Data-Driven Self-Learning Controller Design Approach for Power-Aware IoT Devices based on Double Q-Learning Strategy

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Data-Driven Self-Learning Controller Design Approach for Power-Aware IoT Devices based on Double Q-Learning Strategy

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Data-Driven Self-Learning Controller Design Approach for Power-Aware IoT Devices based on Double Q-Learning Strategy

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - proceedings

ISBN

978-1-72819-048-8

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Orlando

Datum konání akce

5. 12. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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