**1. Introduction**

The progress of industrialization has been changed and transformed from automation to digitalization. Similarly, Industry 4.0 in Germany faces the same problems that originated in different countries, such as the Internet industry in the United States made by China, Japan Industry 4.1, and South Korea manufacturing Industry Innovation 3.0. The connection of entities is based on two main features. Digitalization and identification are important features for entity connection. From another perspective, the Internet of Things (IoT) is determined for managing the identification problems, which mostly happen in the Industrial Internet of Things (IIoT). The cyber-physical system is defined to solve the entities' connection problems.

In a recent development, smart manufacturing was named a core of modern production in the manufacturing industry's digitalization. Similarly, it is the smart factory's foundation [1]. The smart manufacturing process uses information technology (IT) to connect the facilities and terminal devices that are digitalized [2]. The interactions between the devices produce massive amounts of data, which causes multiple requirements for the processing of data, e.g., unstructured, able to handle massive amounts, and less time delay. Big data techniques, cloud computing techniques, and artificial intelligence techniques are presented to simplify data processing, which is part of data technology (DT). Furthermore, operational technology (OT) achievement is based on the combination of detailed control machines and data computation, e.g., a distributed control system, programmable logic controller, data acquisition, and supervisory control. Cloud manufacturing services are applied for further processes of the inner performance of smart manufacturing. This section presents a brief explanation of smart manufacturing and related techniques. There are

**Citation:** Shahbazi, Z.; Byun, Y.-C. Improving Transactional Data System Based on an Edge Computing– Blockchain–Machine Learning Integrated Framework. *Processes* **2021**, *9*, 92. https://doi.org/10.3390/ pr9010092

Received: 24 October 2020 Accepted: 30 December 2020 Published: 4 January 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

three main topics discussed in this section—edge computing; blockchain; IoT, Industrial Internet of Things (IIoT), Industry 4.0, and cyber-physical systems (CPS).

#### *1.1. Edge Computing*

In recent years, many researchers have focused on the edge computing issue regarding intelligent manufacturing. To address some of the low latency and limited resources of this system, Yin et al. [3] proposed a novel visualization service for task scheduling based on fog computing and explored a new approach to the task scheduling algorithm based on a container role. The proposed system is able to reduce the delay rate of the tasks and improve the concurrent tasks on fog nodes. Lei et al. [4] presented the architecture of adaptive transmission containing edge computing and software-defined network (SDN) to solve the problem of data exchanging in IIoT and intelligent devices. Suganuma et al. [5] proposed the Flexible and Advanced Internet of Things (FLEC) to overcome the integration of traditional Internet of Things and edge computing problem that focuses on user positioning adapting to the environment. Lin et al. [6] presented the swarm optimization algorithm connected with a genetic algorithm to overcome the load balancing problem in traditional data placement based on optimizing the transfer time. To achieve detailed control of smart manufacturing systems, communication latency and a reliable environment are required. The multi-access edge computing (MEC) provides all the mentioned requirements. Similarly, cloud computing's capabilities and information technology provide environmental services on the edge network, despite the access technology [7]. Chen et al. [8] proposed a multi-micro-controller structure, which is the gateway for the Industrial Internet and combines the array-based programmable gateway of hardware with multiple scalable micro-controllers. Li et al. [9] proposed adaptive transmission architecture based on the centralized global support for am IIoT edge computing network. Another approach presented by Yu et al. [10] is the survey of edge computing performance on IoT applications—smart cities, smart farms, smart transportation, etc. Porambage et al. [11] showed an MEC overview for IoT applications realization and synergy.
