Mobility Prediction of Mobile Wireless Nodes

Abstract

Artificial intelligence (AI) is a fundamental part of improving information technology systems. Essential AI techniques have revolutionized communication technology, such as mobility models and machine learning classification. Mobility models use a virtual testing methodology to evaluate new or updated products at a reasonable cost. Classifiers can be used with these models to achieve acceptable predictive accuracy. In this study, we analyzed the behavior of machine learning classification algorithms—more specifically decision tree (DT), logistic regression (LR), k-nearest neighbors (K-NN), latent Dirichlet allocation (LDA), Gaussian naive Bayes (GNB), and support vector machine (SVM)—when using different mobility models, such as random walk, random direction, Gauss–Markov, and recurrent self-similar Gauss–Markov (RSSGM). Subsequently, classifiers were applied in order to detect the most efficient mobility model over wireless nodes. Random mobility models (i.e., random direction and random walk) provided fluctuating accuracy values when machine learning classifiers were applied—resulting values ranged from 39% to 81%. The Gauss–Markov and RSSGM models achieved good prediction accuracy in scenarios using a different number of access points in a defined area. Gauss–Markov reached 89% with the LDA classifier, whereas RSSGM showed the greatest accuracy with all classifiers and through various samples (i.e., 2000, 5000, and 10,000 steps during the whole experiment). Finally, the decision tree classifier obtained better overall results, achieving 98% predictive accuracy for 5000 steps.