## 4 Improved GWO-Based Load-Balanced Clustering
### Performance Evaluation for Sustainable WSNs
#### 5.2.4 Comparison of Lifetime  
The CHs' active status determines the WSNs' lifetime; therefore, keeping them alive for as long as possible is essential for effective communication. Table 7's findings demonstrate that the proposed IGWO technique consistently results in fewer CH deaths, with 3, 8, 6 and 14 CHs dying in each of the 500 rounds. It is evident that the HSCA strategy performs almost equal to the proposed approach.  
Figure 14 compares network lifetime in terms of the number of CHs die in different rounds. Here, it can be observed that in 500, 1500 and 2000 rounds, the minimum number of CHs die in the case of the IGWO, whereas the maximum CHs die in the case of the  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Clustering Techniques**} & \multicolumn{4}{c}{**Energy Consumption in Rounds**} \\ \cline{2-5}  & **0–500** & **501–1000** & **1001–1500** & **1501–2000** \\ \hline IGWO & 3 & 8 & 6 & 14 \\ Traditional GWO & 5 & 8 & 7 & 14 \\ HSCA & 4 & 8 & 7 & 14 \\ GA & 7 & 7 & 11 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of dead CHs for network lifetime.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Clustering Techniques**} & \multicolumn{4}{c}{**Lifetime in Rounds**} \\ \cline{2-5}  & **0–500** & **501–1000** & **1001–1500** & **1501–2000** \\ \hline IGWO & 202 & 188 & 192 & 118 \\ Traditional GWO & 217 & 203 & 207 & 133 \\ HSCA & 208 & 193 & 197 & 123 \\ GA & 254 & 238 & 242 & 168 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of energy consumption in different approaches.  
Figure 13: Energy consumption in different number of rounds.  
GA-based approach. In contrast, fewer dead CHs are seen in the GA-based strategy in rounds 501-1000. When compared to other approaches, the IGWO and HSA are determined to have the fewest dead CHs as the number of rounds grows.  
The dead sensor nodes need to be evaluated based upon the first and last dead sensor for assessing the network lifetime. Table 8 compares the different approaches for this analysis. Here, the first node dies in GALBCA in 291, HSCA in 407, traditional GWO in 430 and IGWO in 496 rounds. Therefore, the network failure occurs in GALBCA at an early stage as compared with other approaches. However, the last node that dies in the genetic algorithm is in 1922 rounds, and at this stage, in this approach, most of the nodes were dead, i.e., 454 nodes are dead out of 500, as shown in column 4 of Table 8.  
Table 9 shows the number of sensor nodes die at the different intervals in a range of 0-2000 rounds. Only 17, 34, 98 and 130 sensor nodes die between different steps of rounds in the IGWO approach, whereas a large difference between the dead sensors occurs in other approaches, except HSCA. Figure 15 represents the network lifetime in context of the number of sensors die. From Figure 15, it can be observed that the dead sensors are minimum at the end of 500 and 1000 in the IGWO and in 1500 rounds, GA offers the minimum, whereas in 2000 rounds, the dead sensors are again maximum in a GA-based approach.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Clustering** & **First Nodes Die in** & **Last Nodes Die in** & **Total Nodes Die in** \\
**Techniques** & **No. of Rounds** & **No. of Rounds** & **2000 rounds** \\ \hline IGWO & 496 & 1848 & 279 \\ Traditional GWO & 430 & 1849 & 297 \\ HSCA & 407 & 1836 & 295 \\ GA & 291 & 1922 & 454 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of first and last sensor nodes die in different techniques.  
Figure 14: Network lifetime (with CHs) in different number of rounds.