Decision-Making Support for Data Integration in Cyber-Physical-System Architectures

Cyber-Physical Systems (CPS) design is a complex challenge involving physical and digital components working together to accomplish a specific goal. Integrating such systems involves combining data from various distributed Internet of Things (IoT) devices and cloud services to create meaningful insights and actions. Service-based IoT data integration involves several steps: collection, processing, analysis, and visualization. Adopting a holistic approach that considers physical and digital aspects is crucial when designing data integration in distributed CPS. Architectural design decisions are vital in shaping a CPS' functionality and system qualities, such as performance, security, and reliability. Although several patterns and practices for CPS architecture have been proposed, much of the knowledge in this area is informally discussed in the grey literature, e.g., in practitioner blogs and system documentation. As a result, this architectural knowledge is dispersed across many sources that are often inconsistent and based on personal experience. In this study, we present the results of a qualitative, in-depth study of the best practices and patterns of distributed CPS architecture as described by practitioners. We have developed a formal architecture decision model using a model-based qualitative research method. We aim to bridge the science-practice gap, enhance comprehension of practitioners' CPS approaches, and provide decision-making support.