The Internet of Things as a Semantic Infrastructure – Integration of Sensor Data with Distributed Knowledge Systems in Edge – Cloud Environments
Abstract
The rapid expansion of the Internet of Things (IoT) leads to the generation of large volumes of heterogeneous and context-dependent data, creating significant challenges in data integration, interpretation, and processing. This paper proposes a conceptual model of IoT as a semantic infrastructure that integrates sensor data with distributed knowledge systems within an edge–cloud computing environment. The approach combines semantic technologies, including RDF, OWL, and SSN/SOSA ontologies, with a multi-layer data processing architecture. The model is formalized as a sequence of transformation functions enabling structured data flow from acquisition to analysis. Edge-level preprocessing reduces latency and data volume, while cloud-based processing supports scalable analytics and semantic reasoning. The proposed solution improves interoperability, enhances scalability, and enables efficient near real-time processing. Furthermore, the integration of artificial intelligence methods supports anomaly detection, data completion, and predictive analysis. The presented model provides a unified and adaptable framework for the development of intelligent, data-driven IoT systems.
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