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（IIoT）要求具备自适应性的网络嵌入式系统（NES）以实现最优性能。结合人工智能（AI）的最新进展，可开发定制化解决方案以满足特定应用需求。本研究提出HRL-TSCH方法，该方法是基于分层强化学习（HRL）来制定满足IIoT需求的时隙信道跳频（TSCH）调度方案。HRL-TSCH采用双重策略：高层策略负责TSCH调度的链路管理，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需解决多目标优化问题，即依据预定义应用需求平衡吞吐量、能效与网络延迟。仿真实验表明，HRL-TSCH优于现有先进方法，能有效实现吞吐量、功耗与延迟之间的最优权衡，从而提升IIoT网络性能。