# Three-Dimensional Trajectory Optimization for UAV-Based Post-Disaster Data Collection
## 6 Conclusions  
In this article, we considered the feasibility of UAV-based post-disaster data collection by proposing a three-dimensional trajectory, which optimized the UAV's flight altitude. We transformed the problem to an LSCP solved by a GA, and compared it with the traditional K-means clustering approach. From the simulation results, the proposed method showed better efficiency than the traditional K-means approach, covering all UEs with fewer hovering points. In addition, the effect of completion time reduction by the three-dimensional trajectory proposed in this article is also confirmed, which indicates that the optimization of the UAV's flight altitude is effective for completion time reduction. However, this article focused on the optimization of UAV trajectories, which mainly contributes to the reduction of flight time. Although flight time occupies a large proportion in the mission completion time, the hovering time for data collection and other advanced strategies, e.g., [40, 41], also needs to be considered in future studies. Additionally, in real situations, environmental conditions such as obstacles like buildings, weather such as wind, rain, etc., also affect the flight of UAVs. For future issues of trajectory optimization, it is necessary to adapt advanced simulation models such as wind models, building models, etc. More practical numerical evaluations and demonstration experiments in real fields will be investigated in our future works. Conceptualization, R.Z. and G.K.T. ; methodology, R.Z. ; software, R.Z. ; validation, R.Z. ; formal analysis, R.Z. ; investigation, R.Z. ; data curation, R.Z. ; writing--original draft  
Figure 17: Completion time with varying numbers of UEs. Purple line shows the time reduction rate. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of UEs**} & \multicolumn{2}{c}{**Total Time (s)**} & \multicolumn{2}{c}{**Flight Time (s)**} & \multicolumn{2}{c}{**Hovering Time (s)**} \\  & **Previous Method** & **Proposed** & **Previous Method** & **Proposed** & **Previous Method** & **Proposed** \\  & & **Method** & **Method** & **Method** & **Method** \\ \hline
30 & 325.6855 & 282.3203 & 240.3592 & 195.8832 & 85.3263 & 86.4371 \\
40 & 382.1829 & 327.8415 & 268.6145 & 210.7790 & 113.7684 & 117.0625 \\
50 & 435.2349 & 371.6282 & 293.0245 & 224.9754 & 142.2105 & 146.0774 \\
60 & 484.0537 & 410.4997 & 313.4011 & 233.1659 & 170.6526 & 177.3338 \\
70 & 532.5094 & 446.7737 & 333.4147 & 239.3245 & 199.0947 & 207.4491 \\
80 & 583.8943 & 484.3886 & 356.3576 & 246.9312 & 227.5368 & 237.4574 \\ \hline \hline \end{tabular}
\end{table} 
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