Contract-Based Runtime Monitoring for a Smart House Digital Twin

Digital Twins (DTs) are increasingly used to support monitoring, prediction, and decision-making in cyber-physical systems. However, ensuring the reliability of DT-based systems remains challenging, particularly when data-driven models are used for control and prediction. The D-RODS project addresses this challenge by developing a framework for dynamic and robust distributed DTs that integrates artificial intelligence with verification and validation techniques. In this work-in-progress paper, we present a prototype that applies the D-RODS framework to a smart house scenario. The system maintains the temperature of a physical environment within recommended limits, while enabling monitoring and automated control through a neural network DT implemented in MATLAB, and connected to the physical system through a supervisory application. The application incorporates a contract-based observer that verifies input signals and controller decisions at runtime. When violations are detected, control is switched from the physical controller to the DT controller. Initial experiments show that the DT controller closely follows the behavior of the physical controller, while the temperature prediction model exhibits deviations in some scenarios.