## 4 Proposed Method
### Coverage Hole Repair Based on Mobile Node Redeployment
#### 4.2.2 Calculate the Number of Mobile Sensors Required
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**100 m \(\times\) 100 m**} & \multicolumn{3}{c}{**200 m \(\times\) 200 m**} & \multicolumn{3}{c}{**300 m \(\times\) 300 m**} \\ \cline{2-10}  & **40** & **50** & **70** & **150** & **200** & **250** & **300** & **400** & **500** \\ \hline VB-IVFA & 0.896 & 0.715 & 0.553 & 0.887 & 0.718 & 0.591 & 0.889 & 0.765 & 0.651 \\ VF-IALO & 0.893 & 0.711 & 0.549 & 0.891 & 0.705 & 0.593 & 0.895 & 0.772 & 0.658 \\ MEP-PSO & 0.852 & 0.684 & 0.523 & 0.765 & 0.651 & 0.586 & 0.872 & 0.635 & 0.639 \\ Our method & 0.923 & 0.732 & 0.561 & 0.895 & 0.737 & 0.598 & 0.901 & 0.785 & 0.663 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Node coverage efficiency comparison. Figure 8: Comparison of coverage rate under various sensing radii. Figure 9: Comparison of coverage rate. According to the analysis of the data in Table 4, it can be seen that within the same ROI, the coverage efficiency of the four algorithms tends to decrease with the increase in the number of sensors. The increased number of sensors will inevitably lead to an increase in redundant coverage, resulting in a decrease in coverage efficiency. By comparing the results of three different ROIs, it can be found that our method outperforms the other three methods in terms of coverage efficiency. Our method prioritizes overlapping coverage when repairing coverage holes, resulting in lower redundancy coverage. In addition, redundancy can be reduced, and coverage efficiency can be improved during the rotation sensing direction stage. In this section, we also compared the redundancy rate and redundancy area of different algorithms. As shown in Figure 10, the proposed method reduces the redundancy rate to below 10% after multiple iterations, which is significantly better than other methods. Because the MEP-PSO algorithm can only optimize the redundant coverage of two nodes at a time, its redundancy rate is higher than that of the other methods. The performance of the algorithm in terms of coverage efficiency and redundancy was verified through experiments in this section. Compared with other methods, our approach has higher coverage efficiency. Our algorithm's experimental results indicate that the sensors in the network have been fully utilized and have a high energy utilization rate. ### Comparison of Moving Distance

Energy consumption is an important indicator for evaluating the performance of coverage enhancement methods. During the process of node redeployment, moving energy consumption accounts for the vast majority of the total. Other energy consumption (communication, rotation) can be almost negligible. Therefore, the performance of the algorithm in terms of energy consumption can be determined by comparing the moving distance. In this experiment, the parameter settings are consistent with the previous experiment, and we compare the experimental results with the other three methods. Figure 11 shows the effects of the number of sensors, sensing angle, and sensing radius on the total moving distance. The total moving distance is negatively correlated with the above three parameters. The larger the number of sensors, sensing angle, and sensing radius, the smaller the total moving distance. Specifically, the total moving distance of the proposed method is the smallest; that of VB-IVFA and VF-IALO is slightly larger than that of the proposed method. The total moving distance of the MEP-PSO algorithm is the largest, as MEP-PSO focuses more on reducing redundancy and can only move one sensor at a time. The proposed method can expand the coverage range and reduce the area of coverage holes by rotating the stationary sensor. Therefore, the performance of the proposed method is superior to that of VB-IVFA and VF-IALO. Figure 10: Comparison of redundant rate. Uniformity of moving distance (UMD) can reflect the algorithm's performance in terms of energy balance. Maximum moving distance (MMD) is also an indicator for evaluating the effectiveness of algorithms.