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The development and integration of metrological processes to address complex, large-scale systems of interconnected measuring instruments, i.e., sensor networks, has been a topic of increasing importance in the last decade. Initial developments in sensor network metrology include, e.g., metrological treatment of sensors with digital-only output, measurement uncertainty evaluation for time series data, and the digital representation of metrological information of such sensors. In principle, modern digital technologies allow for a fully automated operation of even rather complex sensor networks. However, the integration of metrological principles to provide confidence in the measurement results in such networks is still at its beginning. In this contribution we consider a recently published structured approach to assess digital maturity based on the level of machine-readability and machine-actionability. We apply this approach to sensor networks, define the different levels of digital maturity, and discuss potential steps for further evolving the integration of metrological principles for the Internet of Things (IoT).

Sensors are a key element of recent Industry 4.0 developments and currently further sophisticated functionality is embedded into them, leading to smart sensors. In a typical “Factory of the Future” (FoF) scenario, several smart sensors and different data acquisition units (DAQs) will be used to monitor the same process, e.g. the wear of a critical component, in this paper an electromechanical cylinder (EMC). If the use of machine learning (ML) applications is of interest, data of all sensors and DAQs need to be brought together in a consistent way. To enable quality information of the obtained ML results, decisions should also take the measurement uncertainty into account. This contribution shows an ML pipeline for time series data of calibrated Micro-Electro-Mechanical Systems (MEMS) sensors. Data from a lifetime test of an EMC from multiple DAQs is integrated by alignment, (different schemes of) interpolation and careful handling of data defects to feed an automated ML toolbox. In addition, uncertainty of the raw data is obtained from calibration information and is evaluated in all steps of the data processing pipeline. The results for the lifetime prognosis of the EMC are evaluated in the light of “fitness for purpose”.