Mobility speed prediction using ARIMA and RNN for random walk mobility model in mobile ad hoc networks

In this article, an auto-regressive integrated moving average (ARIMA) modeling has been proposed to predict the mobility speed of the nodes in the mobile ad hoc networks (MANETs). The mobility speed prediction supports effective route discovery to enhance efficient and reliable routing. The random walk mobility model had been used to forecast the mobility of the nodes. Subsequently, various the recurrent neural network (RNN) model has been developed with different number hidden neurons, and the prediction results are compared with proposed ARIMA based results. The Akaike information criterion, auto-correlation function metrics are evaluated to assess the dataset's quality. Several network scenarios are evaluated with various speeds and the number of nodes. The results of the proposed ARIMA model are compared with the RNN results. It demonstrates that the proposed model has higher prediction rates with reduced error rates of 0.1%-1.4% than RNN values. Such that the proposed methods facilitate the higher prediction rate of 10%-26% as compared with the RNN. Therefore, the proposed mobility speed prediction techniques support the effective routing in the MANETs.