## 5 Performance Evaluation
### Comparative Analysis
#### 5.2.1 Efficiency of Network Lifetime  
The energy efficiency of the proposed DEDG 3D-UWSN was compared with previous works on AEC, ALP, bipartite K-means and ELMPP processes. In ALP and ELMPP, the clustering is not performed, and, hence, each node delivers its data individually to the AUV. Even though scheduling is present in AEC, it is not defined based on the energy constraint of the sensor node. The existence of these limitations degrades the network lifetime. The energy consumption increases due to longer waiting time, multiple hops in data transmission, continuous sensing and elimination of energy-aware constraints. Figure 7 illustrates the comparison with previous works relating to energy consumption, and the results show the DEDG 3D-UWSN has lower energy consumption than the other, previous works. The reduction in energy consumption tends to increase the number of alive sensor nodes in the network. In the DEDG 3D-UWSN, the energy consumption is minimized; thereby, it increases the number of alive sensors and extends network lifetime, providing the following solutions:  
* Clustering with optimal CH selection enhances the stability of the CH, and this minimizes frequent selection of the CH as well as cluster formation. Without the knowledge of neighboring nodes, the cluster cannot be constructed, which requires exchange of hello packets with the neighboring nodes, which consumes energy; \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Work** & **Clustering** & **Scheduling** & **AUV Path Prediction** &
\begin{tabular}{c} **Emergency Event** \\ **Transmission** \\ \end{tabular} \\ \hline
[43] & ✓ & ✓ & ✓ & \(\times\) \\
[44] & \(\times\) & \(\times\) & ✓ & \(\times\) \\
[45] & ✓ & \(\times\) & ✓ & \(\times\) \\
[46] & \(\times\) & \(\times\) & ✓ & \(\times\) \\ DEDG 3D-UWSN & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with existing works. Figure 7: Comparison of energy consumption. * Scheduling the sensor with sleep slots based on their current energy status is the major reason for keeping the sensor alive for multiple rounds;
* The route selected by the CH itself consumes energy, and, hence, a gateway is used for predicting a route that reduces energy consumption. The number of alive nodes is illustrated in Figure 8, which shows that ALP has the worst performance, and DEDG 3D-UWSN has the best performance among all the works. As per this comparison, the increase in simulation time also increases the amount of transmission by the sensor nodes. In total, 100 sensors participated in the network, of which 91 nodes were active after 300 s, whereas only 75 nodes were active in AEC. Other works relating to ALP, bipartite K-means and ELMPP were able to maintain 60-75 sensors at the end of simulation. The number of dead nodes is lower in the DEDG 3D-UWSN than in the other works, as shown in Table 5. From this table, it can be seen that bipartite K-means has a lower number of dead nodes than the proposed work, and this is due to the performance of the clustering algorithm. However, the cluster construction in bipartite K-means consumes time, it constructs a cluster and then the data are gathered. Network lifetime was evaluated with respect to the number of sensors, and the results are shown in Figure 9. The higher network lifetime reflects the efficiency of the proposed DEDG 3D-UWSN. As an average, the previous works relating to ALP, ELMPP and bipartite graph achieved 80-88% network lifetime, whereas 97% was reached in the DEDG 3D-UWSN. Therefore, the processes performed in this work are efficient, and, hence, there is improvement in the network lifetime. Figure 8: Comparison of alive nodes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Simulation Time**} & \multicolumn{5}{c}{**\# of Dead Sensor Nodes**} \\ \cline{2-6}  & **AEC** & **ALP** & **Bipartite K-Means** & **ELMPP** & **DEDG 3D-UWSN** \\ \hline
100 & 13 & 24 & 15 & 18 & 3 \\
200 & 19 & 32 & 19 & 22 & 5 \\
300 & 25 & 40 & 23 & 26 & 9 \\ \hline \hline \end{tabular}
\end{table}