English(EN) RL-ASL: A Dynamic Listening Optimization for TSCH Networks Using Reinforcement Learning

强化学习优化TSCH网络，降低功耗和延迟

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-28 04:00

研究人员开发了RL-ASL，一个旨在优化时分信道跳频（TSCH）网络中监听时隙的强化学习框架。这种自适应方法根据实时网络条件动态决定激活或跳过监听时段，旨在降低工业物联网（IIoT）环境中的功耗。实验表明，RL-ASL在保持高可靠性和显著降低延迟的同时，可将功耗降低高达46%。 AI

影响优化IIoT网络的能源效率和延迟，可能延长设备寿命并实现更具响应性的通信。

排序理由这是一篇详细介绍用于网络优化的新型强化学习方法的学术论文。

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arXiv cs.LG TIER_1 English(EN) · F. Fernando Jurado-Lasso, J. F. Jurado · 2026-04-28 04:00

RL-ASL：一种使用强化学习的TSCH网络的动态监听优化方法

arXiv:2604.07533v2 Announce Type: replace-cross 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 Thin…