边缘智能研究进展

张晓东; 张朝昆; 赵继军

doi:10.7544/issn1000-1239.202220192

边缘智能研究进展

张晓东^{1, 2, 3,},
张朝昆^2, ,,
赵继军^{1, 3}

1.
河北工程大学信息与电气工程学院　河北邯郸　056038
2.
天津大学智能与计算学部　天津　300350
3.
河北省安防信息感知与处理重点实验室（河北工程大学）　河北邯郸　056038

基金项目: 国家重点研发计划项目（2019YFB2102400）；河北省高层次人才资助项目（B202003027）；天津市研究生科研创新项目（2021YJSO2S04）

详细信息

作者简介:
张晓东: 1998年生. 硕士研究生. 主要研究方向为边缘计算、车联网

张朝昆: 1981年生. 博士，副教授，硕士生导师. CCF高级会员. 主要研究方向为下一代互联网、边缘计算、物联网

赵继军: 1970年生. 博士，教授，博士生导师. CCF高级会员. 主要研究方向为宽带通信网、物联网

通讯作者:
张朝昆（zhangchaokun@tju.edu.cn）

中图分类号: TP393
计量
- 文章访问数: 842
- HTML全文浏览量: 120
- PDF下载量: 417
出版历程
- 收稿日期: 2022-03-06
- 修回日期: 2023-02-26
- 网络出版日期: 2023-09-19
- 刊出日期: 2023-11-30

State-of-the-Art Survey on Edge Intelligence

Zhang Xiaodong^{1, 2, 3,},
Zhang Chaokun^2, ,,
Zhao Jijun^{1, 3}

1.
School of Information & Electrical Engineering, Hebei University of Engineering, Handan, Hebei 056038
2.
College of Intelligence and Computing, Tianjin University , Tianjin 300350
3.
Hebei Provicial Key Laboratory of Security and Protection Information Sensing and Processing (Hebei University of Engineering), Handan, Hebei 056038

Funds: This work was supported by the National Key Research and Development Program of China (2019YFB2102400), the Hebei Provincial High-Level Talent Funding Project (B202003027), and the Tianjin Research Innovation Project for Postgraduate Students (2021YJSO2S04).

More Information

Author Bio:
Zhang Xiaodong: born in 1998. Master candidate. Her main research interests include edge computing and Internet of vehicles

Zhang Chaokun: born in 1981. PhD, associate professor, master supervisor. Senior member of CCF. His main research interests include next generation Internet, edge computing, and Internet of things

Zhao Jijun: born in 1970. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include broadband communication network and Internet of things

摘要

摘要:
从智能手机、智能手表等小型终端智能设备，到智能家居、智能网联车等大型应用，再到智慧生活、智慧农业等，人工智能已经逐渐步入人们的生活，改变传统的生活方式. 各种各样的智能设备会产生海量的数据，传统的云计算模式已无法适应新的环境. 边缘计算在靠近数据源的边缘侧实现对数据的处理，可以有效降低数据传输时延，减轻网络传输带宽压力，提高数据隐私安全等. 在边缘计算架构上搭建人工智能模型，进行模型的训练和推理，实现边缘的智能化，对于当前社会至关重要. 由此产生的新的跨学科领域——边缘智能（edge intelligence，EI），开始引起了广泛的关注. 全面调研了边缘智能相关研究：首先，介绍了边缘计算、人工智能的基础知识，并引出了边缘智能产生的背景、动机及挑战. 其次，分别从边缘智能所要解决的问题、边缘智能模型研究以及边缘智能算法优化3个角度对边缘智能相关技术研究展开讨论. 然后，介绍边缘智能中典型的安全问题. 最后，从智慧工业、智慧生活及智慧农业3个层面阐述其应用，并展望了边缘智能未来的发展方向和前景.
- 边缘计算 /
- 人工智能 /
- 边缘智能 /
- 深度学习 /
- 云计算
Abstract:
From smart terminal devices such as smart phones and smart watches, to large-scale intelligent applications, such as smart homes, Internet of vehicles, intelligent life and intelligent agriculture. Artificial intelligence (AI) has gradually entered and changed the life of human being. In this context, various of intelligent devices have produced massive amount of data, making traditional cloud computing paradigm unable to adapt to the unprecedented challenge. Instead, edge computing which aims to process the data at the edge of the network has the great potential to reduce latency and bandwidth pressure, as well as protect data privacy and security. Building AI models upon edge computing architecture, training and inferring the model, realizing the intelligence of the edge are crucial to the current social. As a result, a new interdisciplinary field, edge intelligence (EI), has begun to attract widespread attention. We make a comprehensive study on EI. Specifically, firstly introduce the basic knowledge of edge computing and AI, which leads to the background, motivation and challenges of EI. Secondly, the research on EI related technologies is discussed from three aspects, namely, the problems, the models and the algorithm. Further, the typical security problems in EI are introduced. Next, the applications of EI are described from three aspects of intelligent industry, intelligent life and intelligent agriculture. Finally, we propose the direction and prospect of EI in the future development.
- edge computing /
- artificial intelligence (AI) /
- edge intelligence (EI) /
- deep learning /
- cloud computing

HTML全文

图 1 云计算模型

Figure 1. Cloud computing paradigm

下载: 全尺寸图片幻灯片

图 2 边缘计算模型

Figure 2. Edge computing paradigm

下载: 全尺寸图片幻灯片

图 3 边缘智能的体系架构

Figure 3. Architecture of edge intelligence

下载: 全尺寸图片幻灯片

图 4 整体技术路线图

Figure 4. Overall technical route map

下载: 全尺寸图片幻灯片

图 5 边缘智能图像识别系统

Figure 5. Edge intelligence image recognition system

下载: 全尺寸图片幻灯片

图 6 卷积神经网络模型结构

Figure 6. Structure of CNN model

下载: 全尺寸图片幻灯片

表 1 云计算、边缘计算和边缘智能特点对比

Table 1 Features Comparison of Cloud Computing, Edge Computing , and Edge Intelligence

