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Multi-Players Bandit Algorithms for Internet of Things Networks

Abstract : In this PhD thesis, we study wireless networks and reconfigurable end-devices that can access Cognitive Radio networks, in unlicensed bands and without central control. We focus on Internet of Things networks (IoT), with the objective of extending the devices’ battery life, by equipping them with low-cost but efficient machine learning algorithms, in order to let them automatically improve the efficiency of their wireless communications. We propose different models of IoT networks, and we show empirically on both numerical simulations and real-world validation the possible gain of our methods, that use Reinforcement Learning. The different network access problems are modeled as Multi-Armed Bandits (MAB), but we found that analyzing the realistic models was intractable, because proving the convergence of many IoT devices playing a collaborative game, without communication nor coordination is hard, when they all follow random activation patterns. The rest of this manuscript thus studies two restricted models, first multi-players bandits in stationary problems, then non-stationary single-player bandits. We also detail another contribution, SMPyBandits, our open-source Python library for numerical MAB simulations, that covers all the studied models and more.
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  • HAL Id : tel-02491380, version 1

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Lilian Besson. Multi-Players Bandit Algorithms for Internet of Things Networks. Signal and Image processing. CentraleSupélec, 2019. English. ⟨NNT : 2019CSUP0005⟩. ⟨tel-02491380⟩

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