# W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
## 5 Conclusions  
In this paper, we have presented an underwater relay node optimization framework (W-GUN) for enabling green computing in underwater networking. The underwater relay nodes are selected to cover the maximal paths with the help of an adapted whale and wolf optimization algorithm. The position of underwater relay nodes is varied and checked for optimal deployment to reduce the number of underwater relay nodes for green underwater networking. The adapted W-GUN framework has been designed by integrating underwater-centric optimization techniques, namely a whale and wolf optimization algorithm. Moreover, the performance of the proposed framework is compared with the existing techniques ES-VBF, VBVA and HH-VBF. The proposed methodology is analyzed in terms of various underwater-centric metrics including end-to-end delay, packet delivery ratio, energy consumption, network lifetime, and throughput by varying the number of underwater sensor nodes in the network. The average percentage improvements reflect the effectiveness of the proposed scheme compared with the state-of-the-art underwater techniques. In addition, from the results attained, it can be concluded that the proposed optimal underwater relay selection scheme is effective in terms of improving the network lifetime with less energy consumption. In future works, the authors will focus on using deep learning-centric optimization in order to further evolve the understanding of underwater network environments in optimal relay node selection and route optimization based on network dynamics.  
Conceptualization, R.S.R.; formal analysis, R.S.R.; investigation, R.S.R.; methodology, R.S.R.; resources, R.S.R.; supervision, S.S., O.K., R.K.; validation, S.M., O.K., R.K.; writing, R.S.R.; review & editing, S.M., O.K., K.A., H.S. All authors have read and agreed to the published version of the manuscript. This research was funded by School of Science and Technology, Nottingham Trent University, UK.  
The research work is also supported by the Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal (Sonepat), Haryana-131039, India.  
Figure 12: Detailed percentage-centric performance gain of W-GUN focusing throughput.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Throughput (Packets/Second) Comparison with Varying**} & \multicolumn{4}{c}{**Percentage Improvements of W-GUN Compared with Existing**} \\ \multicolumn{2}{c}{\multicolumn{2}{c}{**Number of Nodes**}} & \multicolumn{4}{c}{**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** & \% **Gain of W-GUN** \\  & & & & & & **Compared with** & **Compared with** & **Compared with** \\  & & & & & & **ES-VBF** & **HH-VBF** & **VBVA** \\ \hline
100 & 92 & 70 & 51 & 124 & 138 & 10 & 49 & 63 \\
200 & 117 & 93 & 70 & 138 & 155 & 11 & 40 & 55 \\
300 & 132 & 112 & 91 & 149 & 166 & 10 & 33 & 45 \\
400 & 144 & 134 & 112 & 156 & 174 & 10 & 23 & 36 \\
500 & 166 & 151 & 136 & 170 & 188 & 10 & 20 & 28 \\ \multicolumn{2}{c}{Average \% Improvement \(\rightarrow\)} & \multicolumn{4}{c}{10} & \multicolumn{4}{c}{33} & \multicolumn{4}{c}{45} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Descriptive performance observation of W-GUN in terms of throughput.