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.

Charge amplifier calibration at low frequencies is a time-consuming service. This paper presents a method to speed up the process by measuring decades of frequencies in a single run. Based on the calibration results gained, a filter design process is introduced that facilitates a multi-rate approach to digital IIR filters in order to circumvent the complications arising from high-pass filtering with low corner frequency at high sampling rates.

Smart sensors are the metrological challenge of the current trend towards digitization and "Internet of Things" (IoT). In particular for use in dynamic measurement applications, these cross-linked devices could help to provide more accurate and reliable data for the user while at the same time relieving the burden of complex signal processing. This paper presents a concept for a smart sensor in metrological terms. It is a forecast of the development that is to be implemented in an approved joint European research project which is about to start in the summer of 2018.

Recently several European National Measurement Institutes have established traceable calibration methods for dynamic mechanical quantities, e.g., dynamic force, torque and pressure. However, the use in industry and elsewhere of dynamic calibration information provided on certificates is not straightforward. Typically it is necessary to employ deconvolution techniques to obtain estimates of measurands, and the deconvolution method itself and the associated algorithms are sources of uncertainty that must be included in uncertainty budgets. There is a need for practical guidance for end users on how to use the newly-available dynamic calibration information. To this end we set out an approach to the evaluation of uncertainties associated with dynamic measurements that we believe covers the most relevant cases. The methods have been embodied in publicly-available software and we show how they can be used to tackle some example problems. We believe that the methods lead to more reliable estimates of the relevant measurands and their associated uncertainties.