类别	云计算	边缘计算	边缘智能
架构模型	集中式	分布式	分布式
服务器位置	互联网中	边缘网络中	云—边—端协同网络
目标应用	互联网应用	物联网或移动应用	各种智能应用程序
服务类型	全球信息服务	有限的本地化信息服务	低延时、高可靠的智能服务
设备数量	数百亿	几千万甚至几亿	数百亿甚至上千亿
研究重点	工作流调度、虚拟机管理等	计算卸载、缓存、资源分配等	在边缘侧利用AI实现数据收集、缓存、处理和分析

下载: 导出CSV

表 2 智能的边缘计算和边缘的智能化特点对比

Table 2 Features Comparison of Intelligent Edge Computing and Edge Intelligence

类别	云/边/端	智能的边缘计算	边缘的智能化
结构层面	云	服务器集群	服务器集群
	边缘	基准站、边缘节点	智能化服务
	终端	终端设备	智能化应用
内容层面		利用AI技术解决边缘计算相关问题	实现边缘环境中应用的智能化

下载: 导出CSV

表 3 智能的边缘计算相关工作分类

Table 3 Related Work Classification for Intelligent Edge Computing

关键技术	适用场景	问题挑战	优化目标	相应算法	数据来源
计算卸载	车联网	高度动态的车辆拓扑结构.	优化卸载决策和带宽/计算资源分配	深度学习	文献[56]
	无人机	无人机与终端用户之间的计算和信道容量有限.	最小化延迟和能耗	深度学习	文献[57]
	设备到设备卸载	数据卸载过程效果不稳定.	优化用户体验	强化学习	文献[58]
	物联网设备	嵌入式设备处理能力及资源受限；	降低了DNN推理的总延迟	深度学习	文献[59]
	物联网设备	设备之间的对抗性竞争；低延时通信约束.	最小化延迟和通信成本	深度强化学习	文献[60]
资源分配	多用户资源约束条件	单个边缘服务器资源受限.	最小化延迟、提高系统实时性	深度学习	文献[61]
	移动设备	边缘计算环境复杂多变.	保持MEC架构在不同条件下的稳定性	深度强化学习	文献[46]
	车辆边缘计算网络	车辆动态变化.	最大化车辆边缘计算网络的长期效用	深度强化学习	文献[62]
	工业物联网	频谱资源有限；电池容量受限.	最大化长期吞吐量	深度学习	文献[63]
	无线网络	框架节点之间达成共识的同时保证系统的性能.	最大化系统吞吐量和用户的服务质量	深度强化学习	文献[64]
边缘缓存	边缘计算系统	无线信道的拥塞.	最小化系统成本消耗系统性能最优	深度强化学习	文献[36]
	车联网	车辆移动性；	最大化系统效用	深度强化学习	文献[65]
		动态网络拓扑；存储容量和带宽资源有限；	最小化系统成本和延迟	深度强化学习	文献[66]
		主动缓存的时间变化性；	提高模型性能预测准确率	深度学习	文献[67]
		车辆的高移动性.	以最大限度地降低能耗	深度强化学习	文献[68]

下载: 导出CSV

表 4 OpenEI和Edgent的特点对比

Table 4 Features Comparison of OpenEI and Edgent

类别	OpenEI	Edgent
可部署的硬件环境	树莓派和集群计算机	树莓派和台式机
适用环境	各种操作系统	静态和动态网络
优化目标	最大化模型准确率	最小化延迟
特点	易于安装、可跨平台使用	超低延时、超高稳定性及可靠性
功能	为边缘提供智能处理和数据共享功能	按需DNN协作推理

下载: 导出CSV

参考文献(141)

