[论文解读] Edge AI: A Taxonomy, Systematic Review and Future Directions

Sukhpal Singh Gill, Muhammed Golec+13 more|arXiv (Cornell University)|Jul 4, 2024

IoT and Edge/Fog Computing被引用 7

一句话总结

本文提供对边缘AI的全面分类法和系统综述，强调架构、应用、挑战与未来研究方向。

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.

研究动机与目标

• 介绍边缘AI、其历史、挑战与前景。
• 提供覆盖应用与方法的边缘AI系统的系统性文献综述。
• 提出一个分类边缘AI系统的分类法，以指导架构决策。
• 突出基础设施考量（云、雾、边缘）和资源管理。
• 识别研究空白并勾画未来工作的方向。

提出的方法

• 对边缘AI研究与应用进行系统性文献综述。
• 开发并提出一个分类边缘AI系统的分类法，涵盖基础设施、服务、ML/DL与资源管理。
• 将现有的边缘AI实现与所提出的分类法进行比较。
• 分析资源约束、安全漏洞和可扩展性等挑战。
• 讨论未来方向及对当前局限性的创新解决方案。

Figure 1: Computing Paradigms and their Objectives.

实验结果

研究问题

• RQ1 在应用与技术方面，边缘AI研究的当前状态是什么？
• RQ2 如何通过分类法有效对边缘AI系统进行分类和组织？
• RQ3 边缘AI面临的主要挑战（资源、安全性、可扩展性）是什么，以及如何解决？
• RQ4 哪些未来方向和研究机会最有前景以推动边缘AI的发展？

主要发现

• 边缘AI使网络边缘实现实时数据处理，提升延迟、隐私和带宽效率。
• 提出一个分类边缘AI系统的分类法，覆盖基础设施、云/雾/边缘、服务、用例、ML/DL和资源管理。
• 当前的边缘AI研究涵盖智慧城市、智能制造、自动驾驶、工业自动化和智慧医疗，重点在ML/DL、安全性与隐私。
• 在边缘环境中，资源有限性、安全威胁、可扩展性和数据治理等重大挑战。
• 本文确定未来方向以解决局限性，包括改进可解释性、在线训练和面向资源感知的AI部署。

Figure 2: Architecture of Edge AI.