RL-ASL: A Dynamic Listening Optimization for TSCH Networks Using Reinforcement Learning
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,...
| Authors | F. Fernando Jurado-Lasso, J. F. Jurado |
|---|---|
| Primary category | cs.NI |
| Published | 8 April 2026 |
| Last updated at source | 26 April 2026 |
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