This study presents OntOraculum, an ontology designed to organize and analyze performance metrics in smart environments. It categorizes metrics into Hardware, Network, Software, Energy, and SecurityAndReliability domains, defining properties such as dependencies, impacts, and trends to support predictive analysis and dynamic adjustments. Using Protégé for modeling, Semantic Web Rule Language (SWRL) rules facilitate automated inferences, while SPARQL Protocol and Resource Description Framework (RDF) Query Language (SPARQL) queries enable data retrieval from the ontology. Through validation queries, the ontology addresses questions related to network latency, energy efficiency, security, quality of service, and anomaly detection. By unifying performance metrics across diverse domains, OntOraculum enables comprehensive analysis of dependencies and interactions among metrics. The results demonstrate its practical applicability for monitoring, optimizing, and adapting systems in distributed environments.

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IoT data simulation,Heuristic optimization,Predictive analytics,Machine learning,Ontology

• Computer and Information Sciences - Natural Sciences
• Other Engineering and Technology Sciences - Engineering and Technology
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology