Streaming Data Fusion for the Internet of Things

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...

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Main Authors: Klemen Kenda, Blaž Kažič, Erik Novak, Dunja Mladenić
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/19/8/1955
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author Klemen Kenda
Blaž Kažič
Erik Novak
Dunja Mladenić
author_facet Klemen Kenda
Blaž Kažič
Erik Novak
Dunja Mladenić
author_sort Klemen Kenda
collection DOAJ
description 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.
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spelling doaj.art-fa047ce67f4b48dd9278b7a8b65fa0d32022-12-22T02:22:10ZengMDPI AGSensors1424-82202019-04-01198195510.3390/s19081955s19081955Streaming Data Fusion for the Internet of ThingsKlemen Kenda0Blaž Kažič1Erik Novak2Dunja Mladenić3Artificial Intelligence Lab, Jozef Stefan Institute, 1000 Ljubljana, SloveniaArtificial Intelligence Lab, Jozef Stefan Institute, 1000 Ljubljana, SloveniaArtificial Intelligence Lab, Jozef Stefan Institute, 1000 Ljubljana, SloveniaArtificial Intelligence Lab, Jozef Stefan Institute, 1000 Ljubljana, SloveniaTo 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.https://www.mdpi.com/1424-8220/19/8/1955data fusionstream miningmachine learningincremental learningtime-series analysis
spellingShingle Klemen Kenda
Blaž Kažič
Erik Novak
Dunja Mladenić
Streaming Data Fusion for the Internet of Things
Sensors
data fusion
stream mining
machine learning
incremental learning
time-series analysis
title Streaming Data Fusion for the Internet of Things
title_full Streaming Data Fusion for the Internet of Things
title_fullStr Streaming Data Fusion for the Internet of Things
title_full_unstemmed Streaming Data Fusion for the Internet of Things
title_short Streaming Data Fusion for the Internet of Things
title_sort streaming data fusion for the internet of things
topic data fusion
stream mining
machine learning
incremental learning
time-series analysis
url https://www.mdpi.com/1424-8220/19/8/1955
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