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- W4328006261 abstract "Jamming attack poses a severe threat to the application promise of underwater sensor networks (UWSNs) in marine monitoring and other domains. Coupled with the low bandwidth and the time unpredictability of transmission media, the typical security technology with significant computational complexity cannot be applied to UWSNs. Therefore, in this work, a distributed hierarchical game learning algorithm with high learning efficiency and without relying on any prior information is presented to realize the adaptive adjustment of the power of communication nodes. To begin, a multi-player Multi-armed bandit (MAB) game model is constructed to characterize the jamming problem. In this approach, each player's purpose is to maximize his own utility. Then, a game method based on softmax-greedy is developed to handle the jamming problem of UWSNs in unknown surroundings by observing and analyzing historical data. Subsequently, in order to further improve the learning efficiency and reduce algorithm learning time, a hierarchical game learning algorithm is presented. Finally, using simulations, the efficacy of the proposed anti-jamming learning algorithm in both reactive and non-reactive jamming modes is proven." @default.
- W4328006261 created "2023-03-22" @default.
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- W4328006261 date "2022-08-01" @default.
- W4328006261 modified "2023-09-27" @default.
- W4328006261 title "Jamming Games in Underwater Sensor Networks with Hierarchical Learning" @default.
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- W4328006261 doi "https://doi.org/10.1109/bigcom57025.2022.00060" @default.
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