin Gal. 2016. Uncertainty in deep learning. Ph.D. Dissertation. PhD thesis, University of Cambridge.Google Scholar
- Meiyi Ma, Sarah M Preum, Mohsin Ahmed, William Tärneberg, Abdeltawab Hendawi, and John Stankovic. 2019. Data sets, modeling, and decision making in smart cities: A survey. ACM Transactions on Cyber-Physical Systems 4, 2 (2019), 1--28.Google ScholarDigital Library
- Meiyi Ma, John A Stankovic, and Lu Feng. 2018. Cityresolver: a decision support system for conflict resolution in smart cities. In Proceedings of the 9th ACM/IEEE International Conference on Cyber-Physical Systems. IEEE Press, 55--64.Google ScholarDigital Library
Index Terms
- Predictive monitoring with uncertainty for deep learning enabled smart cities: poster abstract
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