Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2021, 4(5); doi: 10.25236/AJCIS.2021.040514.

Application of IOT deep learning in edge computing: a review


Houchi Li

Corresponding Author:
Houchi Li

Hunan University of Sicience and Technology, Xiangtan, 411100, Hunan, China


With the development of artificial intelligence technology, deep learning is widely used as a method to extract features from complex networks. However, deep learning models often run in cloud computing data centers with powerful computing capabilities. Traditional cloud computing methods rely heavily on the network, which has high latency, and has problems of security and privacy. Edge computing complements cloud computing by performing tasks at the edge of the network, resulting in significant reductions in system operation time, memory cost, and power consumption. At the same time, because it is deployed in an edge computing environment, network performance can be optimized and user privacy can be protected. This review discusses the application of deep learning on the Internet of things(IOT) in the environment of edge computing, compares the results of  edge computing and cloud computing in the field of deep learning, shows the superiority of the edge computing. This paper introduces the commonly used method of edge computing, and at the same time puts forward the possible problems of edge computing in the field of deep learning.Finally, we make a prospect for the future in the cross field of edge computing and deep learning.


Deep Learning; Artificial Intelligence; Edge Computing; IOT

Cite This Paper

Houchi Li. Application of IOT deep learning in edge computing: a review. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 5: 98-103. https://doi.org/10.25236/AJCIS.2021.040514.


[1] He L, Ota K, Dong M. Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing[J]. IEEE Network, 2018, 32(1):96-101.

[2] Voulodimos A, Doulamis N, Doulamis A, et al. Deep learning for computer vision: A brief review[J]. Computational intelligence and neuroscience, 2018, 2018.

[3] Zhao Z Q, Zheng P, Xu S, et al. Object detection with deep learning: A review[J]. IEEE transactions on neural networks and learning systems, 2019, 30(11): 3212-3232.

[4] Wang F Y, Zhang J J, Zheng X, et al. Where does AlphaGo go: From church-turing thesis to AlphaGo thesis and beyond[J]. IEEE/CAA Journal of Automatica Sinica, 2016, 3(2): 113-120.

[5] Noda K, Yamaguchi Y, Nakadai K, et al. Audio-visual speech recognition using deep learning[J]. Applied Intelligence, 2015, 42(4): 722-737.

[6] Zhang Z, Geiger J, Pohjalainen J, et al. Deep learning for environmentally robust speech recognition: An overview of recent developments[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2018, 9(5): 1-28.

[7] Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis[J]. Medical image analysis, 2017, 42: 60-88.

[8] Mohammadi M, Al-Fuqaha A, Sorour S, et al. Deep learning for IoT big data and streaming analytics: A survey[J]. IEEE Communications Surveys & Tutorials, 2018, 20(4): 2923-2960

[9] Al-Garadi M A, Mohamed A, Al-Ali A K, et al. A survey of machine and deep learning methods for internet of things (IoT) security[J]. IEEE Communications Surveys & Tutorials, 2020, 22(3): 1646-1685.

[10] Chen J, Ran X. Deep Learning With Edge Computing: A Review[J]. Proceedings of the IEEE, 2019, PP(99):1-20.

[11] Jie T, Sun D, Liu S, et al. Enabling Deep Learning on IoT Devices[J]. Computer, 2017, 50(10):92-96.

[12] Yu W, Liang F, He X, et al. A Survey on the Edge Computing for the Internet of Things[J]. IEEE Access, 2017.

[13] Liu S, Liu L, Tang J, et al. Edge Computing for Autonomous Driving: Opportunities and Challenges[J]. Proceedings of the IEEE, 2019, PP(99):1-20.

[14] Sallab A, Abdou M, Perot E, et al. Deep Reinforcement Learning framework for Autonomous Driving[J]. Electronic Imaging, 2017, 2017(19):70-76.

