An Internet of Things-network (IoT-network) allows for the communication of data both within the network and to data hubs. However, the usefulness of the data depends on its ability to be interpreted correctly. For metrology data, effective use of the data is only possible if the numerical value, associated unit and uncertainty, expressed in a standard format, are also available. In order to develop, provide and distribute a formal framework for the transmission of metrology data on the basis of the International System of Units, European project EMPIR 17IND02 SmartCom was agreed between the European Commission and the European Association of National Metrology Institutes (Euramet). The SmartCom project aims to provide the methodological and technical foundation for the unambiguous, universal, safe and uniform communication of metrological smart data in the IoT and Industry 4.0. The project will increase the industrial capabilities and the provision of regulations for data exchange in the IoT. It will also assist countries within the European Union (EU) and those with an association agreement with the EU in developing products that are able to communicate in IoT environments worldwide. In addition to describing the general ideas and aims of the project, this article presents the research results achieved in the first midterm period.

IoT systems based on collaborative sensor networks are becoming increasingly common in various industries owing to the increased availability of low-cost sensors. The quality of the data provided by these sensors may be unknown. For these reasons, advanced data processing and sensor network self-calibration methods have become popular research topics. In terms of metrology, the self-calibration methods lack the traceability to the established measurement standards of National Metrology Institutes (NMIs) through an unbroken chain-link of calibration. This problem can be solved by the ongoing digitalization of the metrology infrastructure. We propose a conceptual solution based on Digital Calibration Certificates (DCCs), Digital SI (D-SI), and cryptographic digital identifiers, for validation of data quality and trustworthiness. The data that enable validation and traceability can be used to improve analytics, decision-making, and security in industrial applications. We discuss the applicability and benefits of our solutions in a selection of industrial use cases, where collaborative sensing has already been introduced. We present the remaining challenges in the digitization and standardization processes regarding digital metrology and the future work required to address them.

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In 1999, when NASA's Mars Climate Orbiter missed its intended orbit and burned up in the Martian atmosphere, the media had a heyday over the reason: one team had used metric units in its thrust calculations, another, imperial. The navigation software that exchanged this information lacked a built-in process to check units. So when one team's software produced data in imperial units rather than the expected metric ones, the spacecraft was set on the wrong trajectory. The result was the loss of five years of effort and hundreds of millions of taxpayers' dollars.Two decades on, such problems persist. Researchers across fields often assume that their colleagues understand details without specifying them, and are therefore remiss when documenting units. Sometimes they leave them out entirely, provide ones that have multiple definitions or use units of convenience that have never been formally recognized.Humans struggle to interpret numbers with sloppy or missing units, and it is much more difficult when computers are involved. Most software packages, data-management tools and programming languages lack built-in support for associating units with numeric data (with the exception of the language F#). This means that information is essentially stored and managed as 'unitless' values. Disciplines including bioscience and aerospace engineering have adopted conventions for unit representation, such as the Unified Code for Units of Measure (UCUM) and the Quantities, Units, Dimensions, and Types (QUDT) Ontology. But there are no broadly agreed technical specifications for how to represent quantities and their associated units without confusing machines.There have been many calls in recent years to make data sets FAIR (Findable, Accessible, Interoperable and Reusable;