🤖 AI Summary
This work addresses key challenges in industrial IoT—namely, weak semantic interoperability, lack of context awareness, and rigid management mechanisms—when handling heterogeneous high-velocity data streams. The authors propose a context-aware knowledge graph platform that uniquely integrates knowledge graphs, contextual reasoning, and distributed stream processing based on Apache Kafka and Flink. This platform provides a unified semantic model encompassing devices, data streams, agents, processing pipelines, and permission roles, enabling flexible data ingestion, composable stream processing, and dynamic, context-driven access control. Experimental results demonstrate that the proposed approach significantly enhances system interoperability, adaptability, and context awareness, thereby establishing a semantic, interpretable, and secure data workflow infrastructure for Industry 5.0.
📝 Abstract
Industrial IoT ecosystems bring together sensors, machines and smart devices operating collaboratively across industrial environments. These systems generate large volumes of heterogeneous, high-velocity data streams that require interoperable, secure and contextually aware management. Most of the current stream management architectures, however, still rely on syntactic integration mechanisms, which result in limited flexibility, maintainability and interpretability in complex Industry 5.0 scenarios. This work proposes a context-aware semantic platform for data stream management that unifies heterogeneous IoT/IoE data sources through a Knowledge Graph enabling formal representation of devices, streams, agents, transformation pipelines, roles and rights. The model supports flexible data gathering, composable stream processing pipelines, and dynamic role-based data access based on agents' contexts, relying on Apache Kafka and Apache Flink for real-time processing, while SPARQL and SWRL-based reasoning provide context-dependent stream discovery. Experimental evaluations demonstrate the effectiveness of combining semantic models, context-aware reasoning and distributed stream processing to enable interoperable data workflows for Industry 5.0 environments.