Exploring the landscape of AI-SDN: A comprehensive bibliometric analysis and future perspectives
Document Type
Article
Publication Date
1-1-2024
Abstract
The rising influence of artificial intelligence (AI) enables widespread adoption of the technology in every aspect of computing, including Software-Defined Networking (SDN). Technological adoption leads to the convergence of AI and SDN, producing solutions that overcome limitations present in traditional networking architecture. Although numerous review articles discuss the convergence of these technologies, there is a lack of bibliometric trace in this field, which is important for identifying trends, new niches, and future directions. Therefore, this study aims to fill the gap by presenting a thorough bibliometric analysis of AI-related SDN studies, referred to as AI-SDN. The study begins by identifying 474 unique documents in the Web of Science (WoS) database published from 2009 until recently. The study uses bibliometric analysis to identify the general information, countries, authorship, and content of the selected articles, thereby providing insights into the geographical and institutional landscape shaping AI-SDN research. The findings provide a robust roadmap for further investigation in this field, including the background and taxonomy of the AI-SDN field. Finally, the article discusses several challenges and the future of AI-SDN in academic research.
Keywords
artificial intelligence, Software-Defined Networking, bibliometrics, machine learning, data visualization
Divisions
Computer
Publication Title
Electronics
Volume
13
Issue
1
Publisher
MDPI
Publisher Location
ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND