Report

FZJ-2026-01377

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Guidelines for data quality, measurement uncertainty, and traceability in sensor networks

Johansen ; Vaa, M. 1. O. i. (Contact person) ; Tabandeh, M. ; Söderblom, S. O. i. ; Harris, H. ; Pearce, P. ; Luo, J. O. i. ; Tucker, Y. ; Vedurmudi, D. O. i. ; Iturrate-Garcia, A. P. O. i. ; Zaidan, M. O. i. ; Davidovic, M. A. O. i. ; Holtwerth, A. ; Stock, J.FZJ* ; Xhonneux, A.FZJ* ; Kok, A. O. i. ; Dijk, G. O. i. v. ; Pires, M. ; Sousa, C. ; , , João A.

2025

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Please use a persistent id in citations: doi:10.34734/FZJ-2026-01377

Report No.: EPM 22DIT02 FunSNM

Abstract: As sensor networks become easier to acquire and deploy, and consequently are morecommon in almost all industries and in everyday life, so ensuring the trustworthiness andreliability of measurements and data in such systems becomes more challenging. Not onlyas the numbers of sensors grow, but also as the inaccessibility of sensors means it isinfeasible to use established methods for their calibration, so the difficulties of assessingmeasurement uncertainty in sensor networks and establishing the traceability ofmeasurements made by such systems increases. Furthermore, due to the large volumes ofdata, it is a challenge to validate the quality of data collected from sensor networks, and it isinfeasible to do so without automated, efficient, and reliable methods.The purpose of this guide is to help address the challenge of ensuring data quality for sensornetworks. It is structured in two main parts, one related to data quality metrics and one totraceability.The part on data quality metrics (Section 2) provides guidance on the importance of dataquality when collecting large amounts of data from sensor networks where there is lesscontrol over the sensor environment as well as the management and architecture of thesensor network, for example, compared to a laboratory setup. This includes choosing whichdimensions of data quality are most important depending on the use case, managing datarequirements during the lifecycle of sensor nodes, and developing ways to measure andquantify data quality.Different use cases have different metrological needs when it comes to traceability. The parton traceability (Section 3) addresses SI-traceability in sensor networks, providing guidanceon different ways of calibrating sensors in sensor networks such as in-situ, self- and co-calibration. Furthermore, it addresses the challenge of making methods of analyzing sensordata uncertainty-aware, for example, for sensor fusion, and using different modellingtechniques, for example, digital shadows and digital twins.Different use cases are used as examples in different sections of the guide. The use casesare district heating networks, heat treatment of high-value components in advancedmanufacturing, gas flow meter networks, air quality monitoring sensor networks and smartbuildings. These are used to highlight certain challenges, needs, and both commonalities anddifferences in certain types of sensor networks within the different subjects covered in theguide.

Keyword(s): Energy (1st) ; Others (2nd)

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Contributing Institute(s):

1. Modellierung von Energiesystemen (IEK-10)

Research Program(s):

1. 1121 - Digitalization and Systems Technology for Flexibility Solutions (POF4-112) (POF4-112)

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