# Underwater Sensor Network Redeployment Algorithm Based on Wolf Search
## 5 Simulation and Performance Analysis
### Simulation Results and Analysis
When the optimal solution is found, only one hop neighbor node moves, which shows the advantage of the distributed algorithm over the large-scale aggregation behavior of nodes in the OAFSA algorithm. The more obstacles exist in the water, the more the OAFSA algorithm will fail and the lower the network coverage will be. When the obstacles are distributed in the water, they are random and may overlap with the position of the target monitoring points of the same random distribution. After a node meets an obstacle, it will miss the coverage of the target monitoring point  
Figure 4: Comparison of network coverage rate at varying iterations. Figure 5: Comparison of the number of ineffective nodes at varying iterations. that is close to the obstacle. These two points cause the RAWS algorithm's coverage to be reduced by a small margin with the increase in the number of obstacles. Figure 9 illustrates the network coverage rate of the OAFSA and RAWS algorithms with the number of nodes in the network. As shown in the figure, with the increase in the number of nodes in the monitoring space, the coverage rate of the two algorithms increases. However, the RAWS algorithm  
Figure 8: Comparison of network coverage rate at varying numbers of obstacles. Figure 6: Layout of ineffective nodes in the second iteration. Figure 7: Layout of ineffective nodes in the tenth iteration. always obtains a coverage rate that is higher than that of OAFSA algorithm because any two nodes will not move in the same direction in the RAWS algorithm unless a common goal exists. The RAWS algorithm can search multiple regions of the monitoring space without a wide range of information exchange. This capability ensures that nodes avoid local optimization, expands the scope of the node search, and improves the quality of coverage. In addition, the use of congestion control improves the coverage effect. Figure 10 shows the change in network average residual energy with the network running round number. Because the RAWS algorithm uses an independent search method in the underwater moving process, information is transmitted only in the neighbor nodes. Thus, the average residual energy of nodes in the RAWS algorithm is generally higher than that of the OAFSA algorithm. The entire group does not need to interact. This condition significantly reduces the node energy consumption in the data transmission process and extends the entire network's running time. This is one of the advantages of the improved wolf search algorithm over other intelligent algorithms. Figure 11 shows the change in a network coverage rate with the monitored targets' number. Obviously, when the monitored targets' number is 50, the two algorithms' coverage rate is 1. It shows that all monitored targets are covered. When the monitored targets' number is 100, the coverage rate  
Figure 10: Comparison of average residual energy of nodes at varying numbers of nodes. Figure 9: Comparison of network coverage rate at varying numbers of nodes. of RAWS algorithm is one. The coverage rate of OAFSA algorithm is 0.89. It shows that all monitored targets are covered. It shows that some nodes are invalid in the OAFSA algorithm due to obstacles. Naturally, the coverage rate is lower with the increase of monitored targets in the OAFSA algorithm.