Abstract
Abstract
This thesis introduces a comprehensive microservice-based architecture tailored for the complexities of the Industrial Internet of Things (IIoT), with a focus on the metrology, in particular, calibration industry. The proposed architecture enhances the Internet of Measurement Things (IoMT) framework to effectively manage the digital transformation challenges encountered in metrology. A pivotal characteristic of the proposed architecture is the incorporation of methodological variability management mechanism, implemented through Textual Variability Model (TVM) integrated with a customized Continuous Integration/Continuous Deployment (CI/CD) pipeline. This approach ensures real-time adaptability, enabling seamless updates and continuous operation tailored to specific system requirements. The architecture's effectiveness is demonstrated through a detailed case study focusing on RF power measurement, showcasing its ability to manage the entire calibration process, from device communication to the generation of Digital Calibration Certificates (DCCs). Advanced uncertainty quantification techniques, including the Law of Propagation (LoP) and Monte Carlo Simulation (MCS), are embedded within the microservices, ensuring precision and reliability in calibration tasks. This thesis contributes to the broader field by illustrating how microservice-based architectures, enhanced with variability management and CI/CD mechanisms, can streamline processes, improve scalability, and support the evolving needs of complex, heterogeneous systems across various industries.