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- W3133109767 abstract "In Unmanned Aeronautical Ad hoc Networks (UAANETs), it is challenging for routing protocols such as ad hoc on-demand distance vector (AODV) routing and improved protocols to provide reliable communications due to limited wireless resources and high mobility. This paper proposes a modified AODV protocol applying multi-agent Q-learning(Modified-QLAODV) based on mobility prediction and load sensing. This protocol is a distributed multi-agent reinforcement routing strategy, which employs reinforcement learning algorithm. It introduces mobility prediction and node relative load to infer link state on the purpose of enhancing robustness of UAANETs. In the learning strategy, mobility prediction based on Kalman filter algorithm combined with Gaussian filter algorithm, and load sensing will have impacts on Q-values. The destination obtains routes with different Q-values and returns the one with the maximum average Q-value to the source, which realizes to select the optimal route from a global view. The simulation results demonstrate that Modified-QLAODV can outperform the Q-learning routing protocol(QLAODV) and AODV on network performance in UAANETs." @default.
- W3133109767 created "2021-03-01" @default.
- W3133109767 creator A5003753200 @default.
- W3133109767 creator A5090287539 @default.
- W3133109767 date "2020-11-01" @default.
- W3133109767 modified "2023-09-24" @default.
- W3133109767 title "A Modified Multi-Agent Reinforcement Learning Protocol Based on Prediction for UAANETs" @default.
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- W3133109767 doi "https://doi.org/10.1109/vtc2020-fall49728.2020.9348732" @default.
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