Abstract
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.
Highlights
The use of collaborative sensor networks in industrial applications is increasing
From a metrological point of view, fully decentralized systems can be considered as somewhat problematic, because in metrology the comparability of measurement results is made possible by traceability to measurement standards, which is strongly based on a hierarchical infrastructure
BIPM defines calibration as an operation that under specified conditions establishes a relation between the quantity values with measurement uncertainties provided by a measurement standard and corresponding indications, with associated measurement uncertainties, and uses this information to establish a relation for obtaining a measurement result from an indication [8]
Summary
The use of collaborative sensor networks in industrial applications is increasing. As an example, a typical application area has been monitoring environmental conditions and air quality by measuring the concentrations of gases, temperature, humidity, or pressure [1]. The main problem with self-calibrating sensors in uncontrolled environments is that the calibration chains of the sensors and the traceability of the measurement results can become excessively indistinctive over time, which will lead to increasing measurement uncertainties. These kind of calibrations are poorly applicable for compensating errors caused by the aging of the sensors, as it is likely that similar sensors will be affected by aging.
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