物联网数据传输方法采用神经网络自编码器

E. Siemens, V. Kurdecha, Serhii Ushakov
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引用次数: 0

摘要

背景。物联网中的设备数量在不断增加。与此同时,市场上针对此类技术的解决方案也在不断增加。统计数据证实,这些因素导致数据传输量增加。这增加了用于数据传输的资源数量。物联网技术用户数量的不断增长趋势导致网络传输的数据量迅速增加的问题出现。目标。本文的目的是通过修改神经网络自编码器来改进物联网中的数据传输过程,以减少网络资源的使用。方法。分析物联网数据传输的专著。在物联网数据传输过程中,基于神经网络自编码器集成现有数据编码解决方案。结果。神经网络自编码器通过使用一种算法来改进,该算法另外包括一个算术编码器,并在一个成熟的自编码器的输出上进一步训练一个新模型。结论。通过对主自编码器的初始数据进行更小的神经网络训练,改进了神经网络自编码器,从而改进了物联网网络中的数据传输过程,减少了数据传输量,从而减少了网络资源的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
INTERNET OF THINGS DATA TRANSFER METHOD USING NEURAL NETWORK AUTOENCODER
Background. The number of devices in the Internet of Things is constantly increasing. At the same time, the number of solutions on the market for such technologies is growing. Statistics confirm that these factors lead to an increase in data transfer volumes. This raises the number of resources spent on data transmission. The growing trend in the number of users of the Internet of Things technology leads to the emergence of the problem of a rapid increase in the data transmitted by the network. Objective. The purpose of the paper is to improve the process of data transmission in the Internet of Things by modifying the neural network autoencoder to reduce network resources use. Methods. Analysis of publications dedicated to Internet of things data transmission. Integration of existing data coding solutions based on a neural network autoencoder in the process of transmitting data from the Internet of things. Results. The neural network autoencoder has been improved by using an algorithm that additionally includes an arithmetic encoder and further training a new model on the output of a full-fledged autoencoder. Conclusions. The process of data transmission in the Internet of Things network has been modified by improving the neural network autoencoder by using the training of a smaller neural network on the initial data of the main autoencoder, which has reduced the amount of data transmitted and, accordingly, reduced the use of network resources.
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