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Q-Learning Based Adaptive Channel Selection for Underwater Sensor Networks

Antony Pottier 1, 2 Paul Mitchell François-Xavier Socheleau 1, 2 Christophe Laot 2, 1
1 Lab-STICC_IMTA_CACS_COM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : In this paper, we provide self-configuration and adaptation capabilities to Underwater sensor networks (UWSN) thanks to Q-learning. UWSN deployed for the long term over large areas for environmental monitoring are possible applications of our work. Sensor nodes deployed on the sea bottom are devoted to measure a physical quantity of interest transmitted to surface buoys considered as access points. Packet transmission are asynchronous and low overheads are desirable so as to save throughput and battery life. Prior to a transmission, the nodes choose, depending on the channel conditions, which access point maximizes the probability of successful decoding a the receiver side. Results show that Q-learning is able to perform close to an ideal "genie-aided" scheme, without the need of a detailed knowledge on the environment.
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-01891656
Contributor : François-Xavier Socheleau <>
Submitted on : Tuesday, October 16, 2018 - 4:39:53 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:46 PM
Long-term archiving on: : Thursday, January 17, 2019 - 12:35:00 PM

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  • HAL Id : hal-01891656, version 1

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Antony Pottier, Paul Mitchell, François-Xavier Socheleau, Christophe Laot. Q-Learning Based Adaptive Channel Selection for Underwater Sensor Networks. Underwater Communications and Networking, Aug 2018, Lerici, Italy. ⟨hal-01891656⟩

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