边缘人工智能：分类体系、系统综述与未来方向
Edge AI: A Taxonomy, Systematic Review and Future Directions
AI总结：
本文系统综述了边缘人工智能的研究现状，提出协作式学习系统和分类体系，分析其在实时数据处理中的作用、面临的资源与安全等挑战，并展望未来研究方向。
摘要：
边缘人工智能（Edge AI）融合了一个由相互连接的系统与设备组成的网络，这些系统与设备在靠近数据采集位置的地方，利用人工智能技术接收、缓存、处理和分析数据。近年来人工智能效率的提升、物联网（IoT）设备的广泛使用以及边缘计算的出现，释放了边缘人工智能的巨大潜力。边缘人工智能旨在优化数据处理效率和速度，同时确保数据的机密性和完整性。尽管从2014年至今这仍是一个相对较新的研究领域，但在过去五年中已经展现出显著而快速的发展。本文对边缘人工智能进行了系统文献综述，以讨论现有研究、最新进展和未来研究方向。我们构建了一个用于云和边缘计算分析的协作式边缘人工智能学习系统，包括对促进该机制的架构进行深入研究。边缘人工智能分类体系有助于对边缘人工智能系统进行分类和配置，同时通过涵盖基础设施、云计算、雾计算、服务、用例、机器学习与深度学习以及资源管理等方面，考察其在多个领域的潜在影响。本研究强调了边缘人工智能在网络边缘处理实时数据方面的重要性。此外，本研究还强调了边缘人工智能系统所面临的研究挑战，包括资源限制、安全威胁的脆弱性以及可扩展性问题。最后，本研究指出了旨在通过提供创新解决方案来解决当前边缘人工智能局限性的潜在未来研究方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
