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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Q-learning Energy Management Strategy for TEG-powered Environmental Monitoring IoT Devices: A Pilot Study
Original language description
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'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.
Czech name
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Czech description
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Project
<a href="/en/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Research Centre of Advanced Mechatronic Systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 : proceedings : 4-7 december 2022, Singapore
ISBN
978-1-66548-769-6
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
211-216
Publisher name
IEEE - Institute of Electrical and Electronics Engineers
Place of publication
Piscataway
Event location
Singapur
Event date
Dec 4, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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