# Three-Dimensional Trajectory Optimization for UAV-Based Post-Disaster Data Collection
## 2 Related Works  
UAVs offer significant potential for wireless communication applications [5, 6, 7, 8]. In general, wireless interfaces are compatible with various types of UAVs and can operate on both unlicensed Wi-Fi and licensed cellular frequency bands. Equipping UAVs with wireless communication capabilities paves the way for a wide range of new application domains, owing to the nature of technical enablers, e.g., channel modeling for aerial networks [6] and edge AI [9]. Within these domains, UAVs can typically assume one of three primary communication roles, as illustrated in Figure 1: (a) Aerial base stations (or access points)--UAVs can be deployed to provide wireless networking and communication services in specific geographic areas [10]. (b) UAVs as UE (user equipment) nodes--UAVs can utilize existing infrastructure (such as cellular networks or Wi-Fi) to communicate with each other or with ground-based devices [11]. (c) Aerial networks: UAVs can form an aerial network for specific applications, e.g., extending the coverage and enhancing the reliability of existing wireless networks [12], sensor network aggregators [13], post-disaster surveillance [14], or even a sensor network itself for localization purpose of ground-based wireless emitters [15]. Not only restricted to theoretical works, the practicality of UAV-based networks was also partially evaluated in field experiments [9, 16, 17]. This paper focuses on the first scenario, which attempts to establish an aerial network to gather sensing data from IoT devices or smartphones in isolated post-disaster areas. UAV-based data collection has been discussed in several previous studies as summarized in Table 1. In [18], the article dealt with the issue of improving rural connectivity. Path planning and UAV relays were conducted to improve quality in rural areas. Unfortunately, the paper only focused on the connectivity. In [19], the authors considered an obstacle-aware deployment of a UAV, where path optimization in obstacle-heavy environments was conducted. The UAV in this paper was employed as a relay rather than a data collector as considered in this paper. In [20], the authors proposed a disaster monitoring system via multi-UAV coordination. The paper only focused on UAV coordination but did not consider how to optimize data collection. In [21], a UAV-based surveillance system was introduced where multi-UAV collaboration was achieved via Deep Reinforcement Learning (DRL). Since this system was specific to surveillance, it is not generally applicable to the scenario of data collection considered in this paper. There were also works that focused on the use of UAVs for data collection. The author of [14] optimized the two-dimensional trajectory with the multi-armed bandit (MAB) algorithm, considering the energy consumption of the UAV and the UEs, to maximize the throughput. In [22], the throughput maximization problem was transformed to a constrained Markov decision-making process (CMDP), considering the energy consumption of the UEs. In addition, the author of [23] aimed to minimize mission completion time by optimizing the transmission power of UEs and the flight trajectory. In [24], flight altitude,  
Figure 1: UAV-based wireless communication networks. (a) UAVs as flying BS; (b) UAVs as UE; (c) UAVs as aerial NW. trajectory, velocity and link scheduling were considered for completion time minimization. Unfortunately, all these works were restricted to the 2D optimization of trajectory as compared to the 3D trajectory optimization in this paper. The relation between the flight altitude and the coverage is shown in [25], where higher altitude brings larger coverage via a high-altitude platform (LAP) rather than a UAV. In [26], three-dimensional locations of the UAV are decided with K-means clustering, without a discussion of data transmission. In [27], a three-dimensional trajectory is optimized to maximize the energy efficiency, considering the presence of wind.