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@TECHREPORT{Johansen:1053035,
      author       = {Johansen and Tabandeh, Michael1 and Söderblom, Shahin2
                      ORCID icon and Harris, Henrik2 and Pearce, Peter3 and Luo,
                      Jonathan3 ORCID icon and Tucker, Yuhui3 and Vedurmudi,
                      Declan3 ORCID icon and Iturrate-Garcia, Anupam Prasad4 ORCID
                      icon and Zaidan, Maitane5 ORCID icon and Davidovic, Martha
                      Arbayani6 ORCID icon and Holtwerth, Alexander and Stock, Jan
                      and Xhonneux, André and Kok, André8 ORCID icon and Dijk,
                      Gertjan ORCID icon van and Pires, Marcel9 and Sousa,
                      Carlos10 and , , João A.},
      othercontributors = {Vaa, Mads 1 ORCID icon},
      title        = {{G}uidelines for data quality, measurement uncertainty, and
                      traceability in sensor networks},
      number       = {EPM 22DIT02 FunSNM},
      reportid     = {FZJ-2026-01377, EPM 22DIT02 FunSNM},
      pages        = {68 p.},
      year         = {2025},
      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.},
      cin          = {IEK-10},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {1121 - Digitalization and Systems Technology for
                      Flexibility Solutions (POF4-112)},
      pid          = {G:(DE-HGF)POF4-1121},
      typ          = {PUB:(DE-HGF)29},
      doi          = {10.34734/FZJ-2026-01377},
      url          = {https://juser.fz-juelich.de/record/1053035},
}