The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing
Open accessAbstract
In this paper, we propose a Distributed Intelligent Video Surveillance (DIVS) system using Deep Learning (DL) algorithms and deploy it in an edge computing environment. We establish a multi-layer edge computing architecture and a distributed DL training model for the DIVS system. The DIVS system can migrate computing workloads from the network center to network edges to reduce huge network communication overhead and provide low-latency and accurate video analysis solutions. We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing. Task-level parallel and model-level parallel training methods are proposed to further accelerate the video analysis process. In addition, we propose a model parameter updating method to achieve model synchronization of the global DL model in a distributed EC environment. Moreover, a dynamic data migration approach is proposed to address the imbalance of workload and computational power of edge nodes. Experimental results showed that the EC architecture can provide elastic and scalable computing power, and the proposed DIVS system can efficiently handle video surveillance and analysis tasks.
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Cites
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Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing
Li, H., Ota, K., & Dong, M. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700202 · Cited by 1,566
Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids
Zheng, Z., Yang, Y., Niu, X., Dai, H.-N., & Zhou, Y. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2785963 · Cited by 788
DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters
Zhao, Z., Barijough, K. M., & Gerstlauer, A. (2018) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2018.2858384 · Cited by 502
Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing
Li, L., Ota, K., & Dong, M. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2842821 · Cited by 466
Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition
Ding, C., & Tao, D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2700390 · Cited by 441
Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach
Han, H., Jain, A. K., Wang, F., Shan, S., & Chen, X. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2738004 · Cited by 278
Robust Mobile Crowd Sensing: When Deep Learning Meets Edge Computing
Zhou, Z., Liao, H., Gu, B., Huq, K. M. S., Mumtaz, S., & Rodriguez, J. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700442 · Cited by 260
LAVEA: latency-aware video analytics on edge computing platform
Yi, S., Hao, Z., Zhang, Q., Zhang, Q., Shi, W., & Li, Q. (2017) · Proceedings of the Second ACM/IEEE Symposium on Edge Computing · doi:10.1145/3132211.3134459 · Cited by 244
Double Auction-Based Resource Allocation for Mobile Edge Computing in Industrial Internet of Things
Sun, W., Liu, J., Yue, Y., & Zhang, H. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2855746 · Cited by 233
Cited by
Chen, J., Li, K., & Yu, P. S. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3105682 · Cited by 116
Self-citationFederated Learning in Robotic and Autonomous Systems
Xianjia, Y., Queralta, J. P., Heikkonen, J., & Westerlund, T. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.07.041 · Cited by 79
Cheng, H., Shi, Y., Wu, L., Guo, Y., & Xiong, N. (2021) · Information Sciences · doi:10.1016/j.ins.2020.12.041 · Cited by 29
Metadata
- Authors
- Chen, J., Li, K., Deng, Q., Li, K., & Yu, P. S.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2024
- Pages
- 1-1
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2929084559
- Cited by
- 253
- References
- 24
- FWCI
- 19.69
- Type
- journal article
- Language
- en
- Open access
- green
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- Chen, J., Li, K., Deng, Q., Li, K., & Yu, P. S. (2024). Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2019.2909473
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