Streaming Data Fusion for the Internet of Things

• Main Authors: Klemen Kenda, Blaž Kažič, Erik Novak, Dunja Mladenić
• Format: Article
• Language: English
• Published: MDPI AG 2019-04-01
• Series: Sensors
• Subjects: data fusion; stream mining; machine learning; incremental learning; time-series analysis
• Online Access: https://www.mdpi.com/1424-8220/19/8/1955

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.