Traffic Prediction as a Multidimensional Random Process in a Three-Dimensional High-Density Internet of Things Network

Relevance. The relevance of the topic considered in the article lies in the active transformation of communication networks and the formation of a three-dimensional high-density communication network, which changes the structure of data traffic, therefore, for this type of network, a traffic model is considered as a multidimensional random process. The main purpose of the study is to improve the efficiency of network traffic forecasting by developing a method, the distinctive feature of which is traffic forecasting as a multidimensional random process, taking into account the mutual dependence of individual flows produced by network nodes. Methods. The paper considers an algorithm for training an artificial neural network (ANN) based on the method of reducing the root of the mean square error RMSE, and also proposes forecasting methods using LSTM-type ANNs and adapting model parameters to changing network operating conditions. The use of LSTM-type ANN for forecasting a multivariate random process describing traffic in a three-dimensional high-density network can yield better results than forecasting individual traffic flows as independent random processes due to the consideration of mutual influences between different traffic flows.The results. Building the corresponding model, collecting statistics (obtaining a training sample), training the ANN and performing the forecast require the use of computing resources. Thus, the forecasting efficiency can be defined as a decrease in the forecasting error while maintaining the volume of resources used or a decrease in the volume of resources while maintaining the forecasting error. In the course of solving the scientific problem, criteria were identified for selecting the value of a unit interval (lag), which, together with the forecasting interval, significantly affects the final scenario.The theoretical significance The scientific novelty of the work lies in the assessment of the change in the error in forecasting the traffic of a three-dimensional high-density communication network as a multivariate random process, compared to presenting the forecast of the same traffic as a set of independent random processes.Significance (theoretical). The efficiency of traffic forecasting as a multidimensional random process in a three-dimensional high-density communication network increases with increasing dimensionality. Thus, such traffic in forecasting problems should be considered as a multidimensional random process, the dimensionality of which is equal to the number of network nodes producing traffic.Significance (practical). The results obtained in the work can be used in the future to optimize the functioning of the traffic management system.

three-dimensional network, high-density network, traffic prediction, neural networks