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.

翻译：时隙跳频（TSCH）是IEEE 802.15.4e标准中广泛采用的媒体接入控制（MAC）协议，旨在为工业物联网（IIoT）网络提供可靠且能效高的通信。然而，现有TSCH调度器依赖静态时隙分配，在动态流量条件下会导致空闲侦听和不必要的功耗。本文提出RL-ASL——一种基于强化学习的自适应侦听框架，能根据实时网络条件动态决策激活或跳过预定的侦听时隙。通过将基于学习的时隙跳过机制与标准TSCH调度相结合，RL-ASL在保持同步和交付可靠性的同时减少了空闲侦听。在FIT IoT-LAB测试平台和Cooja网络模拟器上的实验结果表明，与基线调度协议相比，RL-ASL功耗降低高达46%，同时保持近乎完美的可靠性，且平均延迟相比PRIL-M降低高达96%。其基于链路的变体RL-ASL-LB在高竞争条件下以相近能效进一步改善延迟性能。重要的是，由于模型训练完全离线完成，RL-ASL在资源受限节点上的推理开销可忽略不计。总体而言，RL-ASL为下一代低功耗IIoT网络提供了一种实用、可扩展且具备能耗感知的调度机制。