  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.
