In the disaster scene, the timely and reliable collection of first-hand and important information in the disaster area is the key to the early disaster warning research and rescue work. Unmanned aerial vehicle (UAV) is an efficient auxiliary tool for establishing emergency communication network within disaster zones. Through the investigation of the deployment methods of UAV in emergency scenarios in the existing research, we reported the problem that node energy efficiency is not considered in UAV deployment. For the first time, taking improving the energy efficiency of ground nodes as the optimization goal, based on the deep reinforcement learning method and on the basis of DDQN network model, the adaptive deployment problem of UAV for information collection in disaster scenarios is solved by defining experience playback priority, reasonably designing reward function and adopting complete decentralized training method. Simulation results show that the energy efficiency of the nodes under the proposed algorithm is 21% higher than that of the DDQN benchmark algorithm, and the training speed is 42% and 34% higher than that of the DDPG and A3C algorithms, respectively.

[2024-01-22] Accepted Paper /doc/2023.11.0537/v1