Lifecycle-Based View on Cyber-Physical System Models Using Extended Hidden Markov Models

Abstract

Many components of Cyber-Physical Systems (CPS) are designed based on models that represent the assumed behavior of the CPS at the time of deployment. However, significant or continuous small changes in the CPS, as well as wear and tear reduce the effectiveness of the CPS and its model and may lead to a total failure of the overall system. In this paper, we propose a novel lifecycle-based view of CPS models. First, we define the model's lifespan as the period from the initial conception of the model until it is no longer fit to represent the system behavior. For better differentiation, a lifespan is divided into the initial, operation, and adaptation phases. In the initial phase, a known-good baseline performance metric is established for the model's suitability to reflect the system behavior. In the operation phase, the model is used for CPS analysis, data smoothing, and fault location while its suitability is monitored. The adaptation phase is intended for necessary adaptations to the model and to the CPS itself, which lead to new iterations. To implement these lifecycle augmentations of the CPS, we use formal modeling in the form of Hidden Markov Models extended by unobservable transitions (Є-HMMT) to represent the assumed system behavior and compare the data of the observed system behavior with this modeling. In addition, we are testing our proposed formalism by designing a CPS model based on smart home systems and running a simulation for validation. The simulation covers unforeseen system changes and corrupted data.