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001027779 005__ 20240712112902.0
001027779 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-04085
001027779 037__ $$aFZJ-2024-04085
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001027779 1001_ $$0P:(DE-HGF)0$$aVedurmudi$$b0$$eCorresponding author
001027779 1112_ $$aXXIV IMEKO World Congress "Think Metrology"$$cHamburg$$d2024-07-26 - 2024-08-29$$gIMEKO2024$$wGermany
001027779 245__ $$aAutomation in Sensor Network Metrology: an Overview of Methods and their Implementations
001027779 260__ $$c2024
001027779 29510 $$aAUTOMATION IN SENSOR NETWORK METROLOGY: AN OVERVIEW OF  METHODS AND THEIR IMPLEMENTATIONS
001027779 300__ $$a1-8
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001027779 520__ $$aSensor 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.
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