Edge AI: A Taxonomy, Systematic Review and Future Directions
Authors: 
Sukhpal Singh Gill, 
Muhammed Golec, Jianmin Hu, Minxian Xu, Junhui Du, Huaming Wu, Guneet Kaur Walia, Subramaniam Subramanian Murugesan, Babar Ali, Mohit Kumar, Kejiang Ye, Prabal Verma, Surendra Kumar, Felix Cuadrado, 
Steve UhligAuthors Info & Claims
Sukhpal Singh Gill, Muhammed Golec, Jianmin Hu, Minxian Xu, Junhui Du, Huaming Wu, Guneet Kaur Walia, Subramaniam Subramanian Murugesan, Babar Ali, Mohit Kumar, Kejiang Ye, Prabal Verma, Surendra Kumar, Felix Cuadrado, Steve UhligAuthors Info & ClaimsAbstract
Edge Artificial Intelligence (AI) incorporates a network of interconnected systems and devices that receive, cache, process, and analyse 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. The goal of Edge AI is to optimize data processing efficiency and velocity while ensuring data confidentiality and integrity. Despite being a relatively new field of research, spanning from 2014 to the present, it has shown significant and rapid development over the last five years. In this article, we present 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 also 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.
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Published: 18 October 2024
Accepted: 30 September 2024
Revision received: 14 September 2024
Received: 08 July 2024
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- Basic and Applied Basic Research Foundation of Guangdong Province
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- HE ACES project
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