# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 5 Results and Discussions
Thus, these demonstrated that the proposed adjustment function worked properly in the task of overhead reduction. Additionally, the RTT parameter values obtained in scenario P is presented in Figure 10. Figure 11 shows the average of the RTT values of the scenarios R with also a single flow. These results represent the average values of two AMR-WB mode, because there was not difference between them. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Mode 2**} \\ \hline \multicolumn{6}{c}{**T3**} \\ \hline \multicolumn{2}{c}{**3 Drops**} & **5 Drops** & **7 Drops** & **3 Drops** & **5 Drops** & **7 Drops** \\ \hline \multicolumn{2}{c}{**CE/FB/IH**} & **CE/FB/IH** & **CE/FB/IH** & **CE/FB/IH** & **CE/FB/IH** & **CE/FB/IH** \\ \hline \multicolumn{2}{c}{**(\%)**} & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline e-RLRP & **0.58/0.90**/**0.85**/0 & **0.87/1.06/1.11**/0 & **1.62/2.10/1.55/0 & **0.52/0.76/0.83**/0 & **0.75/1.40/1.01**/0 & **1.17/2.20/1.32**/0 \\ RLRP & 0.59/0.91/0.89/0 & 1.38/1.26/1.19/0 & 1.93/2.20/1.63/0 & 0.62/0.84/ 0.84/0 & **0.75/1.41/1.05/0 & 1.23/2.32/1.43/0 \\ Batman & 1.52/1.55/1.26/0 & 2.70/2.86/2.98/0 & 5.30/4.40/4.10/0 & 1.49/1.35/1.03/0 & 2.41/2.56/2.49/0 & 5.01/4.1/3.89/0 \\ OLSR & 1.62/1.28/0.90/0 & 3.45/1.90/1.47/0 & 2.83/2.60/2.94/0 & 1.49/1.35/1.03/0 & 2.41/2.56/2.49/0 & 5.01/4.1/3.89/0 \\ \hline \hline \end{tabular}
\end{table} 
Table 31: _Ppl_ (%) Obtained in Scenario R with Four Flows considering AMR-WB Mode 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Mode 2**} \\ \hline \multicolumn{2}{c}{**T3**} \\ \hline \multicolumn{2}{c}{**3 Drops**} & **5 Drops** & **7 Drops** & **3 Drops** & **5 Drops** & **7 Drops** \\ \hline \multicolumn{2}{c}{**(kbps)**} & **(kbps)** & **(kbps)** & **(kbps)** & **(kbps)** & **(kbps)** \\ \hline e-RLRP & **2.96** & **4.26** & **4.13** & **3.28** & **4.10** & **4.46** \\ RLRP & 3.88 & 5.25 & 4.61 & 3.72 & 4.55 & 4.88 \\ Batman & 8.26 & 9.04 & 11.65 & 8.25 & 9.16 & 11.33 \\ OLSR & 3.06 & 4.66 & 5.30 & 3.95 & 4.68 & 5.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 32: Overhead (kbps) Obtained in Scenario R with Four flows considering AMR-WB Mode 2. Analyzing the results presented in Figures 10 and 11, it is observed that the e-RLRP and RLRP presented the highest RTT values. This can be justified because they are implemented in user space on Linux using a dynamic Python interpreter. According to Reference [40], this implementation-type generates a great loss of performance mainly due to the high number of I/O operations that cause delays in the packet sending process. It is worth mentioning that this is a limitation generated by the language in which it was implemented and not by the code/project. Thus, the implementation of these both protocols had a restriction in this regard, that was reflected in RTT values obtained in the experimental tests. According to (6) and (7), delays in the network have a negative impact on speech quality predictions. Finally, the speech communication quality was evaluated using (4)-(6) considering the \(Ppl\) and RTT values found in test scenarios that consider a single traffic flow with the topologies T1, T2, T3 and T4 used in this work. Figure 12 presents the \(R_{\text{WB}}\) scores for scenario R with Three Drops, Figure 13 presents the \(R_{\text{WB}}\) scores for Five Drops and Figure 14 with Seven Drops. Figure 11: RTT Obtained in Scenario R (Random). Figure 10: RTT Obtained in Scenario P (Programmed). Figure 15 presents the \(R_{\rm WB}\) scores for scenario P.  
Figure 14: \(R_{\rm WB}\) Score in Scenario R with Seven Drops. Figure 12: \(R_{\rm WB}\) Score in Scenario R with Three Drops. Figure 13: \(R_{\rm WB}\) Score in Scenario R with Five Drops. As can be observed from Figures 12-15, the use of e-RLRP promotes a gain of \(R_{\text{WB}}\) score in relation to those obtained by the RLRP, BATMAN and OLSR protocols. In some cases the gain in relation to OLSR is more to 90%. In relation to BATMAN, in some cases the gain is approximately 33%.