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This paper describes the principles and implementation results ofreinforcement learning algorithms on IoT devices for radio collision mitigationin ISM unlicensed bands. Learning is here used to improve both the IoT networkcapability to support a larger number of objects as well as the autonomy of IoTdevices. We first illustrate the efficiency of the proposed approach in aproof-of-concept based on USRP software radio platforms operating on real radiosignals. It shows how collisions with other RF signals present in the ISM bandare diminished for a given IoT device. Then we describe the firstimplementation of learning algorithms on LoRa devices operating in a realLoRaWAN network, that we named IoTligent. The proposed solution adds neitherprocessing overhead so that it can be ran in the IoT devices, nor networkoverhead so that no change is required to LoRaWAN. Real life experiments havebeen done in a realistic LoRa network and they show that IoTligent devicebattery life can be extended by a factor 2 in the scenarios we faced during ourexperiment.

Decentralized Spectrum Learning for IoT Wireless Networks Collision Mitigation