Decentralized Spectrum Learning for IoT Wireless Networks Collision Mitigation

TL;DR

This paper presents a reinforcement learning approach for IoT devices to mitigate radio collisions in unlicensed bands, enhancing network capacity and device autonomy without additional overhead.

Contribution

It introduces a novel decentralized learning method implemented on real IoT hardware, demonstrating collision reduction and battery life extension in practical scenarios.

Findings

Collision rates decreased in real radio signal tests.

IoT device battery life doubled in experiments.

No additional processing or network overhead introduced.

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.