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Edge AI: A Taxonomy, Systematic Review and Future Directions by 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 is a scholarly article available to read on EtoBox.

What is Edge AI: A Taxonomy, Systematic Review and Future Directions about?

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

Author

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

Published

2024

Language

EN