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GNU Radio Implementation of MALIN: "Multi-Armed bandits Learning for Internet-of-things Networks"

Lilian Besson 1, 2, 3, 4, 5 Remi Bonnefoi 1, 2, 4, 5 Christophe Moy 1, 4, 6
3 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
4 SCEE - Signal, Communication et Electronique Embarquée
IETR - Institut d'Electronique et de Télécommunications de Rennes
Abstract : We implement an IoT network in the following way: one gateway, one or several intelligent (i.e., learning) objects, embedding the proposed solution, and a traffic generator that emulates radio interferences from many other objects. Intelligent objects communicate with the gateway with a wireless ALOHA-based protocol, which does not require any specific overhead for the learning. We model the network access as a discrete sequential decision making problem, and using the framework and algorithms from Multi-Armed Bandit (MAB) learning, we show that intelligent objects can improve their access to the network by using low complexity and decentralized algorithms, such as UCB1 and Thompson Sampling. This solution could be added in a straightforward and costless manner in LoRaWAN networks, just by adding this feature in some or all the devices, without any modification on the network side.
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Contributor : Lilian Besson Connect in order to contact the contributor
Submitted on : Tuesday, February 5, 2019 - 3:14:46 PM
Last modification on : Tuesday, January 11, 2022 - 3:17:03 AM
Long-term archiving on: : Monday, May 6, 2019 - 1:14:48 PM


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Lilian Besson, Remi Bonnefoi, Christophe Moy. GNU Radio Implementation of MALIN: "Multi-Armed bandits Learning for Internet-of-things Networks". IEEE WCNC 2019 - IEEE Wireless Communications and Networking Conference, Apr 2019, Marrakech, Morocco. ⟨10.1109/WCNC.2019.8885841⟩. ⟨hal-02006825⟩



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