Machine-to-machine systems for data acquisition and measurement with self-validation for the digital metrology transition

This paper introduces a methodological framework for developing an autonomous system that minimizes human involvement within a measurement chain. The approach integrates data acquisition, metadata retrieval, and processing within a Machine-to-Machine (M2M) communication paradigm, thereby substantially minimizing manual operations throughout the workflow. To safeguard data integrity and detect unintended corruption during M2M exchanges, a data self-validation mechanism is introduced. Aligned with the Industry 5.0 vision—which emphasizes advanced data management and intelligent machine communication—the proposed approach contributes to the ongoing digital transformation of metrology, namely, through the implementation of Digital Calibration Certificates (DCC), promoting seamless and automated exchange of measurement data. The system was successfully applied to an experimental remote meteorological station, enabling fully autonomous measurement, validation, and data storage using DCC.