A Unified Namespace-Based Edge-to-Cloud Framework for Industrial Data Analytics

Authors

Keywords:

Unified Namespace, Industrial Internet of Things, Edge-to-Cloud Framework, Industrial Analytics, Fault Diagnosis, Induction Motors

Abstract

Industry 4.0 has intensified the demand for Industrial Internet of Things (IIoT) platforms supporting scalable and interoperable industrial analytics. However, many existing architectures treat data acquisition, processing, and visualization as loosely connected components within fragmented designs, limiting scalability, cross-domain generalizability, and accessibility for organizations lacking specialized infrastructure. This paper presents a flexible edge-to-cloud framework for industrial analytics based on a Unified Namespace (UNS) architecture. Unlike existing IIoT architectures, which rely on point-to-point or gateway-mediated integration and recover semantic context from external mappings, the proposed framework makes the namespace itself the integration contract: every message is self-describing, and integration interfaces grow linearly rather than multiplicatively with the number of connected components. The framework is evaluated on physically independent, heterogeneous hardware against three design pillars: scalability, generalizability, and accessibility. Its practical viability is demonstrated through a case study involving induction motor fault diagnosis. Per-device latency remains near 0.5s and is unaffected as independent edge nodes join. Substituting the diagnostic model, and subsequently the data type, sampling regime, and analytic paradigm, required no modification outside the analytics component, while classification accuracy remained above 98% after cross-dataset adaptation. The complete pipeline runs on single-board hardware within the free tiers of open-source and managed services, making the framework viable for resource-constrained industrial deployments.

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Author Biographies

Joaquín D. López, Universidad Industrial de Santander

Joaquín D. López received a B.S. degree in Electronic Engineering and an M.Sc. degree in Telecommunications Engineering from the Universidad Industrial de Santander (UIS), Colombia, in 2023 and 2025, respectively. He is currently pursuing a Ph.D. degree in Electronics Engineering at UIS, where he is affiliated with the GISEL research group. In parallel, he is involved with Dautom, a technology company focused on industrial digitalization. His research interests include smart electrical microgrids, Industrial IoT (IIoT), UNS architectures with MQTT Sparkplug B, and the integration of AI tools for industry applications.

Juliam Díaz, Universidad Industrial de Santander

Juliam Díaz received the B.S. degree in Electronic Engineering, with the highest academic distinction Summa Cum Laude, from the Universidad Industrial de Santander (UIS), Colombia, in 2023, and the M.Sc. degree in Telecommunications Engineering from the same university in 2025. He is currently a Ph.D. student in Engineering at UIS. His experience and research interests include communication protocols, data processing, the Industrial Internet of Things (IIoT), and UNS architectures for the development of data pipelines and information analytics.

Natalia Duarte, Dautom

Natalia Duarte is an industrial designer with over 15 years of experience in business management. She holds a specialization diploma in Senior Management from UIS. She is co-founder and Business Director of Dautom, where she leads digital transformation strategy for manufacturing and energy industries. Her research interests focus on scalable IIoT solutions for regional industry.

Iván Hernández, Dautom

Iván Hernández received a B.Sc. degree in electronic engineering from Universidad Industrial de Santander (UIS), Colombia, in 2006, and a specialization diploma in industrial automation from Universidad Autónoma de Bucaramanga, Colombia, in 2016. For the last two decades, he has worked as an Industry 4.0 integrator and Digital Systems Architect, integrating SCADA, MES, IIoT, and data cloud platforms for industrial solutions. He is currently the director of engineering and lead architect at Dautom, a company he co-founded. His research areas include IIoT, UNS, Data Cloud, and scalable industrial control systems.

Carlos A. Fajardo, Universidad Industrial de Santander

Carlos A. Fajardo is a faculty professor at UIS, Colombia. He holds a Ph.D. in Engineering (High-Performance Computing), an M.Sc. in Electronic Engineering, and a postgraduate certificate in University Teaching, all from UIS. He completed a postdoctoral fellowship at the Center for Brain-Inspired Computing (C-BRIC), Purdue University, specializing in edge AI through hardware-software co-design. His research focuses on AI applied to medical problems and advanced digital systems.

Juan M. Rey, Universidad Industrial de Santander

Juan M. Rey received a B.S. degree in Electrical Engineering and a Specialization Diploma in University Teaching from the Universidad Industrial de Santander (UIS), Colombia, in 2012 and 2016, respectively. In 2019, he received a Ph.D. degree in Electronics Engineering from the Technical University of Catalonia, Spain. In 2022, he was honored by Germany’s Federal Ministry of Education and Research (BMBF) as a Green Talent. Since 2013, he has been affiliated with the Electrical, Electronic, and Telecommunications Engineering School (E3T), UIS, where he is currently an Associate Professor. His research interests include power electronics, the design and control of electrical microgrids, and the use of IoT for industrial applications.

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Published

2026-10-20

How to Cite

D. López, J., Díaz, J., Duarte, N., Hernández, I., Fajardo, C. A., & Rey, J. M. (2026). A Unified Namespace-Based Edge-to-Cloud Framework for Industrial Data Analytics. IEEE Latin America Transactions, 24(11), 1271–1285. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/10869

Issue

Vol. 24 No. 11 (2026): Ordinary Issue

Section

Computing