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

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.

A Connective Framework to Support the Lifecycle of Cyber–Physical Production Systems

“…Saqlain et al [28] report on experimental results from a smart factory case study that demonstrates that a 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 are converted into useful information, which improves productivity and the prognosis of production lines [28]. Their proposed framework contains five basic layers, physical, network, middleware, database, and application layers, to provide a service-oriented architecture for the end users.…”

The Emergence of Artificial Intelligence for Industrial Internet of Thing Engagement

“…ML refers to a set of techniques that allow us to create AI software by training that software with data) to display some desired intelligent behavior. This is as opposed to, for example, explicitly programming our software with a bunch of rules to generate our desired behavior and it's a very powerful concept [19].IIoT will be a significant part of the market. This segment includes self-optimizing production, automated inventory management, predictive maintenance, remote patient monitoring, smart meters, track and trace, connected cards, distributed generation and storage, fleet management, and demand response, all of which can be achieved by using "sensors, computers, robots and other machinery that interact with each other and their environment over a network, transmitting real-time data that, with the aid of an analytics platform, can be used to improve manufacturing processes."…”

“…Industry 4.0 will change the way goods are manufactured. Concepts such as predictive maintenance demand forecasting, and digital twins not only reduce downtime and quality excursions, but can also help optimize production efficiency [19][5], [26], [27]. combination of data from external and internal sources to improve decision-making; the development of digital skills for a better integration and management of resources within the organization, including security, cyber security and risk control; the understanding of how technology can affect the Industry 4.0 localized manufacturing; and finally, the simultaneous work on the development of smart products and manufacturing processes.…”

Data Architecture for Digital Twin of Commercial Greenhouse Production

“…In this new data-driven environment, the processing, storage, and utilization of the manufacturing data become increasingly important which makes the traditional one-way data architecture obsolete as it requires multi-scenario application. [18] develop a Industrial Data Management System (IDMS) with five layers, and IDMS presents a system for real-time and scalable data collection, transmission, processing, and storage in industrial systems. A similar four-layered architecture for IoT devices was presented by Xu et al [19] in a survey that systematically reviewed and summarized the current state-of-art in industry IoT.…”