PReDiCt: A Scenario-based Methodology for Realizing Decision-Making Mechanisms Targeting Cyber-Physical Systems

As systems continue to evolve they rely less on human decision-making and more on computational intelligence. This trend in conjunction to the available technologies for providing advanced sensing, measurement, process control, and communication lead towards the new field of Cyber-Physical System (CPS). Due to superior performance of these systems it is expected to play a major role in the design and development of future engineering platforms with new capabilities that far exceed today’s levels of autonomy, functionality and usability. Although CPS exhibits remarkable characteristics, their design and implementation is a challenging issue, as numerous (heterogeneous) components and services have to be appropriately orchestrated together. The problem of designing efficient decision making mechanisms becomes far more challenging in case the target system has to meet also real-time constraints. Throughout this manuscript, we introduce a novel framework to support the efficient, yet flexible, rapid prototyping of decision-making mechanisms for CPSs. Furthermore, a new toolbox that automates the tasks of our framework was also developed as an open-source software. For evaluation purposes, the proposed solution was applied to a building use case in order to support optimum configuration of smart thermostats. Based on our analysis, we depict that the introduced approach maximizes thermal comfort metric 130with the minimum possible energy cost, as compared to existing state-of-the-art solutions. Additionally, the proposed decision-making mechanism exhibits remarkable lower computational and storage complexity, as compared to available control mechanisms, making it ideal for being executed onto low-cost embedded devices.