A Context-Aware Knowledge Graph Platform for Stream Processing in Industrial IoT

TL;DR

This paper introduces a semantic, context-aware platform using Knowledge Graphs for managing heterogeneous data streams in Industrial IoT, enhancing flexibility, security, and interpretability in Industry 5.0 scenarios.

Contribution

It presents a novel platform integrating semantic models, reasoning, and distributed stream processing for flexible, secure, and context-aware IoT data management.

Findings

Effective integration of semantic reasoning with real-time stream processing.

Enhanced data interoperability and security in industrial environments.

Demonstrated scalability and flexibility in Industry 5.0 scenarios.

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.