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Abstract

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.

Keywords

IoT data management Smart manufacturing Multi-model databases Semantic interoperability Industry 4.0 Time-series analytics

Article Details

How to Cite
Padhiyar, J. . (2026). A multi-model database framework for interoperable IoT sensor data management in smart manufacturing systems . Future Technology, 5(2), 148–158. Retrieved from https://fupubco.com/futech/article/view/703
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