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Related Concept Videos

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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Related Experiment Video

Updated: Dec 6, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
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Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

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A Semantic-Based Belief Network Construction Approach in IoT.

Yuji Dong1, Kaiyu Wan2, Yong Yue2

  • 1Department of Computer Science and Media Technology, Malmö University, 20506 Malmö, Sweden.

Sensors (Basel, Switzerland)
|October 14, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a semantic-based approach for Internet of Things (IoT) systems to manage uncertainty. It enhances data fusion accuracy and enables automatic fault detection in IoT networks.

Keywords:
beliefdata fusionfault detectioninternet of thingsself adaptationuncertainty

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Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
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Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background:

  • Complex systems, particularly those interacting with the physical environment, inherently involve uncertainty.
  • Effective management of uncertainty is crucial for the reliability and functionality of Internet of Things (IoT) systems.

Purpose of the Study:

  • To propose a novel semantic-based approach for constructing belief networks in IoT systems.
  • To address the challenge of handling uncertainty within IoT environments through enhanced data fusion and fault detection.

Main Methods:

  • Defined a belief property for each system component within the IoT network.
  • Developed algorithms for updating belief values, enabling semantic matching for data consistency.
  • Implemented mechanisms for data fusion and fault detection leveraging the defined belief properties.

Main Results:

  • The proposed approach successfully fuses more accurate data from potentially inaccurate IoT devices.
  • Automatic detection of faults within individual nodes of the IoT system was achieved.
  • Simulation experiments validated the effectiveness and expected performance of the semantic-based belief network.

Conclusions:

  • The semantic-based belief network approach effectively handles uncertainty in IoT systems.
  • The method improves data accuracy through fusion and enhances system reliability via fault detection.
  • This approach provides a robust framework for managing complex and uncertain IoT environments.