Prediction Models of the Average Distance Between Nodes of a Flying Ad Hoc Network
DOI:
https://doi.org/10.52575/2687-0932-2022-49-3-616-629Keywords:
flying ad hoc network, prediction of average distance between nodes, regression and autoregression models, fuzzy inference, neural network learningAbstract
The results of a study on the development of models for predicting the average distance between the nodes of a flying ad hoc network are presented. The relevance of creating tools that allow predicting the average distance between transmitting and receiving network nodes with acceptable accuracy is justified by the possibility of using them to select and set an adequate value of the transmitted signal power at the network nodes. The solution to this problem is ultimately focused on ensuring the required quality of video broadcasting based on a flying ad hoc network used to monitor territories during search and rescue operations. It is shown that the use of regression and autoregression models gives unacceptable deviations of the forecast results from real data, which leads to a decrease in the probability of the required quality of video broadcast over the network to an unacceptably low level. To predict the average distance between transmitting and receiving nodes, a model based on fuzzy inference is proposed. Automatic selection of unknown values of fuzzy inference parameters is provided on the basis of neural network tuning during multiple training cycles. The obtained results of the study showed that the use of the fuzzy inference model makes it possible to obtain an acceptable accuracy in predicting the average distance between the nodes of a flying ad hoc network.
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