The Internet of Things (IoT) is characterized by a large number of interconnected devices or assets. Measurement instruments in the IoT are typically digital in the sense that their indications are available only as digital output. Moreover, a growing number of IoT sensors contain a built-in pre-processing system, e.g., for compensating unwanted effects. This paper considers the application of metrological principles to such so-called “smart sensors” in the IoT. It addresses the calibration of digital sensors, mathematical and semantic approaches, the communication of data quality and the meaning of traceability for the IoT in general.

The annotation of sensor data with semantic metadata is essential to the goals of automation and interoperability in the context of Industry 4.0. In this contribution, we outline a semantic description of quality of data in sensor networks in terms of indicators, metrics and interpretations. The concepts thus defined are consolidated into an ontology that describes quality of data metainformation in heterogeneous sensor networks and methods for the determination of corresponding quality of data dimensions are outlined. By incorporating support for sensor calibration models and measurement uncertainty via a previously derived ontology, a conformity with metrological requirements for sensor data is ensured. A quality description for a calibrated sensor generated using the resulting ontology is presented in the JSON-LD format using the battery level and calibration data as quality indicators. Finally, the general applicability of the model is demonstrated using a series of competency questions.

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).

In recent years, data science and engineering have faced many challenges concerning the increasing amount of data. In order to ensure findability, accessibility, interoperability, and reusability (FAIRness) of digital resources, digital objects as a synthesis of data and metadata with persistent and unique identifiers should be used. In this context, the FAIR data principles formulate requirements that research data and, ideally, also industrial data should fulfill to make full use of them, particularly when Machine Learning or other data-driven methods are under consideration. In this contribution, the process of providing scientific data of an industrial testbed in a traceable and FAIR manner is documented as an example.

A co-calibration method suited only for (strictly) homogeneous sensor networks is applied to distributed homogeneous sensor networks. This is achieved by relying on spatial and temporal interpolation models to provide virtual reference measurement points at the location of the device under test. The interpolation method is evaluated in a simulation of an existing real-world use case dealing with room-temperature monitoring using distributed sensors.