# UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios
###### Abstract  
Natural disasters often have an unpredictable impact on human society and can even cause significant problems, such as damage to communication equipment in disaster areas. In such post-disaster emergency rescue situations, unmanned aerial vehicles (UAVs) are considered an effective tool by virtue of high mobility, easy deployment, and flexible communication. However, the limited size of UAVs leads to bottlenecks in battery capacity and computational power, making it challenging to perform overly complex computational tasks. In this paper, we propose a UAV cluster-assisted task-offloading model for disaster areas, by adopting UAV clusters as aerial mobile edge servers to provide task-offloading services for ground users. In addition, we also propose a deep reinforcement learning-based UAV cluster-assisted task-offloading algorithm (DRL-UCTO). By modeling the energy efficiency optimization problem of the system model as a Markov decision process and jointly optimizing the UAV flight trajectory and task-offloading policy to maximize the reward value, DRL-UCTO can effectively improve the energy use efficiency of UAVs under limited-resource conditions. The simulation results show that the DRL-UCTO algorithm improves the UAV energy efficiency by about 79.6% and 301.1% compared with the DQN and Greedy algorithms, respectively. U  
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computational tasks within a tolerable time range of the users, which will make it difficult to ensure the quality of service for the users. Second, due to the limitation of the volume and load-bearing capacity of the UAV, the battery capacity of the UAV is limited [9; 10; 11], which makes it unable to work continuously for a long time, and can only perform post-disaster auxiliary communication within a limited time and area. Furthermore, if the UAV is under high-load computing for a long time, it will shorten its battery life faster [12]. Therefore, it is necessary to make reasonable use of the flexible communication methods of UAVs and find a suitable solution for UAV-assisted post-disaster communication work to achieve effective and timely post-disaster rescue and significantly increase the sustainable operation time of UAVs. There are many research attempts to solve the task-offloading problem of UAVs in disaster areas, partly mainly through the cooperation of UAVs and edge computing servers, allowing UAVs to act as communication relays to forward computational tasks to adjacent mobile edge computing servers, and completing computationally intensive tasks through the more substantial computing power of the servers [13; 14]. Another part of the research focuses on offloading computational tasks to a more distant cloud server by UAVs, which completes the processing of complex tasks through the mighty computing power of the cloud server, and later transmits the computational results of the tasks back to the ground users via UAVs [15; 16]. However, the current work does not take into account the lack of computing power of a single UAV, which may not be able to carry out a large number of tasks for computation and forwarding. Moreover, due to the impact of natural disasters, edge computing servers in disaster-stricken areas are often unavailable or even damaged, making it impossible to provide computing offloading services for UAVs. In addition, the long transmission distance and the time delay between UAVs and remote cloud servers, and offloading tasks to cloud servers via UAVs will make it difficult to guarantee the quality of service for ground users. Considering the sudden and unpredictable unknowns of natural disasters, hardly any current works find an effective solution to the problem of limited UAV resources and user survival cycle limitations. Therefore, it is urgent to design a reasonable solution for the UAV-assisted post-disaster communication problem.