# A Robust UWSN Handover Prediction System Using Ensemble Learning
## 5 Experimental Results
### Results and Discussion  
The proposed system utilized four different classification techniques: decision tres, K-nearest neighbor, Gaussian NB, and gradient boosting. The utilized classification models were evaluated on six different metrics: precision (Equation (9)), recall (Equation (8)), F1-score (Equation (10)), accuracy (Equation (11)), AUC-ROC, and confusion matrix. Table 2 lists five evaluation metrics for Exp-I and Exp-II for the gradient boosting classifier. The classifiers in Exp-I clearly had a superior overall performance to those in Exp-II because the exact location of the sensor node was one of the input features. If the base station (i.e., the static buoy) and sensor node locations are known, the task of predicting the next cell to serve the sensor node should be easy. The problem becomes more difficult when the handover prediction is based on the signal strength. Thus, the results of Exp-I were higher than those of Exp-II. Figures 6 and 7 depict the confusion matrices of the four different classifiers for Exp-I and Exp-II, respectively. The obtained results emphasize the higher results of Exp-I \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\alpha\) & 1 \\ \(\lambda\) & 0.125 m \\ \(P_{t}\) & 5 mW \\ \(G_{r}\) & 1 \\ \(G_{t}\) & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter values of the experiments. compared to those of Exp-II. In addition, the gradient boosting classifier clearly had the best performance among all classifiers. In Exp-I and Exp-II, the behaviour of the classes was almost the same for all of the utilized machine learning models except GNB. This behaviour can be noticed in Exp-I (e.g., Figures 6(c) and 6(c)) and it is obvious in Exp-II (e.g., Figures 6(c) and 6(c)). The lowest performance of GNB can be linked to the assumption of independent predictor features. The features UWSN handover prediction problem is somewhat related to the location of cells and wave directions. Besides, these results indicate that the data of Exp-I were easier to predict in comparison to the data of Exp-II. In Exp-I, the classification rates of the gradient boost and decision tree models were similar. On the other hand, at lower classification rates, the performance of the KNN and GNB models was close. The lowest class accuracy rates were 99.2%, 98.9%, 88.4%, and 85.7% for decision tree, gradient boost, KNN, and GNB, respectively. In Exp-II, the classification rates of the gradient boost and decision tree models were almost identical. The KNN model's performance was lower than that of gradient boost and decision tree models. The GNB model's accuracy was very low. The lowest class accuracy rates were 97.4%, 95.9%, 88.1%, and 49.1% for decision tree, gradient boost, KNN, and GNB, respectively. Figures 8 and 9 depict the precision-recall curves, and Figures 10 and 11 present the AUC-ROC curves. All of these results are consistent with the aforementioned. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Recall** & **Precision** & **F1-Score** & **Accuracy** & **ROC-AUC** \\ \hline \multirow{3}{*}{0.9816} & \multirow{3}{*}{0.9816} & Exp-I & \multirow{3}{*}{0.9816} & Exp-I \\  & & & 0.9816 & \\ \cline{1-1}  & & & 0.9816 & \\ \hline \multirow{3}{*}{0.9659} & \multirow{3}{*}{0.9659} & Exp-II & \multirow{3}{*}{0.9659} & \multirow{3}{*}{0.9987} \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average handover prediction results for the gradient boosting model. Figure 6: Confusion matrices for Exp-I. Figure 8: Precision–recall curves for Exp-I. Figure 7: Confusion matrices for Exp-II. The performance of the GNB classifier in Exp-I and Exp-II was the lowest in comparison to the other classifiers. This is linked to the GNB classifier's assumption that the independence of features should hold true. In this dataset, the input features are dependent to some extent (e.g., the location of the sensor node and the flow direction).