[1]	Cisco U. Cisco annual internet report, 2018—2023, white paper, [EB/OL]. 2020[2022-02-19]. https://www.cisco.com/c/en/us/solutions/colla teral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html
[2]	Shepherd J, Burian S. Detection of urban-induced rainfall anomalies in a major coastal city[J]. Earth Interactions, 2003, 7(4): 1−17 doi: 10.1175/1087-3562(2003)007<0001:DOUIRA>2.0.CO;2
[3]	施巍松,孙辉,曹杰,等. 边缘计算:万物互联时代新型计算模型[J]. 计算机研究与发展,2017,54(5):907−924 doi: 10.7544/issn1000-1239.2017.20160941 Shi Weisong, Sun Hui, Cao Jie, et al. Edge computing—An emerging computing model for the Internet of everything era[J]. Journal of Computer Research and Development, 2017, 54(5): 907−924 (in Chinese) doi: 10.7544/issn1000-1239.2017.20160941
[4]	Patel M, Naughton B, Chan C, et al. Mobile-edge computing—Introductory technical white paper[J]. White Paper, Mobile-edge Computing Industry Initiative, 2014, 29: 854−864
[5]	Shi Weisong, Cao Jie, Zhang Quan, et al. Edge computing: Vision and challenges[J]. IEEE Internet of Things Journal, 2016, 3(5): 637−646 doi: 10.1109/JIOT.2016.2579198
[6]	施巍松,张星洲,王一帆,等. 边缘计算:现状与展望[J]. 计算机研究与发展,2019,56(1):69−89 doi: 10.7544/issn1000-1239.2019.20180760 Shi Weisong, Zhang Xingzhou, Wang Yifan, et al. Edge computing: State-of-the-art and future directions[J]. Journal of Computer Research and Development, 2019, 56(1): 69−89 (in Chinese) doi: 10.7544/issn1000-1239.2019.20180760
[7]	Wang Xiaofei, Han Yiwen, Leung V, et al. Convergence of edge computing and deep learning: A comprehensive survey[J]. IEEE Communications Surveys & Tutorials, 2020, 22(2): 869−904
[8]	Zhou Zhi, Chen Xu, Li En, et al. Edge intelligence: Paving the last mile of artificial intelligence with edge computing[J]. Proceedings of the IEEE, 2019, 107(8): 1738−1762 doi: 10.1109/JPROC.2019.2918951
[9]	Plastiras G, Terzi M, Kyrkou C, et al. Edge intelligence: Challenges and opportunities of near-sensor machine learning applications[C/OL] //Proc of the 29th Int Conf on Application Specific Systems, Architectures and Processors (ASAP). Piscataway, NJ: IEEE, 2018[2022-02-19]. https://ieee xplore.ieee.org/abstract/document/8445118
[10]	Rausch T, Dustdar S. Edge intelligence: The convergence of humans, things, and AI[C] //Proc of the 2019 IEEE Int Conf on Cloud Engineering (IC2E). Piscataway, NJ: IEEE, 2019: 86−96
[11]	Parekh B, Amin K. Edge intelligence: A robust reinforcement of edge computing and artificial intelligence[C] //Proc of Innovations in Information and Communication Technologies (IICT-2020). Berlin: Springer, 2021: 461−468
[12]	Munir A, Blasch E, Kwon J, et al. Artificial intelligence and data fusion at the edge[J]. IEEE Aerospace and Electronic Systems Magazine, 2021, 36(7): 62−78 doi: 10.1109/MAES.2020.3043072
[13]	Dillon T, Wu Chen, Chang E. Cloud computing: Issues and challenges [C] //Proc of the 24th IEEE Int Conf on Advanced Information Networking and Applications. Piscataway, NJ: IEEE, 2010: 27−33
[14]	Khan W, Ahmed E, Hakak S, et al. Edge computing: A survey[J]. Future Generation Computer Systems, 2019, 97: 219−235 doi: 10.1016/j.future.2019.02.050
[15]	Xu Dianlei, Li Tong, Li Yong, et al. Edge intelligence: Empowering intelligence to the edge of network[J]. Proceedings of the IEEE, 2021, 109(11): 1778−1837 doi: 10.1109/JPROC.2021.3119950
[16]	Yi Shanhe, Hao Zijiang, Qin Zhengrui, et al. Fog computing: Platform and applications[C] //Proc of the 3rd IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb). Piscataway, NJ: IEEE, 2015: 73−78
[17]	Meng Jiaying, Tan Haisheng, Xu Chao, et al. Dedas: Online task dispatching and scheduling with bandwidth constraint in edge computing[C] //Proc of IEEE Conf on Computer Communications (INFOCOM 2019). Piscataway, NJ: IEEE, 2019: 2287−2295
[18]	Sun Yaping, Chen Zhiyong, Tao Meixia, et al. Bandwidth gain from mobile edge computing and caching in wireless multicast systems[J]. IEEE Transactions on Wireless Communications, 2020, 19(6): 3992−4007 doi: 10.1109/TWC.2020.2979147
[19]	Xu Xiaolong, Xue Yuan, Qi Lianyong, et al. An edge computing-enabled computation offloading method with privacy preservation for Internet of connected vehicles[J]. Future Generation Computer Systems, 2019, 96: 89−100 doi: 10.1016/j.future.2019.01.012
[20]	林永青. 人工智能起源处的“群星”[J]. 金融博览,2017(5):46−47 doi: 10.3969/j.issn.1673-4882.2017.05.021 Lin Yongqing. "Stars" at the origin of artificial intelligence[J]. Financial View, 2017(5): 46−47 (in Chinese) doi: 10.3969/j.issn.1673-4882.2017.05.021
[21]	崔雍浩,商聪,陈锶奇,等. 人工智能综述:AI的发展[J]. 无线电通信技术,2019,45(3):225−231 doi: 10.3969/j.issn.1003-3114.2019.03.01 Cui Yonghao, Shang Cong, Chen Siqi, et al. Overview of artificial intelligence: The development of AI[J]. Radio Communication Technology, 2019, 45(3): 225−231 (in Chinese) doi: 10.3969/j.issn.1003-3114.2019.03.01
[22]	Qiu Junfei, Wu Qihui, Ding Guoru, et al. A survey of machine learning for big data processing[J]. EURASIP Journal on Advances in Signal Processing, 2016, 2016(1): 1−16 doi: 10.1186/s13634-015-0293-z
[23]	Adam B, Smith I. Reinforcement learning for structural control[J]. Journal of Computing in Civil Engineering, 2008, 22(2): 133−139 doi: 10.1061/(ASCE)0887-3801(2008)22:2(133)
[24]	Arulkumaran K, Deisenroth M P, Brundage M, et al. Deep reinforcement learning: A brief survey[J]. IEEE Signal Processing Magazine, 2017, 34(6): 26−38 doi: 10.1109/MSP.2017.2743240
[25]	LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436−444 doi: 10.1038/nature14539
[26]	Pouyanfar S, Sadiq S, Yan Yilin, et al. A survey on deep learning: Algorithms, techniques, and applications[J]. ACM Computing Surveys, 2018, 51(5): 1−36
[27]	McCulloch W, Pitts W. A logical calculus of the ideas immanent in nervous activity[J]. The Bulletin of Mathematical Biophysics, 1943, 5(4): 115−133 doi: 10.1007/BF02478259
[28]	Fukushima K, Miyake S, Ito T. Neocognitron: A neural network model for a mechanism of visual pattern recognition[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1983(5): 826−834
[29]	Jordan M. Serial Order: A Parallel Distributed Processing Approach[M] //Advances in Psychology. Amsterdam: Elsevier, 1997: 471−495
