A semantic and ontology-based framework for enhancing interoperability and automation in IoT systems

The ever-growing and increasing diversity of IoT ecosystems, the seamless interoperability, scale, and near real-time automation have remained grand challenges. This paper presents a solution enterprise semantic and ontology-based framework to mitigate against these challenges through distributed edge cloud architecture with semantic mapping and ontology models. It seamlessly handles data coordination, protocol reconciliation, and interaction between dynamic devices within heterogeneous IoT environments. In this research paper, work simulation covered 100–500 IoT devices communicating using various protocols, such as MQTT, HTTP, and CoAP. The interoperability success rate of the proposed framework reached up to 98%, and the latency was reduced by 65% through edge processing while keeping the system efficiency at 85% with 500 devices, which proves that the proposed research novel framework is scalable and robust enough for real-world use cases. On top of this, The key research output and innovations involve ontology-based semantic mapping, machine learning models to interpret data, and practical resource usage across edge and cloud layers. The research paper findings reveal real-world applications of the proposed framework for smart cities, healthcare, and industrial IoT. Our ongoing work addresses the integration of AI-based (dynamic) ontologies and applying the framework in real-world deployments for more scalable and adaptable scenarios.