## 4 Proposed Method
### Coverage Hole Repair Based on Mobile Node Redeployment
#### 4.2.2 Calculate the Number of Mobile Sensors Required
Therefore, we designed a set of experiments to calculate UMD and MMD using different methods. Table 5 compares the performance of the four algorithms on the TMD, UMD, and MMD metrics. It can be seen that the proposed method outperforms the compared methods in these three indicators. The advantages in MMD and UMD metrics indicate that the proposed method is more in line with the requirements of network energy balance. Under the same conditions, the network optimized by the proposed method has a longer network lifetime. Figure 11: Comparison of moving distance. (**a**) Results of different number of stationary sensors. (**b**) Results of different sensing angles. (**c**) Results of different sensing radius. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **TMD (m)** & **UMD (m)** & **MMD (m)** \\ \hline Our method & 135.52 & 7.85 & 12.91 \\ VB-IVFA & 146.35 & 9.67 & 16.25 \\ VF-IALO & 178.63 & 10.23 & 22.27 \\ MEP-PSO & 205.32 & 11.77 & 28.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of moving distance. ## 6 Conclusions

In this work, we enhanced the coverage performance of the IoT sensing layer by optimizing the deployment of nodes. The proposed method comprehensively considers coverage optimization and the balance of mobile energy consumption, and fully uses the directional sensor's rotation and mobility. This method can achieve coverage optimization with less mobile energy consumption. The proposed method is divided into two stages. Firstly, we adjust the sensing direction by rotating the sensor to reduce coverage redundancy and expand the effective coverage area. Then, we use the improved sparrow search algorithm to search for the MEP, which is the optimal repair path sought. Finally, a mobile node redeployment method is designed to schedule nodes for coverage enhancement. We used multiple evaluation metrics and conducted multiple sets of simulation experiments to verify the performance of the proposed method. The experimental results show that our method performs better than other methods. In the future, we will further expand this research on the basis of this work. One of the future works will be to consider the impact of obstacles on mobile nodes. In addition, introducing self-powered sensors to optimize network performance is another interesting research direction. Conceptualization, P.W. and Y.X. ; methodology, P.W. and Y.X. ; software, P.W. ; validation, P.W. and Y.X. ; formal analysis, P.W. ; investigation, P.W. ; resources, P.W. ; data curation, Y.X. ; writing--original draft preparation, P.W. ; writing--review and editing, P.W. and Y.X. ; visualization, P.W. ; supervision, Y.X. ; project administration, Y.X. ; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China under Grant 61873249, and in part by the Hubei Provincial Natural Science Foundation of China under Grant 2015 CFA010. The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflicts of interest. ## References

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