The Industrial Internet of Things (IIoT) demands adaptable Networked Embedded Systems (NES) for optimal performance. Combined with recent advances in Artificial Intelligence (AI), tailored solutions can be developed to meet specific application requirements. This study introduces HRL-TSCH, an approach rooted in Hierarchical Reinforcement Learning (HRL), to devise Time Slotted Channel Hopping (TSCH) schedules provisioning IIoT demand. HRL-TSCH employs dual policies: one at a higher level for TSCH schedule link management, and another at a lower level for timeslot and channel assignments. The proposed RL agents address a multi-objective problem, optimizing throughput, power efficiency, and network delay based on predefined application requirements. Simulation experiments demonstrate HRL-TSCH superiority over existing state-of-art approaches, effectively achieving an optimal balance between throughput, power consumption, and delay, thereby enhancing IIoT network performance.

翻译：工业物联网要求自适应的网络嵌入式系统以实现最佳性能。结合人工智能的最新进展，可开发出满足具体应用需求的定制化解决方案。本研究提出HRL-TSCH——一种基于分层强化学习的方法，用于制定满足工业物联网需求的时间同步跳频（TSCH）调度方案。HRL-TSCH采用双重策略：高层策略负责TSCH调度链路管理，低层策略则负责时隙与信道分配。所提出的强化学习智能体处理多目标优化问题，根据预设应用需求优化吞吐量、能效和网络延迟。仿真实验表明，HRL-TSCH优于现有最先进方法，有效实现了吞吐量、能耗与延迟之间的最佳平衡，从而提升了工业物联网网络性能。