## 5 Simulation
### Partial Parameters Impact Analysis  
In this section, we firstly focus on the energy-efficiency of TCEB, i.e., the changes of network lifetime under the different number of nodes and energy heterogeneity factor in UWSN. Usually, the lifetime contains two periods: the stable phase and the unstable phase. The stable phase refers to the time when the first node dies, and the unstable phase denotes the period between when the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Network size (\(M\times M\)) & (100 m\(\times\)100 m) \\ Location of sink & (50,50) \\ Node number (\(n\)) & 100, 200, 300, 400 \\ Energy heterogeneity factor (\(\lambda\)) & [0.5, 4.0] \\ Initial energy (\(E_{0}\)) & 0.5 \\ \(E_{\mathit{elec}}\) & 50 m/bit \\ Depth (\(H\  
first node dies and the last node dies. The round is presented as the unit of the network lifetime. Consequently, the variational trend of both periods should be watched out when the number of nodes and energy heterogeneity parameter change. Figures 3 and 4 show the variation of the stable period and the whole lifetime with the different number of nodes under different energy heterogeneity factor, respectively. In Figure 3, with the increase of \(\lambda\), i.e., as nodes are deployed with more energy, the dead time of the first node will become later than before, no matter what the current number of nodes is, vice versa. Simultaneously, from the longitudinal observation, as more nodes are deployed, the first node dies earlier under the same energy heterogeneity factor (\(\lambda\)). This is because the number of exchange messages between nodes and the interference in the network will sharply increase when the number of nodes is greater than a certain value. Obviously, it may cause dissipating more additional energy during the networking, which directly leads to the first node dead earlier. Therefore, more nodes is not always better, especially in a certain specific concentration area. Figure 4: The whole lifetime with different number of nodes under different energy heterogeneity factor. Figure 3: The stable period with different number of nodes under different energy heterogeneity factor. As shown in Figure 4, with the increase of \(\lambda\), the lifetime also becomes longer under the different number of deployed nodes. Generally, increasing the number of deployed nodes is equivalent to increasing the energy of the network. Nevertheless, we can find that the whole network lifetime does not vary greatly under the same \(\lambda\) while increasing the number of deployed nodes. Similar to the situation of the first node dead, the main reason is located at the increased frequency of communication and the occurrence of interference. It spends extra energy on exchanging the messages and controlling the topology, while increasing the total energy of the network. Consequently, enlarging the energy heterogeneity factor can definitely extend the network lifetime, while the number of nodes should be deployed in reasonable quantity in the practical underwater scenarios. We also pay close attention to the impact of the end-to-end delay caused by the change of nodes number and energy heterogeneity parameter in UWSN. The variation of the average end-to-end delay which is under the different \(n\) and \(\lambda\) in TCEB is shown in Figure 5. As the energy heterogeneity factor \(\lambda\) enlarges, basically, the average end-to-end delay does not change much under the scenario of the same deployed nodes. The changes are only milliseconds, which means equipping more energy almost doe not influence the end-to-end delay. At the same time, it is also reflected from the side that the topology is timely scheduled to make nodes adapt to the new roles by dynamic adjustment, which ensures the QoS of the network. Furthermore, with the increase in the number of deployed nodes, the average end-to-end delay inevitably rises.