## 4 Simulation Analysis
### Simulation Results in the Static Sink Scenario
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network Size** & **200** & **250** & **300** & **350** & **400** & **450** \\ \hline \(T_{r}=500\) m & 20.6 & 23.1 & 26.3 & 27.4 & 29.6 & 30.8 \\ \(550\) m & 21.7 & 24.1 & 26.1 & 25.9 & 28.1 & 28.7 \\ \(600\) m & 21.9 & 23.6 & 26.0 & 26.9 & 27.2 & 27.0 \\ \(650\) m & 22.2 & 21.9 & 23.4 & 25.4 & 26.9 & 26.0 \\ \(700\) m & 20.4 & 21.1 & 22.4 & 25.4 & 25.3 & 25.9 \\ \(750\) m & 19.9 & 18.7 & 22.9 & 24.7 & 25.3 & 27.4 \\ \(800\) m & 19.3 & 16.6 & 19.5 & 21.9 & 24.5 & 26.8 \\ \(850\) m & 18.6 & 17.9 & 21.3 & 23.6 & 26.2 & 28.2 \\ \(900\) m & 16.2 & 17.8 & 20.8 & 23.5 & 25.6 & 28.2 \\ \hline
**Energy Saved** & **20.1\%** & **20.5\%** & **23.2\%** & **25.0\%** & **26.5\%** & **27.7\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall energy saved by the proposed scheme compared to AHH-VBF. Figure 13: Overall network energy consumption for varying number of data packets generated by the source nodes in the network. are successfully propagated in the network, which consumes more network energy and results in a large number of dead nodes in the network, refer Figure 14c,d for further details. Similarly, in the dense network scenario, e.g., Figure 14d, the small transmission range (e.g., \(T_{r}=500\) m) consumes more energy due to the fact that most of the nodes participate in the Data packet forwarding. Hence, large number of nodes die out for small \(T_{r}=500\) m compared to the large \(T_{r}=900\) m. From the results, it can easily be seen that the battery power of a very small number of nodes is depleted during the simulation of ESEVBF. Resultantly, it increases the underwater monitoring duration and the network with ESEVBF can survive for a longer duration that the one using AHH-VBF. From the above analysis, it is clear that the proposed scheme saves energy, reduces broadcast storm, and communicates data packets with less delay. In addition to these performance gains, the average number of hops that each data packet traverse from \(S\) to \(D\) is also analyzed for different network sizes and \(T_{r}\), refer Figures 15 and 16, respectively. The hop count data coincides with our previous analysis that for a small \(T_{r}=500\) and network of 200 nodes, the average number of hops is smaller than the large \(T_{r}\)s. The rationale for this behavior is that the data messages from sources that are at distant location, fail to reach \(D\) due to unavailability of the path(s). However, when the network becomes dense, the data packet has to traverse many hops to reach \(D\) for \(T_{r}=500\). On the other hand, it can easily be noticed from the results that the average number of hop counts of the proposed scheme is smaller than the AHH-VBF because the forwarder selection criteria mostly considers the node's closeness towards the center of the virtual cylinder and the Sink node. Notwithstanding, the proposed scheme scales the holding time with residual energy factor plus the expanded ratio of the closeness towards the center of the virtual cylinder. This may increase the chances of other nodes to serve as potential forwarders that have large residual energy and slightly different virtual cylinder distance ratio. This is the reason that for large \(T_{r}\), the average number of hops traversed by the Data packet in ESEVBF is slightly larger or almost identical to the AHH-VBF. The impact of this slightly larger hop count is reduced by taking the advantage of utilizing the neighborhood information in holding time  
Figure 14: Number of dead nodes for varying number of data packets generated by the source nodes in the network. calculation and reducing the holding time of the potential forwarder that has a smallest holding time among neighbors. Therefore, this marginally high hop count factor has not much impact in presence of the other significant performance gains achieved by the proposed scheme.