The Management of IoT-Based Organizational and Industrial Digitalization Using Machine Learning Methods

Aoqi Xu, Mehdi Darbandi, Danial Javaheri, Nima Jafari Navimipour (), Senay Yalcin and Anas A. Salameh
Additional contact information
Aoqi Xu: School of Economics, Fujian Normal University, Fuzhou 350007, China
Mehdi Darbandi: Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Gazimagusa 99628, Turkey
Danial Javaheri: Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea
Nima Jafari Navimipour: Department of Computer Engineering, Kadir Has University, Istanbul 34083, Turkey
Senay Yalcin: Department of Computer Engineering, Nisantasi University, Istanbul 34485, Turkey
Anas A. Salameh: Department of Management Information Systems, College of Business Administration, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia

Sustainability, 2023, vol. 15, issue 7, 1-28

Abstract: Recently, the widespread adoption of the Internet of Things (IoT) model has led to the development of intelligent and sustainable industries that support the economic security of modern societies. These industries can offer their participants a higher standard of living and working services via digitalization. The IoT also includes ubiquitous technology for extracting context information to deliver valuable services to customers. With the growth of connected things, the related designs often suffer from high latency and network overheads, resulting in unresponsiveness. The continuous transmission of enormous amounts of sensor data from IoT nodes is problematic because IoT-based sensor nodes are highly energy-constrained. Recently, the research community in the field of IoT and digitalization has labored to build efficient platforms using machine learning (ML) algorithms. ML models that run directly on edge devices are intensely interesting in the context of IoT applications. The use of intelligence ML algorithms in the IoT can automate training, learning, and problem-solving while enabling decision-making based on past data. Therefore, the primary aim of this research is to provide a systematic procedure to review the state-of-the-art on this scope and offer a roadmap for future studies; thus, a structure is introduced for industry sustainability, based on ML methods. The publications were reviewed using a systematic approach that divided the papers into four categories: reinforcement learning, semi-supervised learning, unsupervised learning, and supervised learning. The results showed that ML models could manage IoT-enabled industries efficiently and provide better results compared to other models, with significant differences in learning time and performance. The study findings are considered from a variety of angles concerning the industrial sector’s capacity management of the new elements of Industry 4.0 by combining the industry IoT and ML. Additionally, unique and relevant instructions are provided for the designers of expert intelligent production systems in industrial domains.

Keywords: internet of things; industrial IoT; machine learning; industrial digitalization; sustainability; energy; digital economy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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