Robustness Analysis of Data-Driven Self-Learning Controllers for IoT Environmental Monitoring Nodes based on Q-learning Approaches

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10251745" target="_blank" >RIV/61989100:27240/22:10251745 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10022151" target="_blank" >https://ieeexplore.ieee.org/document/10022151</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SSCI51031.2022.10022151" target="_blank" >10.1109/SSCI51031.2022.10022151</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robustness Analysis of Data-Driven Self-Learning Controllers for IoT Environmental Monitoring Nodes based on Q-learning Approaches

Popis výsledku v původním jazyce

Today we are seeing a significant need for efficient control of operating cycles to deliver improvements in services provided by Internet of Things (IoT) devices embedded with environmental monitoring. To design an algorithm which provides sufficient duty-cycle control, we can apply machine learning approaches. The present study investigates the reinforcement learning (RL) algorithm family, especially Q-learning (QL) and Double Q-learning (DQL) algorithms and their suitability for devices deployed in a range of locations. We present a comprehensive analysis of the implemented RL approaches for regulating data-driven self-learning (DDSL) controllers. We tested QL and DQL algorithms on various datasets and evaluated their performance with a statistical analysis. The results indicated that the QL and the DQL approaches were highly dependent on the nature of the environmental parameters which the DDSL controller detected and recorded. (C) 2022 IEEE.

Název v anglickém jazyce

Robustness Analysis of Data-Driven Self-Learning Controllers for IoT Environmental Monitoring Nodes based on Q-learning Approaches

Popis výsledku anglicky

Today we are seeing a significant need for efficient control of operating cycles to deliver improvements in services provided by Internet of Things (IoT) devices embedded with environmental monitoring. To design an algorithm which provides sufficient duty-cycle control, we can apply machine learning approaches. The present study investigates the reinforcement learning (RL) algorithm family, especially Q-learning (QL) and Double Q-learning (DQL) algorithms and their suitability for devices deployed in a range of locations. We present a comprehensive analysis of the implemented RL approaches for regulating data-driven self-learning (DDSL) controllers. We tested QL and DQL algorithms on various datasets and evaluated their performance with a statistical analysis. The results indicated that the QL and the DQL approaches were highly dependent on the nature of the environmental parameters which the DDSL controller detected and recorded. (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

7

Strana od-do

721-727

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

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