Wireless energy supply edge calculation delay optimization method based on deep learning
A wireless energy supply and edge computing technology, applied in computing, computer components, structured data retrieval, etc., to achieve real-time and latency reduction effects
Active Publication Date: 2020-03-31
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 wireless energy supply edge calculation delay optimization method based on deep learning, and belongs to the field of edge calculation. The optimization method provided by the invention comprises the following steps: 1) establishing a time delay model for unloading a task to an edge server based on wireless energy supply edge calculation of node cooperative transmission;2) converting the edge server time delay model into a linear integer programming model by adopting a partwise linearization algorithm; 3) performing a simulation experiment to verify the effectivenessof the optimization method, If the time delay is optimized, going to the step 4); otherwise, turning to the step 2); 4) solving the linear integer programming model to obtain a data set for trainingthe deep belief network; and 5) training the DBN network by using the generated data set until the error rate is less than a preset value to obtain a trained network model, thereby solving the problemof the near-far effect of the wireless energy supply edge calculation network, and reducing the time delay of unloading the data 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): G06F16/27G06K9/62H04L29/08
CPCG06F16/27H04L67/1001G06F18/214
Inventor 张兴军付哲于博成纪泽宇蔡梦华
Owner XI AN JIAOTONG UNIV