# W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
## 4 Results and Discussion
### Analysis of Results
The existing techniques have utilized service quality and location of underwater relay nodes for making data delivery decisions. However, the proposed framework considers underwater network dynamics resulting in significant performance gain in the packet delivery ratio. This performance gain is further shown in a more scientifically understandable way in Figure 6. Here, the percentage gain as well as packet delivery ratio observations are presented close together to make it easy to highlight the performance benefits of the proposal in comparison with the literature. This is also helpful in validating \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{5}{c}{**Delay (ms) Comparison with Varying Number of Nodes**} & \multicolumn{5}{c}{**\% Improvements of W-GUN Compared with Existing Techniques**} \\ \hline \multirow{3}{*}{**Nodes**} & \multirow{3}{*}{**VBF**} & \multirow{3}{*}{**HH-VBF**} & \multirow{3}{*}{**VBVA**} & \multirow{3}{*}{**ES-VBF**} & \multirow{3}{*}{**W-GUN**} & \% **Gain of W-GUN**} & \% **Gain of W-GUN** \\  & & & & & & & **Compared with** & **Compared with** & **Compared with** \\  & & & & & & & **ES-VBF** & **VBVA** & **HH-VBF** \\ \hline
100 & 125 & 115 & 109.8 & 100 & 89 & 11 & 19 & 23 \\
200 & 120 & 109 & 100 & 78 & 66 & 15 & 34 & 39 \\
300 & 112 & 94 & 81 & 64 & 49 & 23 & 40 & 48 \\
400 & 98 & 78 & 65 & 53 & 35 & 34 & 46 & 55 \\
500 & 84.8 & 60 & 35.6 & 20 & 10 & 50 & 72 & 83 \\ \multicolumn{5}{c}{Average \% Improvement \(\rightarrow\)} & 27 & 42 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive performance observation of W-GUN in terms of end-to-end delay. Figure 4: Detailed percentage-centric performance gain of W-GUN focusing on end-to-end delay. A comparative analysis between the proposed framework and state-of-the-art techniques is presented in Figure 7 for energy consumption performance as a function of the number of underwater sensor nodes in the underwater network. It can be observed that the energy consumption is considerably lower for the proposed underwater relay optimization method W-GUN than the state-of-the-art techniques. It is evident that the optimal selection of underwater relay nodes reduces the energy consumption of the overall network, resulting in better utilization of the energy of underwater nodes. For lower energy consumption, the underwater routing path should be as optimal as possible. The proposed optimization framework selects the optimal number of underwater relay nodes for optimizing the energy usage at the node level. Therefore, the route consists of stable underwater nodes as well as the minimum number of underwater relay nodes in our proposed framework. In particular, it can be observed that energy consumption reaches more than 7000 j for VBVA with 500 sensor nodes in the underwater network. In the case of our proposal, it reaches up to approximately 5000 j with similar 500 sensor nodes underwater network density. Here, we want to clarify that the energy consumption is of the overall underwater network, considering all the sensor nodes' energy consumption. As we have specified in our experimental setting description, total simulation time considered for each simulation experiment was 1500 s and each point considered in the result is an average 50 simulation runs. Essentially, after our simulation time, overall 14% of underwater network energy has been consumed in the case of VBVA and approximately 10% of underwater network energy has been consumed in the case of the proposed framework. Therefore, energy consumption is lower considerably compared with existing schemes in the underwater literature. The overall energy consumption of the proposed framework is investigated in detail in Table 3 with a comparison to the literature, focusing on respective performance gain percentage. It can be noted that the average energy consumption performance gain of the proposed framework in percentage is 27%, 53%, and 46% for ES-VBF, VBVA and HH-VBF, respectively.