期刊
IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS
卷 -, 期 -, 页码 199-208出版社
IEEE
DOI: 10.1109/infocom41043.2020.9155535
关键词
UAV crowdsensing; spatiotemporal modeling; deep reinforcement learning; charging stations
类别
资金
- National Natural Science Foundation of China [61772072]
Different from using human-centric mobile devices like smartphones, unmanned aerial vehicles (UAVs) can be utilized to form a new UAV crowdsensing paradigm, where UAVs are equipped with build-in high-precision sensors, to provide data collection services especially for emergency situations like earthquakes or flooding. In this paper, we aim to propose a new deep learning based framework to tackle the problem that a group of UAVs energy-efficiently and cooperatively collect data from low-level sensors, while charging the battery from multiple randomly deployed charging stations. Specifically, we propose a new deep model called j-PPO+ConvNTM which contains a novel spatiotemporal module Convolution Neural Turing Machine (ConvNTM) to better model long-sequence spatiotemporal data, and a deep reinforcement learning (DRL) model called j-PPO, where it has the capability to make continuous (i.e., route planing) and discrete (i.e., either to collect data or go for charging) action decisions simultaneously for all UAVs. Finally, we perform extensive simulation to show its illustrative movement trajectories, hyperparameter tuning, ablation study, and compare with four other baselines.
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