灾害场景下基于MADRL的信息收集无人机部署与节点能效优化

MADRL-based UAV deployment and node efficiency optimization for information collection in disaster scenarios

灾害场景下,对灾区内第一手重要信息的及时、可靠收集是灾害预警研究、灾区救援工作开展的关键。无人机是与灾区内部建立应急通信网络的高效辅助工具。通过对现有研究中应急场景下无人机的部署方法进行调查,指出了无人机部署时对节点能效考虑不充分的问题。由于地面传感器节点位于灾区内部,环境恶劣且极为被动,所以结合灾害场景,首次以提高地面节点能效为优化目标,基于深度强化学习方法,在DDQN网络模型基础上,通过自定义经验回放优先级、合理设计奖励函数和采用完全去中心化训练方式,解决该特定场景下用于信息收集无人机的自适应部署问题。仿真结果表明,所提算法的节点能源效率比DDQN基准算法提高21%,训练速度相比DDPG、A3C算法分别提升42%和34%。

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,this paper reported the problem that node energy efficiency was not considered in UAV deployment.Since the ground sensor nodes were located inside the disaster area in a hostile and extremely passive environment,the combination of disaster scenarios.Taking improving the energy efficiency of ground nodes as the optimization goal for the first time,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 was 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.