[30]	Hinton G, Osindero S, Teh Y. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527−1554 doi: 10.1162/neco.2006.18.7.1527
[31]	Jia Yangqing, Shelhamer E, Donahue J, et al. Caffe: Convolutional architecture for fast feature embedding[C] //Proc of the 22nd ACM Int Conf on Multimedia. New York: ACM, 2014: 675−678
[32]	Parvat A, Chavan J, Kadam S, et al. A survey of deep-learning frameworks[C/OL] //Proc of the 2017 Int Conf on Inventive Systems and Control (ICISC). Piscataway, NJ: IEEE, 2017[2022-02-19]. https://ieeexplore.ieee. org/abstract/document/8068684
[33]	Abadi M. TensorFlow: Learning functions at scale[C/OL] //Proc of the 21st ACM SIGPLAN Int Conf on Functional Programming. New York: ACM, 2016[2022-02-19]. https://dl.acm.org/doi/abs/10.1145/2951913.2976746
[34]	Qiu Chao, Wang Xiaofei, Yao Haipeng, et al. Bring intelligence among edges: A blockchain-assisted edge intelligence approach[C/OL] //Proc of IEEE Global Communications Conf (GLOBECOM 2020). Piscataway, NJ: IEEE, 2020[2022-02-19]. https://ieeexplore.ieee.org/abstract/document/9348271
[35]	Babu B, Jayashree N. Edge intelligence models for industrial IoT (IIoT)[J]. RV Journal of Science Technology Engineering Arts and Management, 2020, 1: 5−17
[36]	Wang Xiaofei, Han Yiwen, Wang Chenyang, et al. In-edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning[J]. IEEE Network, 2019, 33(5): 156−165 doi: 10.1109/MNET.2019.1800286
[37]	Wang Zhiyuan, Xu Hongli, Liu Jianchun, et al. Resource-efficient federated learning with hierarchical aggregation in edge computing[C/OL] //Proc of IEEE Conf on Computer Communications (INFOCOM 2021). Piscataway, NJ. IEEE, 2021[2022-02-19]. https://ieeexplore.ieee.org/abstra ct/document/9488756
[38]	Nguyen D, Ding Ming, Pham Q, et al. Federated learning meets blockchain in edge computing: Opportunities and challenges[J]. IEEE Internet of Things Journal, 2021, 8(1): 12806−12825
[39]	Deng Shuiguang, Zhao Hailiang, Fang Weijia, et al. Edge intelligence: The confluence of edge computing and artificial intelligence[J]. IEEE Internet of Things Journal, 2020, 7(8): 7457−7469 doi: 10.1109/JIOT.2020.2984887
[40]	Qu Guanjin, Wu Huaming, Li Ruidong, et al. DMRO: A deep meta reinforcement learning-based task offloading framework for edge-cloud computing[J]. IEEE Transactions on Network and Service Management, 2021, 18(3): 3448−3459 doi: 10.1109/TNSM.2021.3087258
[41]	Tang Ming, Wong V. Deep reinforcement learning for task offloading in mobile edge computing systems[J/OL]. IEEE Transactions on Mobile Computing, 2020[2022-02-19]. https://ieeexplore.ieee.org/abstract/docume nt/9253665
[42]	Zhang Ke, Zhu Yongxu, Leng Supeng, et al. Deep learning empowered task offloading for mobile edge computing in urban informatics[J]. IEEE Internet of Things Journal, 2019, 6(5): 7635−7647 doi: 10.1109/JIOT.2019.2903191
[43]	Deng Xiaoheng, Yin Jian, Guan Peiyuan, et al. Intelligent delay-aware partial computing task offloading for multi-user industrial Internet of things through edge computing[J/OL]. IEEE Internet of Things Journal, 2021[2022-02-19]. https://ieeexplore.ieee.org/abstract/document/9590527
[44]	Wu Huaming, Zhang Ziru, Guan Chang, et al. Collaborate edge and cloud computing with distributed deep learning for smart city Internet of things[J]. IEEE Internet of Things Journal, 2020, 7(9): 8099−8110 doi: 10.1109/JIOT.2020.2996784
[45]	Huang Hui, Ye Qiang, Du Hongwei. Reinforcement learning based offloading for realtime applications in mobile edge computing [C/OL] //Proc of IEEE Int Conf on Communications (ICC 2020). Piscataway, NJ: IEEE, 2020[2022-02-19]. https://ieeexplore.ieee.org/abstract/document/9148748
[46]	Wang Jiadai, Zhao Lei, Liu Jiajia, et al. Smart resource allocation for mobile edge computing: A deep reinforcement learning approach[J]. IEEE Transactions on Emerging Topics in Computing, 2019, 9(3): 1529−1541
[47]	He Ying, Wang Yuhang, Qiu Chao, et al. Blockchain-based edge computing resource allocation in IoT: A deep reinforcement learning approach[J]. IEEE Internet of Things Journal, 2020, 8(4): 2226−2237
[48]	Dai Yueyue, Zhang Ke, Maharjan S, et al. Edge intelligence for energy-efficient computation offloading and resource allocation in 5G beyond[J]. IEEE Transactions on Vehicular Technology, 2020, 69(10): 12175−12186 doi: 10.1109/TVT.2020.3013990
[49]	Lin Zehong, Bi Suzhi, Zhang Yingjun. Optimizing AI service placement and resource allocation in mobile edge intelligence systems[J]. IEEE Transactions on Wireless Communications, 2021, 20(11): 7257−7271 doi: 10.1109/TWC.2021.3081991
[50]	Yu Zhengxin, Hu Jia, Min Geyong, et al. Mobility-aware proactive edge caching for connected vehicles using federated learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 22(8): 5341−5351
[51]	Zhang Ke, Leng Supeng, He Yejun, et al. Cooperative content caching in 5G networks with mobile edge computing[J]. IEEE Wireless Communications, 2018, 25(3): 80−87 doi: 10.1109/MWC.2018.1700303
[52]	Ning Zhaolong, Zhang Kaiyuan, Wang Xiaojie, et al. Joint computing and caching in 5G-envisioned Internet of vehicles: A deep reinforcement learning-based traffic control system[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 22(8): 5201−5212
[53]	Ning Zhaolong, Zhang Kaiyuan, Wang Xiaojie, et al. Intelligent edge computing in Internet of vehicles: A joint computation offloading and caching solution[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 22(4): 2212−2225
[54]	Ndikumana A, Tran N, Kim K, et al. Deep learning based caching for self-driving cars in multi-access edge computing[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 22(5): 2862−2877
[55]	Zhang Ran, Yu F, Liu Jiang, et al. Deep reinforcement learning (DRL)-based device-to-device caching with blockchain and mobile edge computing[J]. IEEE Transactions on Wireless Communications, 2020, 19(10): 6469−6485 doi: 10.1109/TWC.2020.3003454
[56]	Dai Penglin, Hu Kaiwen, Wu Xiao, et al. Asynchronous deep reinforcement learning for data-driven task offloading in MEC-empowered vehicular networks [C/OL] //Proc of IEEE Conf on Computer Communications (INFOCOM 2021). Piscataway, NJ: IEEE, 2021[2022-02-19]. https://ieeexplore.ieee.org/document/9488886
