A Bibliometric Analysis of Convergence of Artificial Intelligence and Blockchain for Edge of Things

被引:0
|
作者
Deepak Sharma
Rajeev Kumar
Ki-Hyun Jung
机构
[1] Christian-Albrechts-Universität zu Kiel,Department of Computer Science
[2] Delhi Technological University,Blockchain Technology Research Lab, Department of Computer Science and Engineering
[3] Andong National University,Department of Software Convergence
来源
Journal of Grid Computing | 2023年 / 21卷
关键词
Bibliometric analysis; Artificial intelligence; Convergence; Blockchain; Edge of things; EOT;
D O I
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中图分类号
学科分类号
摘要
The convergence of Artificial Intelligence (AI) and Blockchain technologies has emerged as a powerful paradigm to address the challenges of data management, security, and privacy in the Edge of Things (EoTs) environment. This bibliometric analysis aims to explore the research landscape and trends surrounding the topic of convergence of AI and Blockchain for EoTs to gain insights into its development and potential implications. For this, research published during the past six years (2018-2023) in the Web of Science indexed sources has been considered as it has been a new field. VoSViewer-based full counting methodology has been used to analyze citation, co-citation, and co-authorship based collaborations among authors, organizations, countries, sources, and documents. The full counting method in VoSViewer involves considering all authors or sources with equal weight when calculating various bibliometric indicators. Co-occurrence, timeline, and burst detection analysis of keywords and published articles were also carried out to unravel significant research trends on the convergence of AI and Blockchain for EoTs. Our findings reveal a steady growth in research output, indicating the increasing importance and interest in AI-enabled Blockchain solutions for EoTs. Further, the analysis uncovered key influential researchers and institutions driving advancements in this domain, shedding light on potential collaborative networks and knowledge hubs. Additionally, the study examines the evolution of research themes over time, offering insights into emerging areas and future research directions. This bibliometric analysis contributes to the understanding of the state-of-the-art in convergence of AI and Blockchain for EoTs, highlighting the most influential works and identifying knowledge gaps. Researchers, industry practitioners, and policymakers can leverage these findings to inform their research strategies and decision-making processes, fostering innovation and advancements in this cutting-edge interdisciplinary field.
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