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Open AccessArticle

Streaming Data Fusion for the Internet of Things

Artificial Intelligence Lab, Jozef Stefan Institute, 1000 Ljubljana, Slovenia
Jozef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
Author to whom correspondence should be addressed.
Sensors 2019, 19(8), 1955;
Received: 31 March 2019 / Revised: 21 April 2019 / Accepted: 22 April 2019 / Published: 25 April 2019
(This article belongs to the Special Issue Intelligent Signal Processing, Data Science and the IoT World)
PDF [1041 KB, uploaded 26 April 2019]


To achieve the full analytical potential of the streaming data from the internet of things, the interconnection of various data sources is needed. By definition, those sources are heterogeneous and their integration is not a trivial task. A common approach to exploit streaming sensor data potential is to use machine learning techniques for predictive analytics in a way that is agnostic to the domain knowledge. Such an approach can be easily integrated in various use cases. In this paper, we propose a novel framework for data fusion of a set of heterogeneous data streams. The proposed framework enriches streaming sensor data with the contextual and historical information relevant for describing the underlying processes. The final result of the framework is a feature vector, ready to be used in a machine learning algorithm. The framework has been applied to a cloud and to an edge device. In the latter case, incremental learning capabilities have been demonstrated. The reported results illustrate a significant improvement of data-driven models, applied to sensor streams. Beside higher accuracy of the models the platform offers easy setup and thus fast prototyping capabilities in real-world applications. View Full-Text
Keywords: data fusion; stream mining; machine learning; incremental learning; time-series analysis data fusion; stream mining; machine learning; incremental learning; time-series analysis

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Kenda, K.; Kažič, B.; Novak, E.; Mladenić, D. Streaming Data Fusion for the Internet of Things. Sensors 2019, 19, 1955.

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