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F. Fernando Jurado-Lasso
Engineer specializing in IoT, Networked Embedded Systems, and Machine Learning for communications and networking
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IEEE Transactions on Cognitive Communications and Networking
- June 2024
HRL-TSCH: A Hierarchical Reinforcement Learning-based TSCH Scheduler for IIoT
Abstract
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’s superiority over existing state-of-art approaches, effectively achieving an optimal balance …
BibTeX citation
@article{JuradoFafoutis2024,
author = {Jurado Lasso, Fabian Fernando and Orfanidis, Charalampos and
Jurado, Jesus Fabian and Fafoutis, Xenofon},
title = {HRL-TSCH: {A} {Hierarchical} {Reinforcement} {Learning-based}
{TSCH} {Scheduler} for {IIoT}},
journal = {IEEE Transactions on Cognitive Communications and
Networking},
date = {2024-06},
doi = {10.1109/TCCN.2024.3408459}
}