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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Robustness Analysis of Data-Driven Self-Learning Controllers for IoT Environmental Monitoring Nodes based on Q-learning Approaches
Original language description
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.
Czech name
—
Czech description
—

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
—
e-ISSN
—
Number of pages
7
Pages from-to
721-727
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
—

Similar results(10)