Multi-Agent Graph Reinforcement Learning based On-Demand Wireless Energy Transfer in Multi-UAV-aided IoT Network
This paper proposes a new on-demand wireless energy transfer (WET) scheme of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies that simply pursuing the total or the minimum harvested energy maximization at the Internet of Things (IoT) devices, where the IoT devices' own ener...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
27.07.2023
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Subjects | |
Online Access | Get full text |
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Summary: | This paper proposes a new on-demand wireless energy transfer (WET) scheme of
multiple unmanned aerial vehicles (UAVs). Unlike the existing studies that
simply pursuing the total or the minimum harvested energy maximization at the
Internet of Things (IoT) devices, where the IoT devices' own energy
requirements are barely considered, we propose a new metric called the
hungry-level of energy (HoE), which reflects the time-varying energy demand of
each IoT device based on the energy gap between its required energy and the
harvested energy from the UAVs. With the purpose to minimize the overall HoE of
the IoT devices whose energy requirements are not satisfied, we optimally
determine all the UAVs' trajectories and WET decisions over time, under the
practical mobility and energy constraints of the UAVs. Although the proposed
problem is of high complexity to solve, by excavating the UAVs' self-attentions
for their collaborative WET, we propose the multiagent graph reinforcement
learning (MAGRL) based approach. Through the offline training of the MAGRL
model, where the global training at the central controller guides the local
training at each UAV agent, each UAV then distributively determines its
trajectory and WET based on the well-trained local neural networks. Simulation
results show that the proposed MAGRL-based approach outperforms various
benchmarks for meeting the IoT devices' energy requirements. |
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DOI: | 10.48550/arxiv.2307.14626 |