Decentralized Spectrum Learning for IoT Wireless Networks Collision Mitigation

Christophe Moy 1, 2 Lilian Besson 1, 3, 4, 2
4 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : 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.
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https://hal.inria.fr/hal-02144465
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Submitted on : Thursday, May 30, 2019 - 2:13:08 PM
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  • HAL Id : hal-02144465, version 1
  • ARXIV : 1906.00614

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Christophe Moy, Lilian Besson. Decentralized Spectrum Learning for IoT Wireless Networks Collision Mitigation. ISIoT 2019 - 1st International Workshop on Intelligent Systems for the Internet of Things, May 2019, Santorin, Greece. ⟨hal-02144465⟩

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