[57]	Mukherjee M, Kumar V, Lat A, et al. Distributed deep learning-based task offloading for UAV-enabled mobile edge computing[C] //Proc of IEEE Conf on Computer Communications Workshops (INFOCOM 2020). Piscataway, NJ: IEEE, 2020: 1208−1212
[58]	Liu Xianming, Zhang Chaokun, He Shen. Adaptive task offloading for mobile aware applications based on deep reinforcement learning[C] //Proc of the 19th Int Conf on mobile Ad Hoc and Smart Systems (MASS). Piscataway, NJ: IEEE, 2022, accepted
[59]	Mohammed T, Joe-Wong C, Babbar R, et al. Distributed inference acceleration with adaptive DNN partitioning and offloading [C] //Proc of the IEEE Conf on Computer Communications (INFOCOM 2020). Piscataway, NJ: IEEE, 2020: 854−863
[60]	Zhou Zhenyu, Wang Zhao, Yu Haijun, et al. Learning-based URLLC-aware task offloading for Internet of health things[J]. IEEE Journal on Selected Areas in Communications, 2020, 39(2): 396−410
[61]	Tang Xin, Chen Xu, Zeng Liekang, et al. Joint multiuser DNN partitioning and computational resource allocation for collaborative edge intelligence[J]. IEEE Internet of Things Journal, 2020, 8(12): 9511−9522
[62]	Liu Yi, Yu Huimin, Xie Shengli, et al. Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(11): 11158−11168 doi: 10.1109/TVT.2019.2935450
[63]	Liao Haijun, Zhou Zhenyu, Zhao Xiongwen, et al. Learning-based context-aware resource allocation for edge-computing-empowered industrial IoT[J]. IEEE Internet of Things Journal, 2019, 7(5): 4260−4277
[64]	Guo Fengxian, Yu F, Zhang Heli, et al. Adaptive resource allocation in future wireless networks with blockchain and mobile edge computing[J]. IEEE Transactions on Wireless Communications, 2019, 19(3): 1689−1703
[65]	Dai Yueyue, Xu Du, Maharjan S, et al. Artificial intelligence empowered edge computing and caching for Internet of vehicles[J]. IEEE Wireless Communications, 2019, 26(3): 12−18 doi: 10.1109/MWC.2019.1800411
[66]	Qiao Guanhua, Leng Supeng, Maharjan S, et al. Deep reinforcement learning for cooperative content caching in vehicular edge computing and networks[J]. IEEE Internet of Things Journal, 2019, 7(1): 247−257
[67]	Ale L, Zhang Ning, Wu Huici, et al. Online proactive caching in mobile edge computing using bidirectional deep recurrent neural network[J]. IEEE Internet of Things Journal, 2019, 6(3): 5520−5530 doi: 10.1109/JIOT.2019.2903245
[68]	Khan L U, Yaqoob I, Tran N H, et al. Edge-computing-enabled smart cities: A comprehensive survey[J]. IEEE Internet of Things Journal, 2020, 7(10): 10200−10232 doi: 10.1109/JIOT.2020.2987070
[69]	Nakahara M, Hisano D, Nishimura M, et al. Retransmission edge computing system conducting adaptive image compression based on image recognition accuracy[C/OL] //Proc of the 94th IEEE Vehicular Technology Conf (VTC2021-Fall). Piscataway, NJ: IEEE, 2021[2022-02-19]. https://iee explore.ieee.org/document/9625464
[70]	Muhammad G, Hossain M. Emotion recognition for cognitive edge computing using deep learning[J]. IEEE Internet of Things Journal, 2021, 8(23): 16894−16901 doi: 10.1109/JIOT.2021.3058587
[71]	Shen Tao, Gao Chan, Xu Dawei. The analysis of intelligent real-time image recognition technology based on mobile edge computing and deep learning[J]. Journal of Real-Time Image Processing, 2021, 18(4): 1157−1166 doi: 10.1007/s11554-020-01039-x
[72]	Monburinon N, Zabir S, Vechprasit N, et al. A novel hierarchical edge computing solution based on deep learning for distributed image recognition in IoT systems[C] //Proc of the 4th Int Conf on Information Technology (InCIT). Piscataway, NJ: IEEE, 2019: 294−299
[73]	Wu Juan. English real-time speech recognition based on hidden markov and edge computing model[C] //Proc of the 3rd Int Conf on Inventive Research in Computing Applications (ICIRCA). Piscataway, NJ: IEEE, 2021: 376−379
[74]	Xu Yunwei. Research on business English translation architecture based on artificial intelligence speech recognition and edge computing[J/OL]. Wireless Communications and Mobile Computing, 2021[2022-02-19]. https://www.hindawi.com/journals/wcmc/2021/5518868/
[75]	Cheng Shitong, Xu Zhenghui, Li Xiuhua, et al. Task Offloading for automatic speech recognition in edge-cloud computing based mobile networks[C/OL] //Proc of IEEE Symp on Computers and Communications (ISCC). Piscataway, NJ: IEEE, 2020[2022-02-19]. https://ieeexplore.ieee.org/abstract/document/9219579
[76]	Zeng Xiao, Fang Biyi, Shen Haichen, et al. Distream: Scaling live video analytics with workload-adaptive distributed edge intelligence [C] //Proc of the 18th Conf on Embedded Networked Sensor Systems. New York: ACM, 2020: 409−421
[77]	Rocha N, Silva T, Batista T, et al. Leveraging edge intelligence for video analytics in smart city applications [J]. Information, 2021, 12(1): 14
[78]	Zhang Xingzhou, Wang Yifan, Lu Sidi, et al. OpenEI: An open framework for edge intelligence[C] //Proc of the 39th IEEE Int Conf on Distributed Computing Systems (ICDCS). Piscataway, NJ: IEEE, 2019: 1840−1851
[79]	Li En, Zeng Liekang, Zhou Zhi, et al. Edge AI: On-demand accelerating deep neural network inference via edge computing[J]. IEEE Transactions on Wireless Communications, 2019, 19(1): 447−457
[80]	Erfanian A, Amirpour H, Tashtarian F, et al. LwTE-Live: Light-weight transcoding at the edge for live streaming[C] //Proc of the Workshop on Design, Deployment, and Evaluation of Network-assisted Video Streaming. New York: ACM, 2021: 22−28
[81]	Jin Yibo, Jiao Lei, Qian Zhuzhong, et al. Learning for Learning: Predictive online control of federated learning with edge provisioning [C/OL] //Proc of the IEEE Conf on Computer Communications (INFOCOM 2021). Piscataway, NJ: IEEE, 2021[2022-02-19]. https://ieeexplore.ieee.org/abstra ct/document/9488733
[82]	Zhang D, Kou Ziyi, Wang Dong. Fedsens: A federated learning approach for smart health sensing with class imbalance in resource constrained edge computing[C/OL] //Proc of the IEEE Conf on Computer Communications (INFOCOM 2021). Piscataway, NJ: IEEE, 2021[2022-02-19]. https://ieeexplore.ieee.org/document/9488776
[83]	Wang Shuangguang, Guo Yan, Zhang Ning, et al. Delay-aware microservice coordination in mobile edge computing: A reinforcement learning approach[J]. IEEE Transactions on Mobile Computing, 2019, 20(3): 939−951
