# Smartphone-Based Task Scheduling in UAV Networks for Disaster Relief
## 2 Related Works  
Recently, scholars at home and abroad have performed extensive studies on UAV task offloading. For instance, in [4], the authors proposed the Multi-Hop Task Offloading and real-time computing scheme, which fully utilizes the computational power of UAVs, and built a powerful remote edge-computing network with multiple UAVs. The authors addressed the issue of joint resource allocation and deployment and established two algorithms to resolve the proposed problem. [PERSON] et al. [5] designed the Smart and Trust Multi-UAV Task Offloading (STMTO) system, which utilized UAVs with a trusted style to offload tasks from Internet of Things (IoT) devices to edge servers. They further proposed a collaborative task collection scheme to minimize energy consumption and latency. By combining the Long Short-Term Memory (LSTM) task prediction algorithm with the UAV position optimization algorithm, [PERSON] et al. [6] designed a three-layer computing offloading strategy-based energy efficiency optimization algorithm, which reduced the energy consumption of UAVs by dynamically programming and adjusting the UAV Multi-Access Edge Computing (MEC) network. In [7], the authors proposed an innovative UAV-enabled MEC system to solve the service problem of IoT devices in poor-signal areas to minimize network service latency and UAV-weighted energy consumption. In addition, the authors of [8] proposed an optimal partial offloading scheme named Partial Offloading Strategy Maximizing the User task number (POSMU). Through analyzing the 3D communication model, they concluded that this scheme could be applied to UAV-supported MEC systems without a seam. In study [9], the authors proposed an intelligent task-offloading algorithm (iTOA) for the UAV edge-computing network to reduce the processing computational load of the UAV computing platform; their method can adaptively choose the offloading action by sensing the network environment. Additionally, [PERSON] et al. [10] developed an energy efficiency optimization algorithm for Block Coordinate Descent (BCD), which reduces the energy consumption of user terminals by making UAVs work together and executing the BCD algorithm when faced with blocked communication in a complex terrain. The authors of [11] conceptualized an energy-efficient computational offloading technique in the context of information security by considering the physical security layer; it also alleviates the computational pressure on UAVs when dealing with computational tasks with high offloading requirements. Moreover, many studies have been dedicated to post-disaster relief [12; 13; 14; 15]. In [12], the authors proposed a network architecture for UAVs and ground vehicles in which the UAV and the ground vehicle can communicate with each other through air-to-ground (A2G) communication. The UAVs in this system not only perform information acquisition but also act as relay stations when communication among ground vehicles is blocked. In [13], the authors explored a fog computing-based UAV system which allows for collaboration between UAVs and unmanned ground vehicles (UGVs) by designing and implementing a matching iterative algorithm. This scheme effectively improves the utilization of UAVs and reduces the average delay. [PERSON] et al. [2] developed an example of a UAV system that can quickly obtain disaster site information; it can be used to acquire disaster images with different resolution parameters and was applied to the 7.0 magnitude earthquake in Lushan, Sichuan, China. In [14], the authors described a data acquisition framework based on the collaborative processing of UAVs and IoT. In [15], the authors investigated UAVs by utilizing their mobile communication and computing functions to assist local area networks in building a UAV-assisted, multiple-access edge-computing system when the communication infrastructure is not available.