Automation in sensor network metrology: An overview of methods and their implementations

Anupam Prasad Vedurmudi*, Kruno Miličević, Gertjan Kok, Bang Xiang Yong, Liming Xu, Ge Zheng, Alexandra Brintrup, Maximilian Gruber, Shahin Tabandeh, Martha Arbayani Zaidan, André Xhonneux, Jonathan Pearce

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Sensor networks are an integral component of the ongoing automation of industrial processes in a diverse range of sectors. As sensors and, by extension, sensor networks provide information about physical quantities in the form of measurements, the development and adaptation of metrological practices that ensure the reliability, accuracy, and traceability of the data thus generated is essential. A complementary development of tools for the implementation of metrological methods is necessary. In this contribution we present a review of the tools and methods relevant to the automated application of metrological practices to large-scale transient sensor networks with an emphasis on uncertainty aware soft- and middleware, data fusion and machine learning. In this review, we will discuss the state-of-the-art with respect to general metrological methods and specific soft- and middleware tools and motivate future developments in sensor network metrology.

Original languageEnglish
Article number101799
JournalMeasurement: Sensors
DOIs
Publication statusAccepted/In press - 2025
MoE publication typeA1 Journal article-refereed

Funding

The project (22DIT02 FUNSNM) has received funding from the European partnership on metrology, co-financed from the European union’s Horizon Europe Research and Innovation Programme and by the participating states.

Keywords

  • Agent-based systems
  • Data fusion
  • Machine learning
  • Metrology
  • Sensor networks

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