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

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Abstract

This study offers an autoregressive integrated moving average (ARIMA)-based mobility prediction model for forecasting a node's future mobility speed in mobile ad hoc networks (MANETs). The route discovery process among the nodes depends on the network connectivity of the nodes in the MANETs. The selection of mobility nodes to offer reliable routing is facilitated by the presented mobility prediction algorithm. To predict node mobility, the proposed ARIMA model employs autocorrelation to forecast node mobility in the future with the random walk (RW) model. For the predicted mobility model, the Akaike information criterion (AIC) and auto-correlation function (ACF) are assessed. The error performance metric mean square error (MSE) is very lower for the ARIMA-predicted mobility datasets as compared with the RNN-predicted mobility datasets. Different scenarios of speed values are examined for the proposed models. The error metrics for the walking scenario at the speed 1 and 1.5 m/s; the running scenario at the speed 4 and 10 m/s; and the cycling scenario at the speed 5 and 15 m/s are analyzed in the presented work. The proposed ARIMA model yields a higher futuristic mobility prediction precision rate of 3–24% as compared with existing works. Further, the proposed ARIMA has 88–99% of futuristic mobility prediction accuracy.

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  1. Department of Computer Science Engineering, GITAM School of Technology, GITAM University Bengaluru, Doddaballapura, India

    Prasannavenkatesan Theerthagiri

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Theerthagiri, P. Mobility prediction for random walk mobility model using ARIMA in mobile ad hoc networks. J Supercomput 78, 16453–16484 (2022). https://doi.org/10.1007/s11227-022-04503-6

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