基于数据驱动的云边智能协同综述

doi:10.11772/j.issn.1001-9081.2022091418

《计算机应用》唯一官方网站 ›› 2023, Vol. 43 ›› Issue (10): 3162-3169.DOI: 10.11772/j.issn.1001-9081.2022091418

所属专题：先进计算；综述

基于数据驱动的云边智能协同综述

田鹏新¹, 司冠南¹(), 安兆亮¹, 李建辛¹, 周风余²

^1.山东交通学院信息科学与电气工程学院，济南 250357
^2.山东大学控制科学与工程学院，济南 250061

收稿日期:2022-09-22 修回日期:2023-01-05 接受日期:2023-01-06 发布日期:2023-03-16 出版日期:2023-10-10
通讯作者: 司冠南
作者简介:田鹏新（1999—），男，山东济宁人，硕士研究生，主要研究方向：大数据、边缘计算
安兆亮（1998—），男，山东济南人，硕士研究生，主要研究方向：大数据、云计算
李建辛（1997—），男，山东枣庄人，硕士研究生，主要研究方向：机器学习、认知图谱
周风余（1969—），男，山东沂南人，教授，博士，主要研究方向：智能机器人、云机器人。
基金资助:
山东省自然科学基金资助项目(ZR2019MF064)

Survey of data-driven intelligent cloud-edge collaboration

Pengxin TIAN¹, Guannan SI¹(), Zhaoliang AN¹, Jianxin LI¹, Fengyu ZHOU²

^1.School of Information Science and Electrical Engineering，Shandong Jiaotong University，Jinan Shandong 250357，China
^2.School of Control Science and Engineering，Shandong University，Jinan Shandong 250061，China

Received:2022-09-22 Revised:2023-01-05 Accepted:2023-01-06 Online:2023-03-16 Published:2023-10-10
Contact: Guannan SI
About author:TIAN Pengxin， born in 1999， M.S. candidate. His research interests include big data， edge computing.
AN Zhaoliang， born in 1998， M. S. candidate. His research interests include big data， cloud computing.
LI Jianxin， born in 1997， M. S. candidate. His research interests include machine learning， cognitive graph.
ZHOU Fengyu， born in 1969， Ph. D.， professor. His research interests include intelligent robots， cloud robots.
Supported by:
Natural Science Foundation of Shandong Province(ZR2019MF064)

摘要/Abstract

摘要：

随着物联网（IoT）的快速发展，大量在传感器等边缘场景产生的数据需要传输至云节点处理，这带来了极大的传输成本和处理时延，而云边协同为这些问题提供了有效的解决方案。首先，在全面调查和分析云边协同发展过程的基础上，结合当前云边智能协同中的研究思路与进展，重点分析和讨论了云边架构中的数据采集与分析、计算迁移技术以及基于模型的智能优化技术；其次，分别从边缘端和云端深入分析了各种技术在云边智能协同中的作用及应用，并探讨了云边智能协同技术在现实中的应用场景；最后，指出了云边智能协同目前存在的挑战及未来的发展方向。

关键词: 云边协同, 人工智能, 计算迁移, 模型训练与推理, 数据驱动

Abstract:

With the rapid development of Internet of Things （IoT）， a large amount of data generated in edge scenarios such as sensors often needs to be transmitted to cloud nodes for processing， which brings huge transmission cost and processing delay. Cloud-edge collaboration provides a solution for these problems. Firstly， on the basis of comprehensive investigation and analysis of the development process of cloud-edge collaboration， combined with the current research ideas and progress of intelligent cloud-edge collaboration， the data acquisition and analysis， computation offloading technology and model-based intelligent optimization technology in cloud edge architecture were analyzed and discussed emphatically. Secondly， the functions and applications of various technologies in intelligent cloud-edge collaboration were analyzed deeply from the edge and the cloud respectively， and the application scenarios of intelligent cloud-edge collaboration technology in reality were discussed. Finally， the current challenges and future development directions of intelligent cloud-edge collaboration were pointed out.

Key words: cloud-edge collaboration, Artificial Intelligence (AI), computation offloading, model training and inference, data-driven

中图分类号:

TP399

田鹏新, 司冠南, 安兆亮, 李建辛, 周风余. 基于数据驱动的云边智能协同综述[J]. 计算机应用, 2023, 43(10): 3162-3169.

Pengxin TIAN, Guannan SI, Zhaoliang AN, Jianxin LI, Fengyu ZHOU. Survey of data-driven intelligent cloud-edge collaboration[J]. Journal of Computer Applications, 2023, 43(10): 3162-3169.

图/表 5

图1 云边智能协同总体架构

Fig. 1 Overall architecture of intelligent cloud-edge collaboration

图2 数据采集与处理架构

Fig. 2 Data acquisition and processing architecture

图3 计算迁移架构

Fig. 3 Computation offloading architecture

图4 云边智能协同模型

Fig. 4 Intelligent cloud-edge collaboration model

图5 联邦学习模型

Fig. 5 Federated learning model

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基于数据驱动的云边智能协同综述

Survey of data-driven intelligent cloud-edge collaboration

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