Massive Multi-Player Multi-Armed Bandits for IoT Networks: An Application on LoRa Networks

More and more manufacturers, as part of the transition toward Industry 4.0, are using Internet of Things (IoT) networks for more efficient production. The wide and extensive expansion of IoT devices and the variety of applications generate different challenges, mainly in terms of reliability and energy efficiency. In this paper, we propose an approach to optimize the performance of IoT networks by making the IoT devices intelligent using machine learning techniques. We formulate the optimization problem as a massive multi-player multi-armed bandit and introduce two novel policies: Decreasing-Order-Reward-Greedy (DORG) focuses on the number of successful transmissions, while Decreasing-Order-Fair-Greedy (DOFG) also guarantees some measure of fairness between the devices. We then present an efficient way to manage the trade-off between energy consumption and packet losses in Long-Range (LoRa) networks using our algorithms, by which LoRa nodes adjust their emission parameters (Spreading Factor and transmitting power). We implement our algorithms on a LoRa network simulator and show that such learning techniques largely outperform the Adaptive Data Rate (ADR) algorithm currently implemented in LoRa devices, in terms of both energy consumption and packet losses.