Q-learning Energy Management Strategy for TEG-powered Environmental Monitoring IoT Devices: A Pilot Study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10251746" target="_blank" >RIV/61989100:27240/22:10251746 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/10022025" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10022025</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SSCI51031.2022.10022025" target="_blank" >10.1109/SSCI51031.2022.10022025</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Q-learning Energy Management Strategy for TEG-powered Environmental Monitoring IoT Devices: A Pilot Study

Popis výsledku v původním jazyce

In this pilot study, we describe self-learning energy management principles for energy harvesting environmental monitoring nodes using Internet of Things (IoT) communications technology. The solution is powered with ambient energy harvested by a thermoelectric generator (TEG) and stored in an internal supercapacitor. We present a hardware-based model derived from a DC/DC converter, microcontroller and LoRaWAN IoT interface, which is detailed in the paper. The simulation applied historical temperature data obtained at several soil depths. The study&apos;s contribution is a reinforcement learning (Q-learning) method to achieve an optimal energy management strategy to maximize data collection and minimize failure. The results demonstrate that the designed approach was capable of operating more effectively (up to approx. 96 % ratio between complete and missed cycles) than reference solutions with a fixed duty-cycle configuration. We support our conclusions with results from 10 candidate Q-learning controllers which apply various learning and discount factor configurations and demonstrate superior complete/missed cycles ratios than the reference solutions. (C) 2022 IEEE.

Název v anglickém jazyce

Q-learning Energy Management Strategy for TEG-powered Environmental Monitoring IoT Devices: A Pilot Study

Popis výsledku anglicky

In this pilot study, we describe self-learning energy management principles for energy harvesting environmental monitoring nodes using Internet of Things (IoT) communications technology. The solution is powered with ambient energy harvested by a thermoelectric generator (TEG) and stored in an internal supercapacitor. We present a hardware-based model derived from a DC/DC converter, microcontroller and LoRaWAN IoT interface, which is detailed in the paper. The simulation applied historical temperature data obtained at several soil depths. The study&apos;s contribution is a reinforcement learning (Q-learning) method to achieve an optimal energy management strategy to maximize data collection and minimize failure. The results demonstrate that the designed approach was capable of operating more effectively (up to approx. 96 % ratio between complete and missed cycles) than reference solutions with a fixed duty-cycle configuration. We support our conclusions with results from 10 candidate Q-learning controllers which apply various learning and discount factor configurations and demonstrate superior complete/missed cycles ratios than the reference solutions. (C) 2022 IEEE.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Centrum výzkumu pokročilých mechatronických systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 : proceedings : 4-7 december 2022, Singapore

ISBN

978-1-66548-769-6

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

211-216

Název nakladatele

IEEE - Institute of Electrical and Electronics Engineers

Místo vydání

Piscataway

Místo konání akce

Singapur

Datum konání akce

4. 12. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—