Mapping the Convergence of Artificial Intelligence, IoT, and Embedded Systems: A Comprehensive Bibliometric Analysis (2015-2025)
TL;DR
A paradigm shift toward autonomous cyber-physical systems is confirmed, successfully identifying the "gatekeepers" and emerging frontiers of the field and confirming the integration of non-functional requirements, such as energy efficiency and ethics, into the next generation of edge devices.
Abstract
Background: The rapid convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and Embedded Systems has birthed the era of "Edge Intelligence. " Current knowledge indicates a fundamental technological pivot where intelligence is moving away from centralized cloud architectures toward decentralized, autonomous, on-device processing. Objective: The study aimed to map the global research landscape, identify key scientific contributors, analyze collaboration networks, and track the thematic evolution of Edge Intelligence research between 2015 and 2025. Methods: This study utilized a quantitative bibliometric methodology and scientific mapping. A corpus of 2,623 peer-reviewed documents was extracted from the Scopus database and analyzed using specialized software tools, namely R-Bibliometrix and VOSviewer. Results: The findings reveal an extraordinary annual research growth rate of 61.21%. While China is the quantitative leader in total citations, countries like Australia, the UK, and the USA demonstrate the highest qualitative impact per publication. The IEEE Internet of Things Journal was identified as the most influential venue. Thematically, the field has shifted from "cloud-centric AI" to "on-device intelligence," powered by breakthroughs in Tiny ML and FPGA optimization, with a rising focus on data privacy, green computing, and network security. Conclusion: The results confirm a paradigm shift toward autonomous cyber-physical systems, successfully identifying the "gatekeepers" and emerging frontiers of the field. Future research should explore the integration of non-functional requirements, such as energy efficiency and ethics, into the next generation of edge devices.