Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.

中文翻译：

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边缘智能：赋予网络边缘智能


边缘智能是指一组连接的系统和设备，用于基于人工智能捕获数据的位置附近的数据收集、缓存、处理和分析。边缘智能旨在增强数据处理并保护数据和用户的隐私和安全。尽管这一研究领域刚刚兴起，时间跨度为 2011 年至今，但在过去五年中呈现出爆炸性增长。在本文中，我们对有关边缘智能的文献进行了彻底而全面的调查。我们首先根据与所提出和部署的系统相关的理论和实践结果，确定边缘智能的四个基本组成部分，即边缘缓存、边缘训练、边缘推理和边缘卸载。然后，我们的目标是通过检查四个组成部分中每个组成部分的研究结果和观察结果，对解决方案的状态进行系统分类，并提出包括实际问题、采用的技术和应用目标的分类法。对于每个类别，我们从所采用的技术、目标、性能、优缺点等角度对文献进行阐述、比较和分析。本文对边缘智能及其应用领域进行了全面的调查。此外，我们总结了新兴研究领域的发展和现状，并讨论了重要的开放问题和可能的理论和技术方向。