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This study introduces a machine learning framework for unified Internet of Things (IoT) integration, enhancing real-time data exchange and interoperability across diverse devices through intelligent ontology alignment.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Internet of Things

Background:

  • Heterogeneous Internet of Things (IoT) devices present challenges in real-time interoperability and data exchange due to diverse data models and protocols.
  • Existing IoT ecosystems are often fragmented, hindering seamless integration and communication.

Purpose of the Study:

  • To propose a unified framework using machine learning-based ontology alignment for standardized and adaptive IoT integration.
  • To improve consistency and efficiency of data exchange in heterogeneous IoT environments.

Main Methods:

  • Developed a framework integrating real-time data stream processing, semantic similarity analysis, and adaptive ontology mapping.
  • Employed machine learning for dynamic ontology alignment across diverse IoT devices.
  • Tested the framework in simulated and real-world environments (smart homes, healthcare).

Main Results:

  • Achieved 97% ontology alignment accuracy.
  • Reduced data exchange latency to under 20 ms.
  • Maintained over 95% interoperability among diverse IoT device types.

Conclusions:

  • The integration of machine learning with semantic modeling significantly enhances IoT system performance, scalability, and adaptability.
  • The framework provides a robust, scalable solution for real-time, intelligent, and automated IoT interoperability.
  • Successfully addresses semantic inconsistencies and supports dynamic device onboarding without manual intervention.