[84]	Bahreini T, Badri H, Grosu D. Mechanisms for resource allocation and pricing in mobile edge computing systems[J]. IEEE Transactions on Parallel and Distributed Systems, 2021, 33(3): 667−682
[85]	Tran T, Pompili D. Adaptive bitrate video caching and processing in mobile-edge computing networks[J]. IEEE Transactions on Mobile Computing, 2018, 18(9): 1965−1978
[86]	Xing Hong, Liu Liang, Xu Jie, et al. Joint task assignment and resource allocation for D2D-enabled mobile-edge computing[J]. IEEE Transactions on Communications, 2019, 67(6): 4193−4207 doi: 10.1109/TCOMM.2019.2903088
[87]	Zhang Chaokun, Zheng Rong, Cui Yong, et al. Delay-sensitive computation partitioning for mobile augmented reality applications[C/OL] //Proc of the 28th Int Symp on Quality of Service (IWQoS). Piscataway, NJ: IEEE, 2020[2022-02-19]. https://ieeexplore.ieee.org/docu ment/9212917
[88]	Song Fuhong, Xing Huanlai, Luo Shouxi, et al. A multiobjective computation offloading algorithm for mobile-edge computing[J]. IEEE Internet of Things Journal, 2020, 7(9): 8780−8799 doi: 10.1109/JIOT.2020.2996762
[89]	Lei Lei, Xu Huijuan, Xiong Xiong, et al. Joint computation offloading and multiuser scheduling using approximate dynamic programming in NB-IoT edge computing system[J]. IEEE Internet of Things Journal, 2019, 6(3): 5345−5362 doi: 10.1109/JIOT.2019.2900550
[90]	Littman M, Moore A. Reinforcement learning: A survey[J]. Journal of Artificial Intelligence Research, 1996, 4(1): 237−285
[91]	Wang Jin, Hu Jia, Min Geyong, et al. Fast adaptive task offloading in edge computing based on meta reinforcement learning[J]. IEEE Transactions on Parallel and Distributed Systems, 2020, 32(1): 242−253
[92]	Huisman M, Van R, Plaat A. A survey of deep meta-learning[J]. Artificial Intelligence Review, 2021, 54(6): 4483−4541 doi: 10.1007/s10462-021-10004-4
[93]	Zhang Ke, Cao Jiayu, Zhang Yan. Adaptive digital twin and multiagent deep reinforcement learning for vehicular edge computing and networks[J]. IEEE Transactions on Industrial Informatics, 2021, 18(2): 1405−1413
[94]	Dai Yueyue, Xu Du, Zhang Ke, et al. Deep reinforcement learning and permissioned blockchain for content caching in vehicular edge computing and networks[J]. IEEE Transactions on Vehicular Technology, 2020, 69(4): 4312−4324 doi: 10.1109/TVT.2020.2973705
[95]	Liang Fan, Yu Wei, Liu Xing, et al. Toward edge-based deep learning in industrial Internet of things[J]. IEEE Internet of Things Journal, 2020, 7(5): 4329−4341 doi: 10.1109/JIOT.2019.2963635
[96]	Guillén M, Llanes A, Imbernón B, et al. Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning[J]. The Journal of Supercomputing, 2021, 77(1): 818−840 doi: 10.1007/s11227-020-03288-w
[97]	Zhao Ning, Wu Hao, Yu F, et al. Deep-reinforcement-learning-based latency minimization in edge intelligence over vehicular networks[J]. IEEE Internet of Things Journal, 2021, 9(2): 1300−1312
[98]	Li Bo, He Qiang, Chen Feifei, et al. Inspecting edge data integrity with aggregated signature in distributed edge computing environment [J/OL]. IEEE Transactions on Cloud Computing, 2021[2022-02-19]. https://ieeexplore.ieee.org/abstract/document/9354962
[99]	Li Yannan, Yu Yong, Susilo W, et al. Security and privacy for edge intelligence in 5G and beyond networks: Challenges and solutions[J]. IEEE Wireless Communications, 2021, 28(2): 63−69 doi: 10.1109/MWC.001.2000318
[100]	Li Bo, He Qiang, Chen Feifei, et al. Auditing cache data integrity in the edge computing environment[J]. IEEE Transactions on Parallel and Distributed Systems, 2020, 32(5): 1210−1223
[101]	Cui Guangming, He Qiang, Li Bo, et al. Efficient verification of edge data integrity in edge computing environment [J/OL]. IEEE Transactions on Services Computing, 2021[2022-02-19]. https://ieeexplore.ieee.org/abstract /document/9459478
[102]	Li Bo, He Qiang, Chen Feifei, et al. Cooperative assurance of cache data integrity for mobile edge computing[J]. IEEE Transactions on Information Forensics and Security, 2021, 16: 4648−4662 doi: 10.1109/TIFS.2021.3111747
[103]	Tong Wei, Jiang Bingbing, Xu Fengyuan, et al. Privacy-preserving data integrity verification in mobile edge computing [C] //Proc of the 39th Int Conf on Distributed Computing Systems (ICDCS). Piscataway, NJ: IEEE, 2019: 1007−1018
[104]	Xu Rongxu, Hang Lei, Jin Wenquan, et al. Distributed secure edge computing architecture based on blockchain for real-time data integrity in IoT environments[J]. Actuators, 2021, 10(8): 197−212 doi: 10.3390/act10080197
[105]	Shanthamallu U, Thiagarajan J, Spanias A. Uncertainty-matching graph neural networks to defend against poisoning attacks [C] //Proc of the 35th AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2021, 9524−9532
[106]	Alfeld S, Zhu Xiaojin, Barford P. Data poisoning attacks against autoregressive models [C/OL] //Proc of the 30th AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2016[2022-02-19]. https://ojs.aaai.org/index.php/AA AI/article/view/10237
[107]	Jagielski M, Oprea A, Biggio B, et al. Manipulating machine learning: Poisoning attacks and countermeasures for regression learning [C] //Proc of the 2018 IEEE Symp on Security and Privacy (SP). Piscataway, NJ: IEEE, 2018: 19−35
[108]	Steinhardt J, Koh P, Liang P. Certified defenses for data poisoning attacks [J/OL]. Advances in Neural Information Processing Systems, 2017[2022-02-19]. https://proceedings.neurips.cc/paper/2017/hash/9d7311ba459f9e45ed746755a32dcd11-Abstract.html
[109]	Tolpegin V, Truex S, Gursoy M, et al. Data poisoning attacks against federated learning systems[C] //Proc of the European Symp on Research in Computer Security. Berlin: Springer, 2020: 480−501
[110]	Douceur J. The sybil attack[C] //Proc of the Int Workshop on Peer-to-Peer Systems. Berlin: Springer, 2002: 251−260
[111]	Doku R, Rawat D. Mitigating data poisoning attacks on a federated learning-edge computing network[C/OL] //Proc of the 18th Annual Consumer Communications & Networking Conf (CCNC). Piscataway, NJ: IEEE, 2021[2022-02-19]. https://ieeexplore.ieee.org/abstract/document/9369581
