A multi-model database framework for interoperable IoT sensor data management in smart manufacturing systems

The explosive adoption of IoT enabled smart manufacturing has increased the complexity of managing heterogeneous sensor data coming from diverse machines, communication protocols, and vendor-specific formats significantly. Conventional relational and time-series databases are very hard to adapt to the twin problems of high volume of data, structural diversity, and semantic inconsistency in industrial environments today. This paper proposes a multi-model database system in order to achieve high-performance interoperability for heterogeneous IoT sensor streams in the Smart Manufacturing Systems. The architecture includes a semantic integration layer to transform the data coming from formats such as JSON, XML, CSV, OPC-UA, and MQTT into a common canonical data model. The framework is evaluated on a synthetic but realistic Industry 4.0 dataset with roughly 5000 devices and over 40000 sensor measurements that allows the ingestion performance, cross-sensor query latency, and scalability of the framework to be evaluated. Experimental results show increased interoperability, support of unified cross-modal queries and low latency performance under growing loads of data. Furthermore, the cross-sensor correlation and analysis of any anomaly points to the applicability of the framework to analytics-oriented tasks, such as the early detection of abnormal machine behaviour. In general, the offered solution offers a semantically consistent, scalable base of interoperable IoT data management of smart manufacturing settings.