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We implement an IoT network the following way: one gateway, one or severalintelligent (i.e., learning) objects, embedding the proposed solution, and atraffic generator that emulates radio interferences from many other objects.Intelligent objects communicate with the gateway with a wireless ALOHA-basedprotocol, which does not require any specific overhead for the learning. Wemodel the network access as a discrete sequential decision making problem, andusing the framework and algorithms from Multi-Armed Bandit (MAB) learning, weshow that intelligent objects can improve their access to the network by usinglow complexity and decentralized algorithms, such as UCB1 and ThompsonSampling. This solution could be added in a straightforward and costless mannerin LoRaWAN networks, just by adding this feature in some or all the devices,without any modification on the network side.

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