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Learning_Slotted_IoT_networks

This repository contains the code which has been used in order to generate the simulation results for our article:

Bonnefoi, R.; Besson, L.; Kaufmann, E.; Moy, C.; Palicot, J. “Multi-Armed Bandit Learning in IoT Networks:Learning helps even in non-stationary settings”, CROWNCOM, May 2017

This article can be found on Springer. A version is also available at: https://hal.archives-ouvertes.fr/hal-01575419/document

Short description

The main files allow to evaluate the performances of some learning algorithms (UCB and Thompson Sampling) in a networks composed of IoT devices. The aim of this file is to evaluate the performance of learning algorithms when the number of devices which use the network increases.

License

MIT Licensed (file LICENSE.txt).