Leveraging Reinforcement Learning for Adaptive Monitoring of Low-Power IoT Networks

Low-power Internet of Things (IoT) networks are widely deployed in various environments with resource-constrained devices, making their states monitoring particularly challenging. In this paper, we propose an adaptive monitoring mechanism for low-power IoT devices, by using a reinforcement learning (RL) method to automatically adapt the polling frequencies of the collected attributes. Our goal is to minimize the number of monitoring packets while keeping accurate and timely detection of threshold crossings associated with supervised attributes. We study the various RL parameter settings under different monitoring attribute behaviors using the OpenAi Gym simulator. We implement the RL based adaptive polling in Contiki OS, and we evaluate its performance using the Cooja simulator. Our results show that our approach converges to optimal polling frequencies and outperforms static periodic notification-based methods by reducing the number of monitoring packets, with a percentage of correctly detected threshold crossings exceeding 80%.