Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings

Abstract : Setting up the future Internet of Things (IoT) networks will require to support more and more communicating devices. We prove that intelligent devices in unlicensed bands can use Multi-Armed Bandit (MAB) learning algorithms to improve resource exploitation. We evaluate the performance of two classical MAB learning algorithms, UCB1 and Thompson Sampling, to handle the decentralized decision-making of Spectrum Access, applied to IoT networks; as well as learning performance with a growing number of intelligent end-devices. We show that using learning algorithms does help to fit more devices in such networks, even when all end-devices are intelligent and are dynamically changing channel. In the studied scenario, stochastic MAB learning provides a up to 16% gain in term of successful transmission probabilities, and has near optimal performance even in non-stationary and non-i.i.d. settings with a majority of intelligent devices.
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Communication dans un congrès
CROWNCOM, Sep 2017, Lisbon, Portugal. <http://crowncom.org/2017/>
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https://hal.archives-ouvertes.fr/hal-01575419
Contributeur : Rémi Bonnefoi <>
Soumis le : samedi 19 août 2017 - 21:31:18
Dernière modification le : jeudi 21 septembre 2017 - 15:21:46

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BBMKP_CROWNCOM_2017.pdf
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Distributed under a Creative Commons Paternité - Pas d'utilisation commerciale - Partage selon les Conditions Initiales 4.0 International License

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  • HAL Id : hal-01575419, version 1

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Rémi Bonnefoi, Lilian Besson, Christophe Moy, Emilie Kaufmann, Jacques Palicot. Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings. CROWNCOM, Sep 2017, Lisbon, Portugal. <http://crowncom.org/2017/>. <hal-01575419>

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