## 5 Neural Network for Analyzing and Optimizing the Performance of Routing Protocols  
Deep neural networks (DNNs) are a type of artificial neural network that have several hidden layers between input and output. These layers allow the network to learn increasingly complex features and patterns in the input data, which can be used to make accurate predictions or classifications [50]. The objective here is to use a deep neural network model to analyze and optimize routing protocols in wireless networks. This involves training a deep learning model on a dataset, the \"MANIAC Dataset\" [51]. The MANIAC Challenge dataset consists of routing parameters that were collected during the MANIAC (Mobile Ad hoc Networking Interoperability and Collaboration) Challenge. This dataset captures real-world network scenarios and provides valuable insights into the performance and behavior of routing protocols in dynamic wireless environments; this dataset consists of network simulations and metrics, such as packet loss, latency, throughput, and power consumption, as well as protocol-specific parameters, such as hop count, link quality, and table size. The specific objectives are as follows:  
* Analysis: The trained model was used to analyze the dataset, identifying patterns and relationships between input characteristics (metrics and parameters mentioned in this study) and routing protocol performance;
* Optimization: Based on learned patterns and relationships, the DNN model can predict the optimal routing protocol for new network scenarios. By leveraging the knowledge gained from the data set, the model can make accurate predictions regarding the most appropriate routing protocol for a given scenario, thereby improving overall network performance and efficiency;
* Feature importance: The model also helps to understand which features have the greatest impact on protocol performance. By interpreting DNN results, researchers \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nodes** & \multicolumn{3}{c}{**QoE Metrics**} \\ \cline{2-4}
**Density** & **PSNR** & **VQM** & **SSIM** \\ \hline
20 & \(\uparrow\)3.03\% & \(\downarrow\)42.88\% & \(\uparrow\)23.63\% \\
50 & \(\uparrow\)14.20\% & \(\downarrow\)38.69\% & \(\uparrow\)29.62\% \\
100 & \(\uparrow\)14.44\% & \(\downarrow\)35.62\% & \(\uparrow\)25.86\% \\
150 & \(\uparrow\)18.42\% & \(\downarrow\)37.18\% & \(\uparrow\)18.03\% \\
200 & \(\uparrow\)13.00\% & \(\downarrow\)35.71\% & \(\uparrow\)25.00\% \\
250 & \(\uparrow\)25.30\% & \(\downarrow\)41.06\% & \(\uparrow\)12.90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MNH-FGR’s QoE improvement rate regarding GPSR-kP for all different density scenarios. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Routing** & \multicolumn{3}{c}{**QoE Metrics**} \\ \cline{2-4}
**Protocols** & **PSNR** & **VQM** & **SSIM** \\ \hline GPSR-2P & \(\uparrow\)50.00\% & \(\downarrow\)65.29\% & \(\uparrow\)95.00\% \\ GPSR-kP & \(\uparrow\)25.30\% & \(\downarrow\)42.88\% & \(\uparrow\)29.62\% \\ FzGR & \(\uparrow\)12.5\% & \(\downarrow\)31.03\% & \(\uparrow\)23.81\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MNH-FGR’s maximal QoE improvement rate regarding FzGR, GPSR-2P, and GPSR-kP. and network engineers can better understand the underlying mechanisms of different routing protocols, identifying the key factors that influence their performance. To provide a clearer understanding of our methodology, we have included pseudo code (Algorithm 2) that outlines the steps for using the DNN to analyze and optimize routing protocols for wireless networks. The code covers various stages, including dataset loading, model architecture definition, model training and evaluation, predicting optimal routing protocols for new scenarios, and interpreting the DNN results.