## 4 Simulation Analysis
### Simulation Results in the Static Sink Scenario
As data packets are large in size compared to the control packets, therefore, their contribution to the energy consumption dominates the energy consumed by the transmission of other packets or network operations. Therefore, the trends of the overall network energy consumption graphs are comparatively similar to the results that depict the total number of forwarded copies of the data packets in the network. Hence, the main reasons and arguments related to energy saving are similar to the one that avoids broadcast of more copies of the data packet. As a result, the overall energy consumption of the proposed scheme is less than the AHH-VBF. Figure 5:1 The overall percentage less End-to-End delay achieved by the proposed scheme for different \(T_{r}\) and network size. The simulation results show that the energy consumption in the sparse network is very small because most of the data packets could not be forwarded further in the upstream direction towards the Sink. Similarly, the number of forwarders, as well as the data packet receiving nodes (receiving energy consumption), are very few, which is one of the reasons for this small network energy consumption. Conversely, the opposite is the case for a dense network scenario where the successful communication of data packets increase energy consumption in the network. As the number of data sources are fixed in all the simulation scenarios, hence, we also recorded the overall network energy consumption after the individual broadcast of each data packet by the source node, refer Figure 5:2 The results show that maximum energy maximum energy saved by the proposed scheme is approximately 30.8%. The average percentage less energy consumed by the proposed scheme in comparison to AHH-VBF is summarized in Figure 5:3  
Figure 5:4 Overall network energy consumption versus the network size. Figure 5:5 Overall network energy consumption versus the transmission range. As we already discussed that more energy is consumed by the underwater acoustic networks, which can deplete the battery power of some nodes (dead nodes) during the simulation duration. Therefore, we also studied the number of dead nodes during the simulation period, as shown in Figure 5:6 It is evident from the figure that the battery power of few nodes is completely consumed in the network scenario of 200 nodes and \(T_{r}=500\) m, Figure 14a, because a small number of data traffic is handled by the network. In the similar network scenario but for the large \(T_{r}\), more network nodes die out, because more data packets are communicated in the network. The battery power of more number of nodes deplete when the network becomes denser. However, it can easily be seen from the results that for \(T_{R}=700\) m, the number of dead nodes is larger than the \(T=900\) and \(500\) m, refer Figure 14a. The obvious phenomenon behind this is that in an extremely dense network scenario, a large number of potential forwarders get the chance to forward data messages because of a very small difference between their holding time. Therefore, the collision probability at the next hop increases and the next hop nodes fail to further communicate the data packet. Hence, the data packet communication is restricted only to a few hops in the network. On the other hand, in \(T_{r}=700\) m, more data packets