# UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios
###### Abstract
Based on the above, this paper proposes a real-time and efficient UAV cluster-assisted task-offloading solution for the user task-offloading problem under the damaged ground communication infrastructure scenario in disaster areas. This solution enables UAV clusters to form an aerial mobile edge server by introducing clusters of UAVs with intelligent characteristics [17; 18] to provide assisted communication and computational offloading services to ground users. The state transfer function is simplified by modeling the ground user task-offloading policy selection and UAV cluster flight trajectory formulation as Markov decision processes. Then, deep reinforcement learning algorithms are combined to jointly optimize user task-offloading policies and UAV cluster flight trajectories to maximize system energy efficiency, therefore further extending the system lifecycle. The solution not only overcomes the shortage of computing power of a single UAV, but also solves the problem of dynamic changes in the environment. The main contributions of this paper are summarized as follows:  
* This paper proposes a UAV cluster-assisted task-offloading model in the disaster scenario. To ensure that the communications of ground users in the disaster area are repaired promptly, we adopt an aerial mobile edge server composed of UAV clusters to provide assisted communications and computational offloading services for ground users. The ground users first offload the computational tasks to the UAVs with free resources, and then the UAVs forward the task data within the cluster to collaboratively complete the tasks. The model significantly reduces the transmission energy consumption between ground users and traditional edge servers and overcomes the problem of the limited computing power of UAVs. * This paper proposes a deep reinforcement learning-based UAV cluster-assisted task-offloading algorithm (DRL-UCTO) for jointly optimizing UAV flight trajectory and ground user task-offloading policy, taking full advantage of the high mobility and flexible communication of UAVs. This paper simplifies the deep reinforcement learning state transfer model through the Markov decision process to make the modeling process feasible, and optimizes the UAV flight trajectory through deep reinforcement learning, so that the DRL-UCTO algorithm quickly locates the location of ground users and makes optimal task offloading and forwarding decisions. The problem of limited resources in the disaster area is solved by maximizing the energy efficiency of the system while guaranteeing the quality of service for users. * In this paper, we verify the feasibility and effectiveness of the DRL-UCTO algorithm through extensive simulation experiments. Because of the stable action selection policy of the DRL-UCTO algorithm, the flight energy overhead of the UAV cluster during the search for ground users is reduced. The numerical results show that compared with other baseline algorithms, the DRL-UCTO algorithm significantly improves system transmission rate and throughput, and further increases system energy efficiency. The rest of this paper is organized as follows. Section 2 describes the current work related to UAV applications. In Section 3, a system model using UAV clusters as aerial mobile edge servers is described, and optimization problems are presented. In Section 4, we propose the deep reinforcement learning-based UAV cluster-assisted task-offloading algorithm (DRL-UCTO) to jointly optimize the UAV cluster flight trajectory and task-offloading policy. Section 5 gives the simulation experimental results and performance evaluation. Finally, Section 6 describes the conclusions and future work.