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| 001 | 1027779 | ||
| 005 | 20240712112902.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2024-04085 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-04085 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Vedurmudi |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a XXIV IMEKO World Congress "Think Metrology" |g IMEKO2024 |c Hamburg |d 2024-07-26 - 2024-08-29 |w Germany |
| 245 | _ | _ | |a Automation in Sensor Network Metrology: an Overview of Methods and their Implementations |
| 260 | _ | _ | |c 2024 |
| 295 | 1 | 0 | |a AUTOMATION IN SENSOR NETWORK METROLOGY: AN OVERVIEW OF METHODS AND THEIR IMPLEMENTATIONS |
| 300 | _ | _ | |a 1-8 |
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| 520 | _ | _ | |a 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. |
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