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Virtual sensors: an industrial application for illumination attributes based on machine learning techniques

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Abstract

Nowadays, the Internet of Things is a technology used in a wide range of applications, empowering fields such as the smart city, smart transportation, and the manufacturing industry. Their growing number increases the demand for robust and intelligent solutions providing relevant data, while relying on as few resources as possible. To this end, it is essential to search for techniques and solutions that can achieve a high level of quality of service, using the least hardware and cost. Machine learning can tackle the challenge by generating virtual data. It aims on replicating a sensor’s activity by utilizing real data from a subset of sensors. Such a task could be difficult with existing means, while the proposed approach might reduce it to a trivial calculation. In a similar fashion, it is possible to use simulation models for data analysis and model validation, by feeding the existing simulation models with varying conditions and comparing the results with the real ones. The current work aims to utilize the virtual IoT paradigm, in order to immerse and test everyday applications in realistic conditions and constraints. Finally, a prototype’s implementation in real-life use cases is discussed, such as the illumination in an industrial environment.

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Correspondence to Michalis Drakoulelis.

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A preliminary version of this paper appeared in [8].

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Drakoulelis, M., Filios, G., Ninos, V.G. et al. Virtual sensors: an industrial application for illumination attributes based on machine learning techniques. Ann. Telecommun. 76, 529–535 (2021). https://doi.org/10.1007/s12243-021-00856-w

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