[112]	Xiao Liang, Wan Xiaoyue, Dai Canhuang, et al. Security in mobile edge caching with reinforcement learning[J]. IEEE Wireless Communications, 2018, 25(3): 116−122 doi: 10.1109/MWC.2018.1700291
[113]	Jia Xiaoying, He Debiao, Kumar N, et al. A provably secure and efficient identity-based anonymous authentication scheme for mobile edge computing[J]. IEEE Systems Journal, 2019, 14(1): 560−571
[114]	Kaur K, Garg S, Kaddoum G, et al. A lightweight and privacy-preserving authentication protocol for mobile edge computing [C/OL] //Proc of IEEE Global Communications Conf (GLOBECOM 2019). Piscataway, NJ: IEEE, 2019[2022-02-19]. https://ieeexplore.ieee.org/abstract/document/9013856
[115]	Gupta R, Reebadiya D, Tanwar S, et al. When blockchain meets edge intelligence: Trusted and security solutions for consumers[J]. IEEE Network, 2021, 35(5): 272−278 doi: 10.1109/MNET.001.2000735
[116]	Ding Jiahao, Liang Guannan, Bi Jinbo, et al. Differentially private and communication efficient collaborative learning [C/OL] //Proc of the 35th AAAI Conf on Artificial Intelligence, Virtual Conf. Palo Alto, CA: AAAI, 2021[2022-02-19]. https://ojs.aaai.org/index.php/AAAI/article/view/16887
[117]	Wang Ji, Bao Weidong, Sun Lichao, et al. Private model compression via knowledge distillation [C] //Proc of the 33rd AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2019: 1190−1197
[118]	Abadi M, Chu A, Goodfellow I, et al. Deep learning with differential privacy [C] //Proc of the 2016 ACM SIGSAC Conf on Computer and Communications Security (CCS’16). New York: ACM, 2016: 308−318
[119]	Li He, Ota K, Dong Miaoxiong. Learning IoT in edge: Deep learning for the Internet of things with edge computing[J]. IEEE network, 2018, 32(1): 96−101
[120]	Yang Qiang, Liu Yang, Cheng Yong, et al. Federated learning[J]. Synthesis Lectures on Artificial Intelligence and Machine Learning, 2019, 13(3): 1−207 doi: 10.1007/978-3-031-01585-4
[121]	Liu Yi, Zhao Ruihui, Kang Jiawen, et al. Towards communication-efficient and attack-resistant federated edge learning for industrial internet of things[J]. ACM Transactions on Internet Technology, 2021, 22(3): 1−22
[122]	Liu Yi, James J, Kang Jiawen, et al. Privacy-preserving traffic flow prediction: A federated learning approach[J]. IEEE Internet of Things Journal, 2020, 7(8): 7751−7763 doi: 10.1109/JIOT.2020.2991401
[123]	Liu Yi, Yuan Xingliang, Xiong Zehui, et al. Federated learning for 6G communications: Challenges, methods, and future directions[J]. China Communications, 2020, 17(9): 105−118 doi: 10.23919/JCC.2020.09.009
[124]	Li Tian, Sahu A, Talwalkar A, et al. Federated learning: Challenges, methods, and future directions[J]. IEEE Signal Processing Magazine, 2020, 37(3): 50−60 doi: 10.1109/MSP.2020.2975749
[125]	Thalluri L, Venkat S, Prasad C, et al. Artificial intelligence enabled smart city IoT system using edge computing [C] //Proc of the 2nd Int Conf on Smart Electronics and Communication (ICOSEC). Piscataway, NJ: IEEE, 2021: 12−20
[126]	Nasir M, Muhammad K, Ullah A, et al. Enabling automation and edge intelligence over resource constraint IoT devices for smart home [J/OL]. Neurocomputing, 2021[2022-02-19]. https://www.sciencedirect.com/scienc e/article/abs/pii/S0925231221016301
[127]	Sadiku M, Tembely M, Musa S. Internet of vehicles: An introduction [J]. Internet Journal of Advanced Research in Computer Science and Software Engineering, 2018, 8(1): 11
[128]	Grover H, Alladi T, Chamola V, et al. Edge computing and deep learning enabled secure multitier network for Internet of vehicles[J]. IEEE Internet of Things Journal, 2021, 8(19): 14787−14796 doi: 10.1109/JIOT.2021.3071362
[129]	Zhang Qingyang, Wang Yifan, Zhang Xingzhou, et al. OpenVDAP: An open vehicular data analytics platform for CAVs[C] //Proc of the 38th IEEE Int Conf on Distributed Computing Systems (ICDCS). Piscataway, NJ: IEEE, 2018: 1310−1320
[130]	Lv Zhihan, Chen Dongliang, Wang Qingjun. Diversified technologies in Internet of vehicles under intelligent edge computing[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 22(4): 2048−2059
[131]	Li Tailai, Zhang Chaokun, Zhou Xiaobo. BP-CODS: Blind-spot-prediction-assisted multi-vehicle collaborative data scheduling [C/OL] //Proc of the 17th Int Conf on Wireless Algorithms, Systems, and Applications (WASA 2022). Berlin: Springer, 2022[2022-02-19]. https://link.springer.com/chapter/10.1007/978-3-031-19211-1_25
[132]	Liu Jia, Xiang Jianjian, Jin Yongjun, et al. Boost precision agriculture with unmanned aerial vehicle remote sensing and edge intelligence: A survey [J]. Remote Sensing, 2021, 13(21): 4387
[133]	Tyagi A. Towards a second green revolution[J]. Irrigation and Drainage, 2016, 4(65): 388−389
[134]	Kamilaris A, Prenafeta-Boldú F. Deep learning in agriculture: A survey[J]. Computers and electronics in agriculture, 2018, 147: 70−90 doi: 10.1016/j.compag.2018.02.016
[135]	Dionisio J, William G, Gilbert R. 3D virtual worlds and the metaverse: Current status and future possibilities[J]. ACM Computing Surveys, 2013, 45(3): 1−38
[136]	Duan Haihan, Li Jiaye, Fan Sizheng, et al. Metaverse for social good: A university campus prototype [C] //Proc of the 29th ACM Int Conf on Multimedia. New York: ACM, 2021: 153−161
[137]	Zhang Zhengquan, Xiao Yue, Ma Zheng, et al. 6G wireless networks: Vision, requirements, architecture, and key technologies[J]. IEEE Vehicular Technology Magazine, 2019, 14(3): 28−41 doi: 10.1109/MVT.2019.2921208
[138]	You Xiaohu, Wang Chengxiang, Huang Jie, et al. Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts[J]. Science China Information Sciences, 2021, 64(1): 1−74
[139]	Dai Hongning, Wu Yulei, Wang Hao, et al. Blockchain-empowered edge intelligence for Internet of medical things against COVID-19[J]. IEEE Internet of Things Magazine, 2021, 4(2): 34−39 doi: 10.1109/IOTM.0011.2100030
[140]	Han Tao, Ansari N. On optimizing green energy utilization for cellular networks with hybrid energy supplies[J]. IEEE Transactions on Wireless Communications, 2013, 12(8): 3872−3882 doi: 10.1109/TCOMM.2013.051313.121249
[141]	Quan Li, Huang Qin, Zhang Shengli, et al. Downsampling blockchain algorithm[C] //Proc of the IEEE Conf on Computer Communications Workshops (INFOCOM WKSHPS 2019). Piscataway, NJ: IEEE, 2019: 342−347

