Design of an improved validation model for semantic interoperability in IoT using MOVFRGD ECDTSA and CCEAICM process

Abstract Despite numerous initiatives aimed at enhancing the Semantic Web and IoT interoperability, semantic interoperability continues to be a primary barrier in large-scale IoT deployments; as disparate devices, data formats, and contextual representations must exchange meaningful information. The proposed solution will address this issue by developing a unified deep learning framework for integrating representation learning, temporal reasoning, causal alignment, and distributed consensus for maintaining a consistent semantic representation of information across dynamically changing environments. Unlike existing approaches that have relied on static ontology alignments, the proposed framework continually adapts to changes in the context (e.g., device updates, new terms), to maintain a robust and scalable level of cross-system communication and knowledge transfer in process. Using multi-ontology benchmarks and real-world sensor datasets, we empirically evaluate the effectiveness of our proposed approach and demonstrate significant performance improvements in key interoperability tasks. Specifically, the proposed framework achieves a semantic alignment accuracy of 92.5% between two heterogeneous ontologies, a drift detection accuracy of 89.4% in detecting changes in temporal sensor streams, and a federated semantic agreement rate of 94.1% across distributed nodes without requiring the sharing of raw data samples. These results demonstrate strong performance in maintaining a consistent semantic representation of information over time, across differing contexts, and in accordance with privacy constraints. The federated agreement metric is a novel measure used in this paper to quantify the proportion of nodes that converge to a common semantic interpretation following iterative updates, thus providing a means for assessing the degree of semantic coherence at the system level sets. The observed performance enhancements derive from combining several mechanisms, as opposed to optimizing a single strategy. The context-aware embeddings provide accurate alignment of semantic representations at the outset, while the temporal modeling preserves the stability of the semantic representation as the meanings of the data evolve, causal mapping provides a mechanism for transferring knowledge across environments, and the decentralized learning mechanisms support scalability in distributed IoT deployments. Collectively, these mechanisms provide a coherent semantic layer upon which reliable reasoning and decision-making processes can operate in dynamic IoT environments.