[15] Liu S, Liu L, Tang J, et al. Edge Computing for Autonomous Driving: Opportunities and Challenges[J]. Proceedings of the IEEE, 2019, 107(8): 1697-1716.

[16] Dai H, Zeng X, Yu Z, et al. A Scheduling Algorithm for Autonomous Driving Tasks on Mobile Edge Computing Servers[J]. Journal of Systems Architecture, 2019, 94:14-23.

[17] Yuan Q, Zhou H, Li J, et al. Toward efficient content delivery for automated driving services: An edge computing solution[J]. IEEE Network, 2018, 32(1): 80-86.

[18] Zhao H, Yao L B, Zeng Z X, et al. An edge streaming data processing framework for autonomous driving[J]. Connection Science, 2021, 33(2): 173-200.

[19] Uçar A, Demir Y, Güzeliş C. Object recognition and detection with deep learning for autonomous driving applications[J]. Simulation, 2017, 93(9): 759-769. 

[20] Li G, Yang Y, Qu X, et al. A deep learning based image enhancement approach for autonomous driving at night[J]. Knowledge-Based Systems, 2021, 213: 106617.

[21] Huval B, Wang T, Tandon S, et al. An Empirical Evaluation of Deep Learning on Highway Driving[J]. Computer Science, 2015.

[22] Yuan Y, Xun G, Jia K, et al. A multi-view deep learning framework for EEG seizure detection[J]. IEEE journal of biomedical and health informatics, 2018, 23(1): 83-94.

[23] Hussein R, Palangi H, Ward R, et al. Epileptic Seizure Detection: A Deep Learning Approach[J]. arXiv, 2018.

[24] Daoud H, Bayoumi M A. Efficient Epileptic Seizure Prediction Based on Deep Learning[J]. IEEE Transactions on Biomedical Circuits and Systems, 2019, PP(99):1-1.

[25] Zhang Z, Parhi K K. Low-complexity seizure prediction from iEEG/sEEG using spectral power and ratios of spectral power[J]. IEEE transactions on biomedical circuits and systems, 2015, 10(3): 693-706.

[26] Amin S U, Alsulaiman M, Muhammad G, et al. Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion[J]. Future Generation Computer Systems, 2019, 101.

[27] Epileptic Seizure Prediction over EEG Data using Hybrid CNN-SVM Model with Edge Computing Services[J]. Matec Web of Conferences, 2018, 210.

[28] Shi W, Jie C, Quan Z, et al. Edge Computing: Vision and Challenges[J]. Internet of Things Journal, IEEE, 2016, 3(5):637-646.

[29] Dong R, She C, Hardjawana W, et al. Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin[J]. IEEE Transactions on Wireless Communications, 2019, 18(10):4692-4707.

[30] Huh J H, Seo Y S. Understanding Edge Computing: Engineering Evolution with Artificial Intelligence[J]. IEEE Access, 2019, PP(99):1-1.

[31] Huang Y, Ma X, Fan X, et al. When deep learning meets edge computing[C]// 2017 IEEE 25th International Conference on Network Protocols (ICNP). IEEE, 2017.

[32] Al-Qizwini M, Barjasteh I, Al-Qassab H, et al. Deep learning algorithm for autonomous driving using GoogLeNet[C]// 2017 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2017.

[33] Prabhakar G, Kailath B, Natarajan S, et al. Obstacle detection and classification using deep learning for tracking in high-speed autonomous driving[C]//2017 IEEE region 10 symposium (TENSYMP). IEEE, 2017: 1-6.

[34] Hosseini M P, Tran T X, Pompili D, et al. Deep learning with edge computing for localization of epileptogenicity using multimodal rs-fMRI and EEG big data[C]//2017 IEEE international conference on autonomic computing (ICAC). IEEE, 2017: 83-92.

[35] Sayeed A, Mohanty S P, Kougianos E, et al. A Robust and Fast Seizure Detector for IoT Edge[C]// 2018 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS). IEEE, 2019.