Loading...

Multi-Armed bandit Learning in Iot Networks (MALIN) - Demo at ICT 2018

128 views

Loading...

Loading...

Transcript

The interactive transcript could not be loaded.

Loading...

Rating is available when the video has been rented.
This feature is not available right now. Please try again later.
Published on Jul 16, 2018

In this demo, we implement an IoT network the following way: one gateway, one or several intelligent (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 with no specific overhead for learning needs. We model the network access as a discrete sequential decision making, 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 load complexity and decentralized algorithms, such as UCB and Thompson Sampling.

For more information:
http://www-scee.rennes.supelec.fr/wp/...

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

The open-source GNU-Radio code used for this demo is available at:
https://bitbucket.org/scee_ietr/malin...

Video editing: Lilian Besson

Loading...

When autoplay is enabled, a suggested video will automatically play next.

Up next


to add this to Watch Later

Add to

Loading playlists...