State-of-the-Art Survey on Edge Intelligence

Zhang Xiaodong; Zhang Chaokun; Zhao Jijun

doi:10.7544/issn1000-1239.202220192

Journal of Computer Research and Development > 2023 > 60(12): 2749-2769. > DOI: 10.7544/issn1000-1239.202220192

Zhang Xiaodong, Zhang Chaokun, Zhao Jijun. State-of-the-Art Survey on Edge Intelligence[J]. Journal of Computer Research and Development, 2023, 60(12): 2749-2769. DOI: 10.7544/issn1000-1239.202220192

Citation:

PDF (1896 KB)

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
Received Date: March 06, 2022
Revised Date: February 26, 2023
Available Online: September 19, 2023

Graphical Abstract

Abstract

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

FullText(HTML)

References (141)

References

[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

[1]	Luo Yuzhe, Li Ling, Hou Pengpeng, Yu Jiageng, Cheng Limin, Zhang Changyou, Wu Yanjun, Zhao Chen. Survey of AIoT-Oriented Collaborative Intelligence[J]. Journal of Computer Research and Development, 2025, 62(1): 179-206. DOI: 10.7544/issn1000-1239.202330975
[2]	Zhang Xueqing, Liu Yanwei, Liu Jinxia, Han Yanni. An Overview of Federated Learning in Edge Intelligence[J]. Journal of Computer Research and Development, 2023, 60(6): 1276-1295. DOI: 10.7544/issn1000-1239.202111100
[3]	Wang Rui, Qi Jianpeng, Chen Liang, Yang Long. Survey of Collaborative Inference for Edge Intelligence[J]. Journal of Computer Research and Development, 2023, 60(2): 398-414. DOI: 10.7544/issn1000-1239.202110867
[4]	Sun Bing, Liu Yan, Wang Tian, Peng Shaoliang, Wang Guojun, Jia Weijia. Survey on Optimization of Federated Learning Efficiency in Mobile Edge Networks[J]. Journal of Computer Research and Development, 2022, 59(7): 1439-1469. DOI: 10.7544/issn1000-1239.20210119
[5]	Wang Lu, Zhang Jianhao, Wang Ting, Wu Kaishun. A Fine-Grained Multi-Access Edge Computing Architecture for Cloud-Network Integration[J]. Journal of Computer Research and Development, 2021, 58(6): 1275-1290. DOI: 10.7544/issn1000-1239.2021.20201076
[6]	Wang Zhigang, Wang Haitao, She Qi, Shi Xuesong, Zhang Yimin. Robot 4.0: Continual Learning and Spatial-Temporal Intelligence Through Edge[J]. Journal of Computer Research and Development, 2020, 57(9): 1854-1863. DOI: 10.7544/issn1000-1239.2020.20200254
[7]	Huang Qianyi, Li Zhiyang, Xie Wentao, Zhang Qian. Edge Computing in Smart Homes[J]. Journal of Computer Research and Development, 2020, 57(9): 1800-1809. DOI: 10.7544/issn1000-1239.2020.20200253
[8]	Lu Haifeng, Gu Chunhua, Luo Fei, Ding Weichao, Yang Ting, Zheng Shuai. Research on Task Offloading Based on Deep Reinforcement Learning in Mobile Edge Computing[J]. Journal of Computer Research and Development, 2020, 57(7): 1539-1554. DOI: 10.7544/issn1000-1239.2020.20190291
[9]	Shi Weisong, Zhang Xingzhou, Wang Yifan, Zhang Qingyang. Edge Computing: State-of-the-Art and Future Directions[J]. Journal of Computer Research and Development, 2019, 56(1): 69-89. DOI: 10.7544/issn1000-1239.2019.20180760
[10]	Jiang Han, Xu Qiuliang. Secure Multiparty Computation in Cloud Computing[J]. Journal of Computer Research and Development, 2016, 53(10): 2152-2162. DOI: 10.7544/issn1000-1239.2016.20160685

Cited By

Cited by

Periodical cited type(12)

1.	陆玲霞，于淼，彭勇刚. 面向I~3型卓越人才培养的嵌入式人工智能实验教学探索. 实验室研究与探索. 2025(01): 85-90 .
2.	张骥，宋文付，张星. 边缘AI平台在智能制造系统中的应用. 自动化应用. 2025(04): 52-55 .
3.	高赟贤，陆军，陈振，倪国政，陈旭哲. 基于边缘AI技术的地铁运维安全管理系统创新实践. 市政技术. 2025(03): 297-302 .
4.	申秀雨，姬伟峰，李映岐，吴玄. 面向边缘计算的TCA1C DDoS检测模型. 计算机工程. 2024(01): 198-205 .
5.	叶新东，刘泽民. 基于多模态大模型的精准教学支持体系构建研究. 远程教育杂志. 2024(01): 84-93 .
6.	侯祥鹏，兰兰，陶长乐，寇小勇，丛佩金，邓庆绪，周俊龙. 边缘智能与协同计算:前沿与进展. 控制与决策. 2024(07): 2385-2404 .
7.	曹金玲. AR与BIM结合辅助项目经理现场管理的应用研究. 建筑施工. 2024(07): 1065-1068 .
8.	张雷，林子煜，李飞达，郭婧，闵丽娟. 基于环境声识别的工业设备状态监测实验系统设计. 实验室研究与探索. 2024(08): 47-51 .
9.	刘怡龙，许盛伟，岳梓岩. 密文长度固定的策略部分隐藏多授权机构CP-ABE方案. 通信学报. 2024(08): 20-36 .
10.	李东升. 探索AI大模型时代云计算与智能化融合路径. 中国信息界. 2024(05): 154-156 .
11.	韩京宇，王彦之，陈进，晏鑫鑫，张怡婷. 一种车载端为主的城市路网当前与未来速度查询方法. 电子与信息学报. 2024(09): 3722-3730 .
12.	董梦涛，潘志安. 边缘智能：多领域实时处理与智能决策的关键技术. 电脑与电信. 2024(09): 1-6 .