数据与计算发展前沿 ›› 2020, Vol. 2 ›› Issue (4): 16-27.doi: 10.11871/jfdc.issn.2096-742X.2020.04.002

• 专刊:下一代互联网络技术与应用(下) • 上一篇    下一篇

边缘智能:人工智能向边缘分布式拓展的新触角

莫梓嘉(),高志鹏(),苗东()   

  1. 北京邮电大学,网络与交换技术国家重点实验室,北京 100876
  • 收稿日期:2020-03-31 出版日期:2020-08-20 发布日期:2020-09-10
  • 通讯作者: 高志鹏
  • 作者简介:莫梓嘉,北京邮电大学,在读博士研究生。研究方向为边缘智能的分布式协同合作、边缘计算中的任务卸载和资源分配问题等。
    本文主要承担文献调研及边缘智能技术概述。
    Mo Zijia is a PhD candidate at Beijing University of Posts and Telecommunications. Her research interests include distributed collaboration on edge intelligence, task offloading and resource allocation in edge computing.
    In this paper, she is mainly responsible for literature researching and summarizing overview of edge intelligence technologies.
    E-mail: mozijia@bupt.edu.cn|高志鹏,北京邮电大学,教授,博士生导师,中国计算机学会区块链专委会委员、YOCSEF总部AC委员、中国电子学会青年科学家俱乐部委员、中国人工智能学会智能服务专委会委员、中国指挥控制学会大数据与工程专委会委员;目前研究方向包括:边缘计算、群智计算与大数据、区块链应用技术、云平台管理等。研究成果获省部级科技一等奖3次;发表论文50余篇,拥有20余个国家发明专利、4项国际标准和多项行业/企业标准。
    本文主要承担工作为边缘智能整体技术分析及技术指导。
    Gao Zhipeng, professor, is the doctoral tutor of Beijing University of Posts and Telecommunications, member of the Blockchain Committee of the Chinese Computer Society, AC member of YOCSEF headquarters, member of the Youth Scientific Club of the Chinese Institute of Electronics, member of the Intelligent Services Committee of the Chinese Association for Artificial Intelligence, and member of Chinese Institute of Command and Control of the Special Committee on Big Data and Engineering. His current research interests include edge computing, crowd computing, big data, blockchain application technology, and cloud platform management, etc. He have won 3 provincial and ministerial science and technology first prizes; published more than 50 articles papers, with more than 20 national invention patents, 4 international standards and multiple industry/enterprise standards.
    In this paper, he is mainly responsible for overall technical analysis and technical guidance of edge intelligence.
    E-mail: gaozhipeng@bupt.edu.cn|苗东,北京邮电大学,在读硕士研究生。研究方向为边缘智能下的边云协同合作、模型优化策略等
    本文主要承担边缘智能模型优化以及应用场景分析等部分。
    Miao Dong is a postgraduate student at Beijing University of Posts and Telecommunications. His research interests include edge cloud collaboration and model optimization strategies on edge intelligence.
    In this paper, he is mainly responsible for the optimization of edge intelligent models and analysis of application scenarios.
    E-mail: MIAODONG@bupt.edu.cn
  • 基金资助:
    国家重点研发计划项目(2016YFE0204500)

Edge Intelligence: A New Exploration for Artificial Intelligence Expanding to Edge

Mo Zijia(),Gao Zhipeng(),Miao Dong()   

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications,Beijing 100876, China
  • Received:2020-03-31 Online:2020-08-20 Published:2020-09-10
  • Contact: Gao Zhipeng

摘要:

【目的】对边缘智能技术进行系统而全面的介绍,为相关读者了解和关注边缘智能提供一定的参考,并启发更多的学者开展物联网时代边缘智能模型的研究。【方法】本文首先简要介绍了边缘智能的起源与概念,梳理了边缘智能的研究趋势与发展动态,然后归纳了目前存在的三种主要矛盾,最后概括了当前针对边缘智能矛盾的四个研究方向,并列举了典型的边缘智能场景。【局限】作为正处于技术储备阶段的新型技术,边缘智能的发展多数由产业驱动,学术界缺乏对其标准化、一体化的研究思路,暂时无法论述未来的发展。【结论】尽管处于发展初期,边缘智能在未来将成为智能产业发展的催化剂,促进整个工业体系的升级转型。

关键词: 边缘智能, 物联网, 边缘计算, 人工智能, 协同计算

Abstract:

[Objective] This paper provides a comprehensive introduction to edge intelligence technologies, aiming to provide a reference for related readers to understand and focus on edge intelligence, and inspire more scholars to carry out researches on edge intelligence models in the era of the Internet of Things. [Methods] The paper first briefly introduces the origin and concept of edge intelligence and sorts out development trends, and then summarizes three major contradictions that currently exist. Finally, we summarize the current four research directions for the contradiction of edge intelligence and list typical application scenes. [Limitations] As a new technology at the initial stage, the development of edge intelligence is mostly driven by the industry rather than academia. The academic community lacks research ideas for standardization and integration, and cannot discuss future development for the time being. [Conclusions] Despite in the early stages of development, edge intelligence will become a catalyst for the development of the intelligent industry in the future, and promote the upgrading and transformation of the entire industry system.

Key words: edge intelligence, IoT, edge computing, artificial intelligence, collaborative computing