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Multi-Armed Bandit Learning in IoT Networks: Learning Helps Even in Non-stationary Settings

  • Rémi BonnefoiEmail author
  • Lilian Besson
  • Christophe Moy
  • Emilie Kaufmann
  • Jacques Palicot
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 228)

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, \(\mathrm {UCB}_1\) and Thomson 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.

Keywords

Internet of Things Multi-Armed Bandits Reinforcement learning Cognitive Radio Non-stationary bandits 

Notes

Acknowledgements

This work is supported by the French National Research Agency (ANR), under the projects SOGREEN (grant coded: N ANR-14-CE28-0025-02) and BADASS (N ANR-16-CE40-0002), by Région Bretagne, France, by the French Ministry of Higher Education and Research (MENESR) and ENS Paris-Saclay.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.CentraleSupélec (campus of Rennes), IETR, SCEE TeamCesson-SévignéFrance
  2. 2.Univ. Lille 1, CNRS, Inria, SequeL Team, UMR 9189 - CRIStALLilleFrance

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