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 ...

Liu et al. [24] proposed energy-efficient drone crowdsensing schemes based on randomly deployed charging stations, by leveraging deep reinforcement learning.

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.

Jul 6, 2020 · 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 ...

Different from using human-centric mobile devices like smartphones, unmanned aerial vehicles (UAVs) can be utilized to form a new UAV crowdsensing paradigm, ...

This paper proposes a distributed control framework for energy-efficient and DIstributed VEhicle navigation with chaRging sTations, called “e-Divert”, ...

Energy-Efficient UAV Crowdsensing with Multiple Charging Stations by Deep Learning. Chi Harold Liu and Chengzhe Piao (Beijing Institute of Technology, ...

It is a distributed multi-agent deep reinforcement learning (DRL) solution, which uses a convolutional neural network (CNN) to extract useful spatial features.

Distributed and energy-efficient mobile crowdsensing with charging stations by deep reinforcement learning. CH Liu, Z Dai, Y Zhao, J Crowcroft, D Wu, KK Leung.

Apr 27, 2024 · In this paper, we propose a distributed control framework for energy-efficient and DIstributed VEhicle navigation with chaRging sTations, called ...