# Underwater Sensor Network Redeployment Algorithm Based on Wolf Search
## 5 Simulation and Performance Analysis
### Simulation Results and Analysis  
The UWSN node deployment process was simulated with Matlab (R2011b, MathWorks, Natick, MA, USA) based on the background of Xixi Wetland water environment monitoring. The underwater monitoring range was set to 200 \(\times\) 200 \(\times\) 200 m\({}^{3}\), and 20 obstacles were randomly distributed in the water. Initially, 100 nodes were randomly distributed in the monitored water area, and 200 target monitoring points were randomly distributed in the wetland water environment. In the Matlab simulation environment, the process of node deployment and optimization were simulated to verify the performance of the RAWS algorithm. The meaning and value of the simulation parameters are shown in Table 1.  
Figure 4 shows the network coverage rate of the OAFSA and RAWS algorithms with the change in the number of iterations. The graph shows that the network coverage rate of the OAFSA algorithm stabilizes after 10 iterations. However, the RAWS algorithm stops the iteration after 25 times. The nodes will continue to jump out of the local optimum and find the global optimal solution because the nodes in the RAWS algorithm will not fail in the process of moving. Some of the nodes in the OAFSA algorithm encounter obstacles in each iteration. According to the simulation data (Figure 5), when all iterations are completed, the 12 nodes have been failed. Therefore, the OAFSA network coverage rate increases 66% and no longer changes. The RAWS algorithm finally makes the network coverage rate reach 75%. At the initial stage of the iteration, a node can avoid obstacles when it judges the surrounding obstacles. It can also avoid the local optimal value and improve the network coverage. After 11 iterations, the nodes are still searching for the global optimal solution, and the coverage rate is improved by a small margin. The search stops after 25 iterations. This result shows that the network coverage rate of the RAWS algorithm is better than that of the OAFSA algorithm with the same number of iterations in the network. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Sensing radius R (m) & 30 \\ Communication radius r (m) & 60 \\ Initial energy consumption Eint (J) & 10 \\ Carrier frequency f (KHz) & 25 \\ Energy consumption of data reception Pr (mW) & 3 \\ Uncertainty factor Re (m) & 15 \\ Moving step length s (m) & 0 \(<\) s \(<\) 30 \\ Moving speed \(\partial\) (m/s) & 1 \\ Relevant measuring equipment parameters \(\alpha\) & 0.2 \\ Relevant measuring equipment parameters \(\beta\) & 2 \\ Obstacle number Obs & 20 \\ Common node number Nod & 100 \\ Energy diffusion factor \(\eta\) (Khz) & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 5 presents the OAFSA and RAWS algorithms in the iteration process with the number of failure nodes. Figures 6 and 7 show the 3D distribution of the invalid nodes of the OAFSA algorithm with two and 10 iterations, respectively. As shown in Figure 6, the nodes can avoid obstacles in time and do not experience node failure in the iteration process of the RAWS algorithm. In the early stage of the iteration process of the OAFSA algorithm, several nodes encounter obstacles. In second iteration, six invalid nodes exist. When the number of iterations reach 10, the nodes cease moving because the UWSN has reached the maximum coverage, and the number of failed nodes increases to 12 (Figure 7). The simulation results show that the RAWS algorithm can effectively avoid obstacles in water and prevent node failure resulting from external force. Thus, the stability of the underwater wireless sensor network topology is ensured. Figure 8 presents a comparison chart of the network coverage rate of the OAFSA and RAWS algorithms. Regardless of the amount of obstacles in the water, because the individual search of each node is independent, the RAWS algorithm can always maintain a relatively high coverage rate in the process of moving.