A Review of Using Green Industrial Internet of Things in Sustainable Development and Energy Management: A Bibliometrics Analysis

Introduction
Over the last decade, energy consumption has surged to alarming levels, fueled by the expansive digital landscape, increasing subscriber base, and the proliferation of devices. Projections indicated that the number of connected devices could soar to 100 billion by 2030. This growth in connected devices and the advancement of Internet of Things technology have led to a paradoxical situation where, despite improvements in energy efficiency, the overall energy consumption has escalated due to the sheer volume of these devices. This situation has not only led to rising energy costs but also posted significant environmental challenges. In response, the concept of a sustainable and green industrial Internet of Things (G-IIoT) has emerged. This paradigm is dedicated to reducing environmental impacts and fostering sustainable industrial development by enhancing energy management. The G-IIoT focuses on various aspects, from manufacturing and consumption to planning, and extends to recycling and disposal, all of which significantly affect the environment. This research aims to bridge the gap in the literature by providing a comprehensive framework for studying sustainable and green industrial IoT, an area that has seen increasing interest and adoption but lacks a cohesive analytical framework. By employing bibliometric analysis—a widely used method in engineering research for evaluating the impact of previous studies and suggesting future directions—the study seeks to guide researchers and enhance the understanding of this critical topic.
 
Methodology
This study conducted a bibliometrics analysis of researches related to G-IIoT have published in WOS and Scopus, during 2013 to 2024. Bibliometric analysis is a scientific discipline that employs statistical techniques to evaluate the advancement and growth of knowledge within a particular subject area, as well as to assess the academic quality and impact of various studies and sources. Based on the inclusion criteria, a total of 416 articles were retrieved from WOS (n=342) and Scopus (n=425). Then, 300 duplicate articles were excluded, resulting in 467 scientific distinct documents related to the field of G-IIoT.
 
Findings
The analysis demonstrates a significant annual growth rate of 63.68% in research interest up to the end of 2023, highlighting the expanding engagement of scholars in the G-IIoT. China has notably positioned itself as a pivotal contributor to this domain, with a total of 117 publications, 53 of which feature international collaborations, underscoring its global research leadership. Our study identifies the evolution of key themes within green industrial Internet of Things (G-IIoT) research across three distinct periods: the initial eight years (2013-2020), followed by a two-year span (2021-2022), and the most recent 13 months (2023-2024). Throughout these intervals, persistent themes have included the IoT, industrial IoT, Industry 4.0, energy efficiency, sustainability, wireless sensor networks, and big data. A notable development was the rise of artificial intelligence in the second phase, while Industry 5.0 emerged as a significant concept in the latest timeframe. Through keyword co-occurrence analysis (Fig. 1), two primary research clusters were identified within the G-IIoT field. The first cluster encompasses discussions on IoT and its associated advancements, such as Industry 4.0 and 5.0, artificial intelligence, and sustainability. The second cluster focuses on into the specifics of energy management and efficiency within the industrial IoT framework.
Figure (1): Keyword co-occurrence network
 
According to fig. 1, research topics in the domain of G-IIoT have been organized into three primary categories as follows:
Key Concepts and Themes: This category encompasses the foundational and emerging themes within green industrial IoT research. It includes a broad range of topics such as the Internet of Things, Industry 4.0 and Industry 5.0, smart production, big data, sustainability and sustainable development, circular economy, sustainable production practices, the Sustainable Development Goals, digital twins, energy management, sustainable supply chains, carbon emissions, energy usage, industrial IoT, energy efficiency and consumption, task and resource management, security, quality of service (QoS), monitoring, protocols, energy harvesting, reliability, 5G mobile communication, and routing.
Hardware (Key Tools and Devices): This category highlights the hardware components critical to energy management and efficiency in the field of G-IIoT. Key hardware elements include cyber-physical systems, wireless sensor networks, sensors, wireless communication technologies, servers, unmanned aerial vehicles, and batteries.
Software (Computing Technologies): In the realm of G-IIoT, focus on the software and computing technologies that underpin both the industry 4.0 and 5.0. Notable technologies in this category include 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.
These categories collectively provide a comprehensive overview of the current research landscape and technological advancements in the green industrial Internet of Things.
 
Discussion and Conclusion
Energy efficiency is a growing concern in every aspect of the technology. Apart from maintaining profitability, energy efficiency means a decrease in the overall environmental effects, which is a serious concern in today’s world. This study not only identifies leading researchers, journals, and countries in the field but also uncovers patterns of intra- and inter-disciplinary collaboration, thereby facilitating the development of research collaborations both nationally and internationally. By systematically identifying relevant literature, understanding the scope of research, and highlighting gaps in the existing body of knowledge, this study serves as a valuable resource for researchers undertaking review studies like meta-synthesis and meta-analysis in the GI-IoT domain. Additionally, the findings can assist science and technology policymakers in formulating research strategies and funding decisions to ensure effective allocation of financial resources to high-impact or strategically important areas.