# Three-Dimensional Trajectory Optimization for UAV-Based Post-Disaster Data Collection
## 3 System Architecture  
As shown in Figure 2, the simulation scenario in this article is that a single UAV collects data from user equipment (UE) such as smartphones and tablets owned by users in the isolated disaster area. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Application Focus** & **Key Technologies** & **Contributions** & **Limitations** \\ \hline
[18] & Rural connectivity enhancement & Path planning and UAV relays & Focused on improving link quality in rural zones & Limited to rural; not scalable for high-mobility urban networks \\ \hline
[19] & Obstacle-aware deployment & Area barrier-aware deployment & Path optimization in obstacle-heavy environments & Focuses on fixed deployment scenarios \\ \hline
[20] & Disaster monitoring & Multi-UAV coordination & Disaster-focused UAV coordination and communication & Does not focus on data collection optimization \\ \hline
[21] & Surveillance via DRL & Deep Reinforcement Learning (DRL) & Multi-UAV collaboration using DRL & Specific to surveillance, not general data collection \\ \hline
[14] & Data collection & Multi-armed bandit (MAB) & Consideration of energy consumption of UAV and UE & 2D trajectory optimization \\ \hline
[22] & Data collection & Markov decision making process (CMDP) & Consideration of energy consumption of UE & No consideration of energy consumption of UAV \\ \hline
[23] & Data collection & Traveling salesman problem (TSP) & Completion time minimization & 2D trajectory optimization \\ \hline
[24] & Data collection & Segment-based trajectory optimization algorithm (STOA) & Joint optimization of trajectory and link scheduling & 2D trajectory optimization \\ \hline
[25] & Data collection & Single LAP covering a whole area & Coverage enhancement & Limited data rate \\ \hline
[26] & IoT networks & K-means clustering & Energy harvesting efficiency & No discussion on data transmission \\ \hline
[27] & Flying base station & Offline-based online adaptive (OBOA) design & Wind consideration & No discussion on completion time \\ \hline
[28] & Flying base station & 3D placement & Maximum coverage of UEs with different QoS requirements & No discussion on completion time \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related works. The UAV is assumed to collect data from the UEs when hovering, and there is no data transmission between the UAV and the UEs during flight. This assumption was made due to the challenges in maintaining stable and efficient data collection from multiple UEs. In related research where data is collected from a single UE at one time, it is relatively simple to establish and maintain the communication link between the UAV and the UE. However, when multiple UEs are involved, the movement of the UAV causes frequent changes in coverage area, leading to intermittent and potentially inefficient data collection. Therefore, this assumption allows us to simplify the system and ensure more reliable communication between the UAV and multiple UEs. As shown in Figure 3, the three-dimensional trajectory proposed by this article optimizes the UAV's flight altitude. In areas with dense UE distributions, the UAV's flight altitude is increased to achieve a larger coverage area. In contrast, in areas with scattered UE distributions, the UAV's flight altitude is reduced to achieve higher throughput. The detailed process of the data collection mission is shown below:  
1. Collect location information from UEs using other systems such as HAPS, LEO, etc. [29; 30; 31; 32; 33];
2. The UAV departs from the starting point;
3. The UAV flies to the next hovering point;
4. The UAV collects data from all covered UEs;
5. Repeat 3-4 until all UEs' data is collected;
6. The UAV returns to the starting point;
7. Send the collected data to the disaster response headquarters. Note that for simplicity, 1 and 7 are not covered in this article. For simulation, a \(500\times 500\) m flat urban area is assumed as the simulation environment. As shown in Figure 4, \(M\) UEs are randomly distributed in this area.