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.

Keywords

• UAV cluster
• task offloading
• trajectory optimization
• deep reinforcement learning
• emergent disaster scenarios