Elastic IoT Ontologies for Industry 4.0: Methodological Approach and Hybrid Architecture

Industry 4.0 requires IoT ontologies that are interoperable, scalable, and adaptive in non-stationary industrial environments. This study combines methodological ontology optimization with a hybrid elastic framework for dynamic semantic updates and feedback-driven refinement. The methodological component systematizes literature and industrial practices to identify structural gaps and derive practical requirements. The engineering component integrates truth-table-based data structuring, vector–matrix automata for real-time classification and clustering, and in-memory event processing for low-latency operation. Experimental evaluation across no-drift, abrupt-drift, gradual-drift, and cyclic-drift scenarios shows a trade-off between semantic proximity and operational robustness: the rule-based approach reaches lower semantic distance in drift regimes, while the hybrid approach delivers higher stability and fewer false alarms in cyclic dynamics. All tested configurations preserve sub-millisecond processing latency, supporting edge/fog deployment. The results indicate that combining methodological analysis with elastic architecture is a practical pathway from static to adaptive IoT ontologies and a relevant step toward human-centric Industry 5.0 systems.