Framework of an IoT-based Industrial Data Management for Smart Manufacturing

TLDR

The Internet of Things connects diverse industrial devices, generating massive, heterogeneous, time‑sensitive data that challenges real‑time collection, processing, and decision making. This paper proposes an IoT‑based Industrial Data Management System to handle large industrial data, enabling online monitoring and smart manufacturing control. The framework comprises five layers—physical, network, middleware, database, and application—to deliver a service‑oriented architecture for end users. Case‑study experiments show the framework manages routine data and urgent events from factory devices, converting them into actionable information that boosts productivity and production line prognosis.

Abstract

The Internet of Things (IoT) is the global network of interrelated physical devices such as sensors, actuators, smart applications, objects, computing devices, mechanical machines, and people that are becoming an essential part of the internet. In an industrial environment, these devices are the source of data which provide abundant information in manufacturing processes. Nevertheless, the massive, heterogeneous, and time-sensitive nature of the data brings substantial challenges to the real-time collection, processing, and decision making. Therefore, this paper presents a framework of an IoT-based Industrial Data Management System (IDMS) which can manage the huge industrial data, support online monitoring, and control smart manufacturing. The framework contains five basic layers such as physical, network, middleware, database, and application layers to provide a service-oriented architecture for the end users. Experimental results from a smart factory case study demonstrate that the framework can manage the regular data and urgent events generated from various factory devices in the distributed industrial environment through state-of-the-art communication protocols. The collected data is converted into useful information which improves productivity and the prognosis of production lines.