Energy-Efficient UAV Crowdsensing with Multiple Charging Stations by Deep Learning

Chi Harold Liu, Chengzhe Piao, Jian Tang

Research output: Chapter in Book/Entry/PoemConference contribution

45 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationINFOCOM 2020 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781728164120
StatePublished - Jul 2020
Event38th IEEE Conference on Computer Communications, INFOCOM 2020 - Toronto, Canada
Duration: Jul 6 2020Jul 9 2020

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Conference38th IEEE Conference on Computer Communications, INFOCOM 2020


  • UAV crowdsensing
  • charging stations
  • deep reinforcement learning
  • spatiotemporal modeling

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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