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 data exchange. Existing IoT ecosystems often operate using diverse data models, communication protocols, and semantic representations, resulting in fragmented systems that hinder integration. To address this problem, we propose a unified framework that employs machine learning-based ontology alignment for standardized, adaptive IoT integration. The hypothesis guiding this research is that combining semantic modeling with intelligent optimization techniques can significantly improve the consistency and efficiency of data exchange across heterogeneous IoT environments. The proposed framework integrates real-time data stream processing, semantic similarity analysis, and adaptive ontology mapping to dynamically align device ontologies. Using simulated and real-world environments, including smart homes and healthcare systems, the framework was tested against key performance metrics such as accuracy, latency, and interoperability rate. Results demonstrate that the proposed method achieves a high ontology alignment accuracy of 97%, reduces latency to under 20 ms, and maintains over 95% interoperability among diverse device types. The findings confirm that the integration of machine learning algorithms with semantic modeling significantly enhances the performance, scalability, and adaptability of IoT systems. The framework successfully addresses semantic inconsistencies and supports dynamic device onboarding without manual intervention. This study presents a robust and scalable solution for IoT interoperability, offering real-time, intelligent ontology alignment that is adaptable to evolving devices and data standards. This work contributes to the development of next-generation IoT architectures capable of supporting standardized, efficient, and automated communication across diverse applications.