Document Type : Original Article
Authors
1
Assistant Professor, Department of Management and Entrepreneurship, Faculty of financial science, Management and Entrepreneurship, University of Kashan, Kashan, Iran
2
1Assistant Professor, Department of Business Administration, University of Kashan, Kashan, Iran
3
3Assistant Professor, Faculty of Electrical Engineering, British Columbia Institute of Technology, Vancouver, Canada
Abstract
This study conducts a bibliometric analysis of research on the green industrial IoT published in the Web of Science (WOS) and Scopus databases from 2013 to 2024. A total of 467 scientific documents were analyzed, highlighting a research growth trend of 63.68% annually up to the end of 2023, underscoring the escalating interest in this domain. Our analysis categorizes the frequent keywords into three main themes: 1) Key themes(e.g., green industrial IoT, sustainability, energy efficiency, industry 4.0 industry and 5.0, and big data; 2) Hardware (e.g., essential IoT devices and tools such as cyber-physical systems, wireless sensor networks, sensors, wireless communication technologies, servers, unmanned aerial vehicles, and batteries; and 3) Software (e.g., computing technologies like artificial intelligence, machine learning, blockchain, optimization algorithms, deep learning, cloud computing, edge computing, non-orthogonal multiple access, mobile edge computing, reinforcement learning, and fog computing). This research not only identifies leading researchers, institutions, and countries in this field but also provides a comprehensive overview of the current state of knowledge and identifies research gaps. This analysis aims to guide researchers interested in conducting review studies, such as meta-syntheses and meta-analyses, in the green industrial IoT filed. Furthermore, it offers valuable insights for science and technology policymakers in developing research strategies and funding decisions within this critical area of study.
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