  This paper describes the principles and implementation results of
reinforcement learning algorithms on IoT devices for radio collision mitigation
in ISM unlicensed bands. Learning is here used to improve both the IoT network
capability to support a larger number of objects as well as the autonomy of IoT
devices. We first illustrate the efficiency of the proposed approach in a
proof-of-concept based on USRP software radio platforms operating on real radio
signals. It shows how collisions with other RF signals present in the ISM band
are diminished for a given IoT device. Then we describe the first
implementation of learning algorithms on LoRa devices operating in a real
LoRaWAN network, that we named IoTligent. The proposed solution adds neither
processing overhead so that it can be ran in the IoT devices, nor network
overhead so that no change is required to LoRaWAN. Real life experiments have
been done in a realistic LoRa network and they show that IoTligent device
battery life can be extended by a factor 2 in the scenarios we faced during our
experiment.