施引文献(20)

期刊类型引用(7)

1.	毛伊敏，甘德瑾，廖列法，陈志刚. 基于Spark框架和ASPSO的并行划分聚类算法. 通信学报. 2022(03): 148-163 . 百度学术
2.	王永贵，林佳敏，何佳玉. 融合领导者影响与隐式信任度的群组推荐方法. 计算机工程与应用. 2022(09): 98-106 . 百度学术
3.	刘鑫，梅红岩，王嘉豪，李晓会. 图神经网络推荐方法研究. 计算机工程与应用. 2022(10): 41-49 . 百度学术
4.	刘聪，谢莉，杨慧中. 基于改进DPC的青霉素发酵过程多模型软测量建模. 化工学报. 2021(03): 1606-1615 . 百度学术
5.	刘功民，朱俊杰. WSN中利用双重接收器结合自适应加权数据融合的簇首优化聚类算法. 计算机应用与软件. 2021(05): 145-151 . 百度学术
6.	任昌鸿，安军. 改进PSO结合DSA技术的无线传感器网络均衡密度聚类方法. 计算机应用与软件. 2020(08): 122-129 . 百度学术
7.	许晓明，梅红岩，于恒，李晓会. 基于偏好融合的群组推荐方法研究综述. 小型微型计算机系统. 2020(12): 2500-2508 . 百度学术