• Eduard Siemens Anhalt University of Applied Systems, Germany
  • Vasyl Kurdecha Educational and Research Institute of Telecommunication Systems of National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Ukraine
  • Serhii Ushakov Educational and Research Institute of Telecommunication Systems of National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Ukraine



Internet of things, data coding, neural network, autoencoder, training on the original model, data compression


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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