Research on the UAV-aided data collection and trajectory design based on the deep reinforcement learning - Zhiyu MOU; Yu ZHANG; Dian FAN; Jun LIU; Feifei GAO (物联网学报)
The Internet of things (IoT) era needs to realize the wide coverage and connections for the IoT nodes.However,the IoT communication technology cannot collect data timely in the remote area.UAV has been widely used in the IoT wireless sensor network for the data collection due to its flexibility and mobility.The trajectory design of the UAV assisted sensor network data acquisition was discussed in the proposed scheme,as well as the UAV charging demand in the data collection process was met.Specifically,based on the hierarchical reinforcement learning with the temporal abstraction,a novel option-DQN (option-deep Q-learning) algorithm targeted for the discrete action was proposed to improve the performance of the data collection and trajectory design,and control the UAV to recharge in time to ensure its normal flight.The simulation results show that the training rewards and speed of the proposed method are much better than the conventional DQN (deep Q-learning) algorithm.Besides,the proposed algorithm can guarantee the sufficient power supply of UAV by controlling it to recharge timely.