<p class="jove_content">This study presents a machine learning-based framework for real-time IoT ontology alignment, enabling seamless data exchange across heterogeneous systems. By integrating semantic modeling and adaptive optimization, the approach enhances interoperability, reduces latency, and achieves high accuracy. Validated in real-world settings, it offers a scalable, standardized IoT integration solution.</p>

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Toward Standardized IoT Ontologies Using a Machine Learning-Based Framework for Seamless Data Exchange

The increasing heterogeneity of Internet of Things (IoT) devices has led to significant challenges in achieving real-time interoperability and seamless ...

Institution - J. C. Bose University of Science & Technology, YMCA Faridabad

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