# Mobility Prediction of Mobile Wireless Nodes
## 4 Related Works  
Many studies have been conducted on mobility models and their importance in improving technology. For example, in [8], several mobility models were used to show the performance of an AODV routing protocol over constant bit rate (CBR) and variable bit rate (VBR) data. Furthermore, the authors in [12] used three types of mobility models, taking into account the effects of these mobilities on node density, to study the behavior of various delay-tolerant networking (DTN) routing protocols. The authors in [9] sought to define the importance of the mobility model for handover rates in heterogeneous wireless networks over urban regions. Moreover, ref. [13] showed that mobility models can enhance the connectivity of a network under the leader-follower model with mobile leaders. In [10], two novel random models were offered as alternatives to random entity mobility models. [PERSON] et al. described a new model, RSSGM, that is useful with nodes that have semi-similar routes [1]. ML is a popular technique used in AI technology and has considerable ability to enhance different systems. Consequently, there is a considerable amount of research investigating ML and its implementation in several applications. Ref. [15] summarized the performance of eight ML models (i.e., LR, KNN, SVM, DT, random forest, gradient boosting method, and naive Bayes). The authors examined models' ability to predict breast cancer in women, determining that the best ML model for this purpose is the gradient boosting classifier, with an accuracy of 74.14%. However, ref. [25] found that the GNB algorithm successfully predicted breast and lung cancer with an accuracy of 98% and 90%, respectively. As for [16], the authors presented a new ML model for predicting the quality of energy model systems. Moreover, ref. [18] proposed a new ML model, called stepwise-support vector machine (StepSVM), based on the SVM model and used for stepwise model selection. In [19], the authors presented a model that works by tuning the current XGBoost model. Their new model was shown to perform better than several other models (LR, DT, random forest, AdaBoost, and XGBoost) using four datasets (NASA-KC2, PC3, JM1, and CM1). In [22], the k-NN classifier was used, after applying three feature selection algorithms, i.e., CBFS, FFRS, and KFRS, to a leukemia dataset in order to improve the model's cancer classification ability. The authors in [2] applied four ML models: a deep neural network, extreme gradient boosting trees, semi-Markov, and SVM to investigate the performance in terms of energy and accuracy. They evaluated the models' performance using 84 users, simulated by the self-similar least action walk (SLAW) mobility model. They found that the XGBoost was the most accurate, achieving 90% accuracy and exceeding 80% on energy saving. Ref. [29] compared variation logit models and ML in terms of model growth, evaluation, and behavioral mode-choice modeling, providing an understanding of both models' strengths and shortcomings. Results of the analysis suggest that ML models are preferable to logit models. Ref. [30] provided a complete image of ML and deep learning (DL), including definitions, the generation of a dataset in an ad-hoc network, and where ML can be used, extracting valuable information from several research articles. They defined the issues and challenges that face ML and DL in a wireless network. In [23], the authors presented a weighted latent Dirichlet allocation (W-LDA) that overcomes the weaknesses of a classic LDA. Another work, ref. [24], suggested an LDA-based data augmentation algorithm that provides a large training dataset to reveal key audio events in a recording, and further creates new recordings based on that key. Additionally, many articles have discussed and applied machine learning algorithms to improve various applications and systems such as [3; 31; 32]. The authors in [33] suggested a method for non-orthogonal multiple access (NOMA) depending on the satellite-terrestrial integrated network (STIN).