Harris, Peter; Østergaard, P F; Tabandeh, S; Söderblom, H; Kok, G; van Dijk, M; Luo, Y; Pearce, J; Tucker, D; Vedurmudi, A P; Iturrate-Garcia, M (2025) Measurement Uncertainty Evaluation for Sensor Network Metrology. Metrology, 5 (1). 3 ISSN 2673-8244

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Abstract

Sensor networks, which are increasingly being used in a broad range of applications, constitute a measurement paradigm involving ensembles of sensors measuring possibly different quantities at a discrete sample of spatial locations and temporal points outside the laboratory. If sensor networks are to be considered as true metrology systems and the measurement results derived from them used for decision-making, such as in a regulatory context, it is important that the results are accompanied by reliable statements of measurement uncertainty. This paper gives a preview of some of the work undertaken within the European-funded ‘Fundamental principles of sensor network metrology (FunSNM)’ project to address the challenges of measurement uncertainty evaluation in some real-world sensor network applications. The applications demonstrate that sensor networks possess features related to the nature of the measured quantities, to the nature of the measurement model, and to the nature of the measured data. These features make conventional methods of measurement uncertainty evaluation, and established guidelines for measurement uncertainty evaluation difficult to apply. An overview of some of the modelling tools used to address the challenges of measurement uncertainty evaluation in those applications is given.

Item Type:	Article
Keywords:	sensor network; measurement uncertainty; Kalman filter; Laplace transform; Gaussian process model; inverse problem
Subjects:	Mathematics and Scientific Computing > Measurement Uncertainties
Divisions:	Data Science
Identification number/DOI:	10.3390/metrology5010003
Last Modified:	11 May 2026 09:55
URI:	https://eprintspublications.npl.co.uk/id/eprint/10383