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Related Experiment Video

Updated: Jan 25, 2026

Data Communication Based on MQTT in a Polymer Extrusion Process
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Streaming Data Fusion for the Internet of Things.

Klemen Kenda1,2, Blaž Kažič3,4, Erik Novak5,6

  • 1Artificial Intelligence Lab, Jozef Stefan Institute, 1000 Ljubljana, Slovenia. klemen.kenda@ijs.si.

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|April 28, 2019
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Summary

This study introduces a new data fusion framework for integrating diverse internet of things (IoT) data streams. The framework enhances machine learning models for predictive analytics, improving accuracy and enabling rapid prototyping.

Keywords:
data fusionincremental learningmachine learningstream miningtime-series analysis

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Area of Science:

  • Data Science
  • Machine Learning
  • Internet of Things (IoT)

Background:

  • Integrating heterogeneous data streams from the Internet of Things (IoT) is crucial for unlocking their full analytical potential.
  • Existing approaches often struggle with the complexity of diverse data sources and require domain-specific knowledge.
  • Machine learning for predictive analytics offers a domain-agnostic approach but needs effective data integration.

Purpose of the Study:

  • To propose a novel framework for data fusion of heterogeneous data streams.
  • To enrich streaming sensor data with contextual and historical information.
  • To prepare feature vectors for machine learning algorithms.

Main Methods:

  • Developed a data fusion framework to integrate diverse data streams.
  • Enriched streaming sensor data with relevant contextual and historical information.
  • Applied the framework to both cloud and edge computing environments, demonstrating incremental learning on edge devices.

Main Results:

  • The framework successfully generated feature vectors suitable for machine learning.
  • Significant improvements in data-driven models applied to sensor streams were observed.
  • Demonstrated higher accuracy in predictive models compared to baseline approaches.

Conclusions:

  • The proposed framework effectively fuses heterogeneous data streams for enhanced machine learning.
  • The system offers easy setup and fast prototyping for real-world IoT applications.
  • Achieved improved model accuracy and demonstrated incremental learning capabilities on edge devices.