# Mobility Prediction of Mobile Wireless Nodes
## 1 Introduction and Motivation  
Machine learning classification has recently garnered attention from information technology developers. It can indeed enhance many fields by improving infrastructure, protocols, and services, making life easier and more predictable. Consequently, numerous studies on machine learning classification have looked at improving classifier algorithms, how to use them, or even at which algorithm performs best for a particular task. Mobility models are a notable feature in this domain. They simulate the movements of mobile nodes in a typical testing environment in order to evaluate the newest techniques, since examining these techniques in a real network is time-consuming and expensive. Classification algorithms are useful in various functions, from simple binary to multi-category classifiers. Each algorithm has a unique manner of doing the required job. However, all algorithms need a hypothetical environment in which to evaluate a given task. Mobility models are able to fill such a need by offering an appropriate virtual environment. Therefore, there are basic popular mobility models and novel mobility models. Basic models can implement general applications, such as Gauss-Markov or random models (random waypoint, random direction, and random walk), while novel ones are used for more specific tasks, as it is the case with RSSGM [1]. Many basic mobility models have widespread usage in wireless networks, such as Gauss-Markov, random direction, and random walk. These models mimic the movement of nodes by supposing the position, direction, and speed of mobile nodes in a virtual environment. However, most mobility models simulate user movement in a completely randomized way, as they do not have enough resilience to dominate the randomness transition. In consequence, they cannot simulate recurrent human behavior that has a daily routine (i.e., the movement of human beings is not entirely random). This problem has been solved by the novel recurrent self-similar Gauss-Markov (RSSGM) mobility model, proposed in our previous work. This model executes recurrent node movement with semi-similar routes by controlling the degree of randomness, which helps the model capture repeated visits by a user to a same location. The RSSGM algorithm's performance, which was determined in [1], makes it a more realistic and suitable model for applications with semi-similar routes, such as lifestyle patterns, airplane controls, or public transportation in a wireless network. Recent studies made use of classification models for the targeted task, whereas others used mobility models to do a specific job. Yet, each of them discussed classifications separately or with a particular mobility model, as in [2], where the authors applied four machine learning (ML) models: DNN, SVM, semi-Markov, and extreme gradient boosting trees simulated by the self-similar least action walk (SLAW) mobility model. In [3], various ML models were used to predict network traffic classification. Results show that the decision tree reached a better average accuracy (i.e., 99.18%) than other models, and higher performance than the port-based method. However, our paper has addressed this gap to comprehensively study the relationship between classifiers and mobility models. In this paper, we aim to demonstrate the effectiveness of mobility models when using classification algorithms. The best way to achieve this goal is by analyzing factors that directly affect the network performance, that is to say the classifier's behavior over different types of mobility models. Recording the accuracy of prediction for user movement during every experiment is considered the main evaluation parameter in this study. Therefore, the evaluation was carried out by applying six classification machine learning algorithms (i.e., decision tree, logistic regression, k-nearest neighbors, latent Dirichlet allocation, Gaussian naive Bayes, and support-vector machine) that rely on mobility models.