Optimizing of Q-learning day/night energy strategy for solar harvesting environmental wireless sensor networks nodes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10248101" target="_blank" >RIV/61989100:27240/21:10248101 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.eejournal.ktu.lt/index.php/elt/article/view/28875" target="_blank" >https://www.eejournal.ktu.lt/index.php/elt/article/view/28875</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5755/j02.eie.28875" target="_blank" >10.5755/j02.eie.28875</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimizing of Q-learning day/night energy strategy for solar harvesting environmental wireless sensor networks nodes

Popis výsledku v původním jazyce

This research article presents the application of the Q-learning algorithm in the operational duty cycle control of solar-powered environmental wireless sensor network (EWSN) nodes. Those nodes are commonly implemented as embedded devices using low-power and low-cost microcontrollers. Therefore, there is a significant need for an effective and easy way to implement a machine learning (ML) algorithm in terms of computer performance. This approach uses a Q-learning-based policy implementing a sleep/run switching algorithm driven by the state of charge. The presented algorithm is based on two modes: daylight and nighttime, which is a suitable solution for solar-powered systems. The study includes the complete process of design EWSN node strategy with an optimal reward policy. The presented algorithm was tested and verified on an EWSN node model and a 5-year data set of solar irradiance values was used for the learning process and its validation. As part of the study, we are also presenting the validation in terms of Q-learning parameters, which include the learning rate and discount factor. The result section shows that the overall performance of the presented solution is more suitable for solar-powered EWSN then state-of-the-art studies. Both day/night experiments reached 828 203 measurement/transmission cycles, which is 12.7 % more than in the previous studies using the strategy defined by the state of energy storage. (C) 2021 Kauno Technologijos Universitetas. All rights reserved.

Název v anglickém jazyce

Optimizing of Q-learning day/night energy strategy for solar harvesting environmental wireless sensor networks nodes

Popis výsledku anglicky

This research article presents the application of the Q-learning algorithm in the operational duty cycle control of solar-powered environmental wireless sensor network (EWSN) nodes. Those nodes are commonly implemented as embedded devices using low-power and low-cost microcontrollers. Therefore, there is a significant need for an effective and easy way to implement a machine learning (ML) algorithm in terms of computer performance. This approach uses a Q-learning-based policy implementing a sleep/run switching algorithm driven by the state of charge. The presented algorithm is based on two modes: daylight and nighttime, which is a suitable solution for solar-powered systems. The study includes the complete process of design EWSN node strategy with an optimal reward policy. The presented algorithm was tested and verified on an EWSN node model and a 5-year data set of solar irradiance values was used for the learning process and its validation. As part of the study, we are also presenting the validation in terms of Q-learning parameters, which include the learning rate and discount factor. The result section shows that the overall performance of the presented solution is more suitable for solar-powered EWSN then state-of-the-art studies. Both day/night experiments reached 828 203 measurement/transmission cycles, which is 12.7 % more than in the previous studies using the strategy defined by the state of energy storage. (C) 2021 Kauno Technologijos Universitetas. All rights reserved.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control 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

2021

Kód důvěrnosti údajů

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

Název periodika

Elektronika ir elektrotechnika

ISSN

1392-1215

e-ISSN

—

Svazek periodika

27

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

LT - Litevská republika

Počet stran výsledku

7

Strana od-do

50-56

Kód UT WoS článku

000668351900005

EID výsledku v databázi Scopus

2-s2.0-85108971347