Data-Driven Self-Learning Controller Design Approach for Power-Aware IoT Devices based on Double Q-Learning Strategy
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10249506" target="_blank" >RIV/61989100:27240/21:10249506 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9659989" target="_blank" >https://ieeexplore.ieee.org/document/9659989</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SSCI50451.2021.9659989" target="_blank" >10.1109/SSCI50451.2021.9659989</a>
Alternative languages
Result language
angličtina
Original language name
Data-Driven Self-Learning Controller Design Approach for Power-Aware IoT Devices based on Double Q-Learning Strategy
Original language description
Operational cycle control is an attractive field of research which can lead to improvements in the services offered by power-aware monitoring embedded IoT devices. Machine learning (ML) is an infrastructure for operational cycle control and provides many approaches which provide more energy-efficient operation. One subfield of ML is Q-learning (QL), which forms the basis of the data-driven self-learning (DDSL) controller. The DDSL algorithm dynamically sets operational duty cycles according to estimates of future collected data values, leading to effective operation of power-aware systems. However, QL performs very poorly in stochastic environments as a result of overestimation of action values. The double estimator implemented in QL therefore applies Double QL (DQL) and forms the basis for a novel Double DDSL (DDDSL). The results of testing a DDDSL controller on historical data showed 42-50 % greater performance than a controller with a fixed duty-cycle, and 2-12 % more performance than a DDSL controller. (C) 2021 IEEE.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
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
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - proceedings
ISBN
978-1-72819-048-8
ISSN
—
e-ISSN
—
Number of pages
7
Pages from-to
—
Publisher name
IEEE
Place of publication
Piscataway
Event location
Orlando
Event date
Dec 5, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—