## 4 Performance Validation  
This section investigates the performance of the proposed MCR-UWSN technique with other techniques [46]. Figure 3 shows the network lifetime results of the MCR-UWSN model, such as the number of surviving nodes (NSN). Figure 3 depicts that the LEACH technique has attained an ineffective performance with the least NSN. At the same time, the LEACH-ANT technique has gained slightly enhanced NSN over the LEACH technique. Besides, the CUWSN, EOCA, and ACOCR techniques have resulted in a moderate performance over the other techniques. However, the proposed MCR-UWSN technique has accomplished the superior performance with the maximum NSN. Table 1 and Figure 4 illustrate the network lifetime analysis of the MCR-UWSN technique with existing techniques. From the figure, it is apparent that the MCR-UWSN technique has offered the maximum network lifetime [47]. With respect to FND, the MCR-UWSN technique has attained a higher FND of 852 rounds, whereas the LEACH, LEACH-ANT, CUWSN, EOCA, and ACOCR techniques achieved a lower FND of 424, 560, 629, 689, and 805 rounds, respectively. Moreover, in terms of HND, the MCR-UWSN approach reached a superior HND of 1121 rounds, whereas the LEACH, LEACH-ANT, CUWSN, EOCA, and ACOCR manners had an inferior HND of 646, 813, 891, 949, and 1050 rounds, respectively. Furthermore, with respect to LND, the MCR-UWSN method has the superior LND of 1187 rounds, whereas the LEACH, LEACH-ANT, CUWSN, EOCA, and ACOCR methods have a minimal LND of 710, 906, 989, 1021, and 1187 rounds, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Number of Rounds**} \\ \hline  & **LEACH** & **LEACH-ANT** & **CUWSN** & **EOCA** & **ACOCR** & **MCR-UWSN** \\ \hline FND & 424 & 560 & 629 & 689 & 805 & 852 \\ HND & 646 & 813 & 891 & 949 & 1050 & 1121 \\ LND & 710 & 906 & 989 & 1021 & 1165 & 1187 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network lifetime analysis of the MCR-UWSN model with different measures. Figure 3: NSN analysis of MCR-UWSN method with existing approaches. Figure 5 demonstrates the total energy consumption (TEC) analysis of the MCR-UWSN technique with existing techniques. The figure depicts that the LEACH technique has gained an ineffective outcome with the higher TEC over the other techniques. Likewise, the LEACH-ANT technique has attained an improved performance, whereas the CUWSN, EOCA, and ACOCR techniques have a moderate TEC [48]. However, the MCR-UWSN technique has resulted in the superior performance over the other techniques with minimal TEC. Table 2 and Figure 6 illustrate the network lifetime analysis of the MCR-UWSN technique with respect to the number of rounds for energy exhausted (NREE). The figure shows that the LEACH approach has an ineffective performance with the minimum NREE. Simultaneously, the LEACH-ANT method has a slightly higher NREE over the LEACH algorithm. In addition, the CUWSN, EOCA, and ACOCR methods resulted in a moderate performance over the other methods. Eventually, the presented MCR-UWSN method has accomplished a higher performance with the maximal NREE. Figure 4: Result analysis of MCR-UWSN method with different measures. Figure 5: TEC analysis of MCR-UWSN method under distinct rounds. Figure 7 depicts the network lifetime analysis of the MCR-UWSN approach with respect to the number of received packets (NRP). The figure displays that the LEACH method has achieved an ineffective performance with the minimal NRP. Likewise, the LEACH-ANT method has reached slightly increased NRP over the LEACH method. Followed by the CUHWSN, EOCA and ACOCR methods have a moderate efficiency over the other algorithms [49]. Finally, the projected MCR-UWSN algorithm has accomplished the maximal performance with a higher NRP. The major strength of the proposed systems is that the MCR-UWSN approach consists of two stages: CEPOC-based cluster building and MHR-GOA-based routing. The CEPOC approach has produced a fitness function with unique input parameters for CH selection and cluster formation.