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Edge AI: A Taxonomy, Systematic Review and Future Directions

Authors :
Gill, Sukhpal Singh
Golec, Muhammed
Hu, Jianmin
Xu, Minxian
Du, Junhui
Wu, Huaming
Walia, Guneet Kaur
Murugesan, Subramaniam Subramanian
Ali, Babar
Kumar, Mohit
Ye, Kejiang
Verma, Prabal
Kumar, Surendra
Cuadrado, Felix
Uhlig, Steve
Source :
Springer Cluster Computing, Volume 28, article number 18, pages 11953 - 11981, (2025)
Publication Year :
2024

Abstract

Edge Artificial Intelligence (AI) incorporates a network of interconnected systems and devices that receive, cache, process, and analyze data in close communication with the location where the data is captured with AI technology. Recent advancements in AI efficiency, the widespread use of Internet of Things (IoT) devices, and the emergence of edge computing have unlocked the enormous scope of Edge AI. Edge AI aims to optimize data processing efficiency and velocity while ensuring data confidentiality and integrity. Despite being a relatively new field of research from 2014 to the present, it has shown significant and rapid development over the last five years. This article presents a systematic literature review for Edge AI to discuss the existing research, recent advancements, and future research directions. We created a collaborative edge AI learning system for cloud and edge computing analysis, including an in-depth study of the architectures that facilitate this mechanism. The taxonomy for Edge AI facilitates the classification and configuration of Edge AI systems while examining its potential influence across many fields through compassing infrastructure, cloud computing, fog computing, services, use cases, ML and deep learning, and resource management. This study highlights the significance of Edge AI in processing real-time data at the edge of the network. Additionally, it emphasizes the research challenges encountered by Edge AI systems, including constraints on resources, vulnerabilities to security threats, and problems with scalability. Finally, this study highlights the potential future research directions that aim to address the current limitations of Edge AI by providing innovative solutions.<br />Comment: Preprint Version Accepted for Publication in Springer Cluster Computing, 2024

Details

Database :
arXiv
Journal :
Springer Cluster Computing, Volume 28, article number 18, pages 11953 - 11981, (2025)
Publication Type :
Report
Accession number :
edsarx.2407.04053
Document Type :
Working Paper
Full Text :
https://doi.org/10.1007/s10586-024-04686-y