Physics-Aware Mixed-Criticality Systems Design via End-to-End Verification of CPS

Autonomous systems are heavily used in many safety-critical systems, such as industrial automation, autonomous cars, Industrial Internet of Things (I-IoT), etc. Verification of the functional and temporal correctness of such systems is necessary before deployment to ensure their safety. However, due to the presence of physical systems in the continuous-time domain and computational models in the discrete-time domain, end-to-end verification of these systems is highly challenging. Existing formal methods focus on verifying physical models assuming static or simplified computation models. In contrast, existing real-time systems focus on satisfying strict timing bounds but do not care how those bounds are obtained and how they relate to physical safety. Our approach bridges these two domains, and constitutes an end-to-end verification framework for arbitrary physical models and computational models incorporated within a cyber-physical automated system. By allowing the interaction between the computational and physical models, our verification framework enables a fine-grained scheme that verifies against the local environment instead of verifying against global worst-case assumptions. Moreover, to support locally varying worst-case scenarios, a mixed-criticality system is proposed where the system supports several critical models and switches among the modes based on environmental uncertainty. Finally, a proof-of-concept evaluation of the proposed framework is reported.