期刊
PROCEEDINGS OF THE IEEE
卷 109, 期 11, 页码 1778-1837出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPROC.2021.3119950
关键词
Training data; Data privacy; Systematics; Edge computing; Data collection; Market research; Artificial intelligence; Inference algorithms; Artificial intelligence (AI); edge caching; edge computing; inference; model training; offloading
资金
- National Key Research and Development Program of China [2020YFA0711400, 2020AAA0106000]
- National Natural Science Foundation of China [U1936217, 61971267, 61972223, 61941117, 61861136003]
- Beijing Natural Science Foundation [L182038]
- Beijing National Research Center for Information Science and Technology [20031887521]
- Academy of Finland [319669, 319670, 325570, 326305, 325774, 335934]
- Academy of Finland (AKA) [326305, 319670, 325774, 326305, 319670, 319669, 325570, 325774] Funding Source: Academy of Finland (AKA)
Edge intelligence refers to a connected system of devices for data processing based on AI at the edge where data is captured. It aims to enhance data processing capabilities and ensure data privacy and security. This research field has shown significant growth in recent years, with a focus on systematic classification and comprehensive analysis of proposed and deployed systems.
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.
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