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