*Article* **Improving Transactional Data System Based on an Edge Computing–Blockchain–Machine Learning Integrated Framework**

**Zeinab Shahbazi and Yung-Cheol Byun \***

> Department of Computer Engineering, Jeju National University, Jeju 63243, Korea; zeinab.sh@jejunu.ac.kr **\*** Correspondence: ycb@jejunu.ac.kr

**Abstract:** The modern industry, production, and manufacturing core is developing based on smart manufacturing (SM) systems and digitalization. Smart manufacturing's practical and meaningful design follows data, information, and operational technology through the blockchain, edge computing, and machine learning to develop and facilitate the smart manufacturing system. This process's proposed smart manufacturing system considers the integration of blockchain, edge computing, and machine learning approaches. Edge computing makes the computational workload balanced and similarly provides a timely response for the devices. Blockchain technology utilizes the data transmission and the manufacturing system's transactions, and the machine learning approach provides advanced data analysis for a huge manufacturing dataset. Regarding smart manufacturing systems' computational environments, the model solves the problems using a swarm intelligencebased approach. The experimental results present the edge computing mechanism and similarly improve the processing time of a large number of tasks in the manufacturing system.

**Keywords:** smart manufacturing; edge computing; machine learning; blockchain; Industrial Internet of Things
