# Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme
## 5 Conclusions  
In this work, we introduced a novel approach utilizing the Quad-Tree concept to address the wireless charging pad deployment problem, aiming to reduce the number of deployed pads. However, our proposed scheme may overlook the optimal pad placement due to insufficient segmentation, potentially leading to an increase in the number of pads required in the resulting configuration. Conversely, reducing the minimum unit of the Quad-Tree could increase the time needed for Quad-Tree division and also expand the number of positions that need to be considered, thus potentially prolonging the algorithm's running time. Therefore, further exploration and theoretical analysis are needed to determine the optimal size of Quad-Tree segmentation units. Additionally, determining a more precise upper bound on the number of pads used by the proposed method, considering the characteristics of the Quad-Tree, will be a direction for our future research.  
Furthermore, we recognize that other Quad-Tree-like geometric methods may offer additional opportunities for devising new pad deployment schemes in the future. These alternative methods could potentially enhance the efficiency and effectiveness of wireless charging pad deployment, warranting further investigation and research.  
The values of \(D_{maxcharging}\) and \(D_{maxquad}\) of every specific drone are quite ideal and different due to different battery size, flying speed, power consumption rates, charging speed, charging position and direction, etc. However, in real conditions, the two adopted system parameters should be much less than \(D_{maxcharging}\) and \(D_{maxquad}\) to guarantee the coverage and connectivity property of the constructed flight network. After adopting such large system parameters, the number of required pads and the costs would increase by applying our proposed algorithm. Another issue is the delay time between when a sensor issues a charging request and a drone visits (from the base station) and starts to charge the desired sensor, which should be shorter than the waiting time the sensor can accept. Similarly, when considering the delay time issue, either a sensor should issue its charging request early, or more pads need to be deployed to shorten the maximum shortest path between the sensor and base station in the network to meet the needs of the actual situations.  
Figure 17: Comparisons of (**a**) the required number of pads and (**b**) the average execution time of the proposed QT&MSC method while changing the minimal unit size of QT on large-scale maps.  
**Author Contributions:** Conceptualization, R.-H.C. and Z.-L.Z.; methodology, C.-W.Y.; software, R.-H.C.; validation, Z.-L.Z. and C.-W.Y.; writing--original draft preparation, Z.-L.Z. and R.-H.C.; writing--review and editing, Z.-L.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Wenzhou Science and Technology Commissioner Project (Grant No. X2023012), in part by 2023 Zhejiang Province Industry University Cooperation Collaborative Education Project (Grant No. 323), in part by the first batch of teaching innovation teams for teachers in higher vocational colleges in Wenzhou (Grant No. 1).  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank Chung Hua University for supporting this research.  
The authors declare no conflicts of interest.