A Delay Optimization Method for Wireless Powered Edge Computing Based on Deep Learning
A technology of wireless energy supply and edge computing, which is applied in computing, computer components, structured data retrieval, etc., to achieve the effect of reducing delay and satisfying real-time performance
Active Publication Date: 2022-04-22
XI AN JIAOTONG UNIV
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[0004] The purpose of the present invention is to solve the problem of the "far and near" effect of the wireless power supply edge computing network, and provide a wireless power supply edge computing delay optimization method based on deep learning
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[0048] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is an embodiment of a part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.
[0049] It should be noted that the terms "first" and "second" in the description and claims of the present invention and the above drawings are used to distinguish similar objects, but not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate ci...
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The invention discloses a delay optimization method for edge computing of wireless power supply based on deep learning, which belongs to the field of edge computing. The optimization method of the present invention includes the following steps: 1) establishing a time delay model for task offloading to the edge server based on wireless power supply edge computing based on node cooperative transmission; 2) converting the time delay model of the edge server into a linear Integer programming model; 3) Carry out simulation experiments to verify the effectiveness of the optimization method, if the time delay is optimized, go to step 4); otherwise, go to step 2); 4) Solve the linear integer programming model to obtain the data for training the deep belief network 5) Use the generated data set to train the DBN network until the error rate is less than the preset value to obtain a trained network model. It solves the problem of the "near and far" effect of the wireless power edge computing network, and reduces the delay of data offloading to the edge server.
Description
technical field [0001] The invention belongs to the field of edge computing, in particular to a deep learning-based wireless power supply edge computing delay optimization method. Background technique [0002] As the proportion of mobile data in the total global data is increasing year by year, mobile cloud computing, which migrates data processing tasks to the cloud, can no longer meet users' needs for low latency and high service quality. In order to solve the above needs, migrating cloud servers to edge computing closer to users came into being. Users can offload complex computing tasks to edge servers to meet their own needs for low latency, low energy consumption, and high quality of service. With the continuous improvement of the hardware level of mobile terminals and the gradual improvement of related communication technologies, users can not only serve as resource demanders, but also contribute their own computing and storage resources to mobile edge computing as re...
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IPC IPC(8): G06F16/27G06K9/62H04L67/1001
CPCG06F16/27H04L67/1001G06F18/214
Inventor 张兴军付哲于博成纪泽宇蔡梦华
Owner XI AN JIAOTONG UNIV