RL-ASL: A Dynamic Listening Optimization for TSCH Networks Using Reinforcement Learning

TL;DR

RL-ASL introduces a reinforcement learning-based adaptive listening framework for TSCH networks, significantly reducing power consumption and latency while maintaining reliability in dynamic IIoT environments.

Contribution

It presents a novel RL-driven adaptive listening approach that dynamically optimizes TSCH scheduling, outperforming static schedulers in energy efficiency and latency.

Findings

Achieves up to 46% lower power consumption compared to baseline protocols.

Reduces average latency by up to 96% relative to PRIL-M.

Maintains near-perfect reliability with dynamic slot skipping.

Abstract

Time Slotted Channel Hopping (TSCH) is a widely adopted Media Access Control (MAC) protocol within the IEEE 802.15.4e standard, designed to provide reliable and energy-efficient communication in Industrial Internet of Things (IIoT) networks. However, state-of-the-art TSCH schedulers rely on static slot allocations, resulting in idle listening and unnecessary power consumption under dynamic traffic conditions. This paper introduces RL-ASL, a reinforcement learning-driven adaptive listening framework that dynamically decides whether to activate or skip a scheduled listening slot based on real-time network conditions. By integrating learning-based slot skipping with standard TSCH scheduling, RL-ASL reduces idle listening while preserving synchronization and delivery reliability. Experimental results on the FIT IoT-LAB testbed and Cooja network simulator show that RL-ASL achieves up to 46% lower power consumption than baseline scheduling protocols, while maintaining near-perfect reliability and reducing average latency by up to 96% compared to PRIL-M. Its link-based variant, RL-ASL-LB, further improves delay performance under high contention with similar energy efficiency. Importantly, RL-ASL performs inference on constrained motes with negligible overhead, as model training is fully performed offline. Overall, RL-ASL provides a practical, scalable, and energy-aware scheduling mechanism for next-generation low-power IIoT networks.