## 4 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Time Interval(s)/No of Nodes**} & \multicolumn{2}{c}{**1-Hop Latency**} & \multicolumn{2}{c}{**End–End Latency**} & \multicolumn{2}{c}{**Success Rate**} & \multicolumn{2}{c}{**Admission Rate**} & \multirow{2}{*}{**CBO**} \\ \cline{3-3} \cline{5-7}  & \multicolumn{1}{c}{**of Nodes**} & & & & & & \\ \hline \multirow{4}{*}{ELQSSM-ML} & 500 & 0.61 & 0.65 & 0.88 & 0.89 & 0.27 \\ \cline{2-7}  & 100 & 0.64 & 0.68 & 0.91 & 0.93 & 0.29 \\ \cline{2-7}  & 1500 & 0.69 & 0.71 & 0.93 & 0.95 & 0.31 \\ \cline{2-7}  & 2000 & 0.71 & 0.77 & 0.95 & 0.98 & 0.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of metrics for node range 500–1000. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Time Interval(s)/No**} & \multicolumn{2}{c}{**1-Hop Latency**} & \multicolumn{2}{c}{**End–End Latency**} & \multicolumn{2}{c}{**Success Rate**} & \multicolumn{2}{c}{**Admission Rate**} & \multirow{2}{*}{**CBO**} \\ \cline{3-3} \cline{5-7}  & \multicolumn{1}{c}{**of Nodes**} & & & & & \\ \hline \multirow{4}{*}{EECRM} & 50 & 0.43 & 0.54 & 0.44 & 0.54 & 0.76 \\ \cline{2-7}  & 100 & 0.47 & 0.61 & 0.39 & 0.51 & 0.78 \\ \cline{2-7}  & 150 & 0.61 & 0.72 & 0.31 & 0.49 & 0.78 \\ \cline{2-7}  & 200 & 0.65 & 0.81 & 0.29 & 0.32 & 0.81 \\ \hline \multirow{4}{*}{EECRP-PSO} & 50 & 0.49 & 0.63 & 0.25 & 0.65 & 0.67 \\ \cline{2-7}  & 100 & 0.52 & 0.71 & 0.51 & 0.61 & 0.61 \\ \cline{2-7}  & 150 & 0.69 & 0.81 & 0.49 & 0.59 & 0.58 \\ \cline{2-7}  & 200 & 0.71 & 0.89 & 0.44 & 0.54 & 0.54 \\ \hline \multirow{4}{*}{QSSM-ML} & 50 & 0.63 & 0.65 & 0.36 & 0.71 & 0.46 \\ \cline{2-7}  & 100 & 0.69 & 0.69 & 0.31 & 0.68 & 0.49 \\ \cline{2-7}  & 150 & 0.71 & 0.78 & 0.30 & 0.65 & 0.51 \\ \cline{2-7}  & 200 & 0.82 & 0.89 & 0.29 & 0.63 & 0.57 \\ \hline \multirow{4}{*}{ELQSSM-ML} & 50 & 0.31 & 0.41 & 0.81 & 0.87 & 0.21 \\ \cline{2-7}  & 100 & 0.36 & 0.47 & 0.81 & 0.86 & 0.21 \\ \cline{2-7}  & 150 & 0.41 & 0.51 & 0.80 & 0.86 & 0.23 \\ \cline{2-7}  & 200 & 0.52 & 0.59 & 0.80 & 0.85 & 0.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of performance metrics. Figure 3: Comparison of 1-Hop LatencyIt is noted from Figure 1 that there is a consistency of the delay being kept minimum while increasing the time interval in the proposed ELQSSM-ML method. The End-End latency of the proposed method is 34.1% less than EECRM, 28% less than EECRP-PSO, and 18% less than the QSSM-ML method when the time for simulation is recorded for 150 s.  
It is evident from Figure 3 that the success rate is high for the proposed method when compared to that of all the other methods taken for comparison. For the given number of nodes as 200, the success rate of the proposed method ELQSSM-ML is 31% more than the EECRM, 29.2% more than EECRP-PSO, and 11% more than the QSSM-ML. Figure 4 depicts the comparative analysis of the Admission rate, and it is seen that the rate of admission for the proposed method is high and consistent even if the number of nodes is increased. It is seen that the proposed method ELQSSM-ML has 86% of admission rate when the no of nodes is 50 and 100 and an 87% admission rate when the no of nodes is 150 and 200, respectively, which is relatively better than other methods. Figure 5 shows that the CBO of the proposed method ELQSSM-ML has surpassed the other methods with a different number of nodes. It is seen that the proposed method can significantly decrease CBO by 29.8% when compared with EECRM, 17.6% when compared with EECRP-PSO, and 14.2% when compared with the QSSM-ML method. Figure 4: Comparison of Admission Rate. Figure 5: Comparison of CBO.