Self-learning for Day-night Mode Energy Strategy for Solar Powered Environmental WSN Nodes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246783" target="_blank" >RIV/61989100:27240/20:10246783 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9255790" target="_blank" >https://ieeexplore.ieee.org/document/9255790</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CCECE47787.2020.9255790" target="_blank" >10.1109/CCECE47787.2020.9255790</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Self-learning for Day-night Mode Energy Strategy for Solar Powered Environmental WSN Nodes

Popis výsledku v původním jazyce

Environmental data is in many cases acquired in remote locations that are difficult to access for sensor maintenance. Therefore, efficient use of available energy is crucial, particularly in systems that use energy harvesting devices, such as solar panels. This study presents a hybrid energy management strategy implemented in an environmental wireless sensor network (EWSN) controller. The control unit employs a model-free Q-learning algorithm during the day and linear energy discharging at night. A three-component Q-learning reward signal along with 7 actions and 11 energy states are designed for the system to achieve optimal performance in terms of data sensing and transmission operation and to minimize the amount of failures due energy storage depletion. (C) 2020 IEEE.

Název v anglickém jazyce

Self-learning for Day-night Mode Energy Strategy for Solar Powered Environmental WSN Nodes

Popis výsledku anglicky

Environmental data is in many cases acquired in remote locations that are difficult to access for sensor maintenance. Therefore, efficient use of available energy is crucial, particularly in systems that use energy harvesting devices, such as solar panels. This study presents a hybrid energy management strategy implemented in an environmental wireless sensor network (EWSN) controller. The control unit employs a model-free Q-learning algorithm during the day and linear energy discharging at night. A three-component Q-learning reward signal along with 7 actions and 11 energy states are designed for the system to achieve optimal performance in terms of data sensing and transmission operation and to minimize the amount of failures due energy storage depletion. (C) 2020 IEEE.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

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

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Canadian Conference on Electrical and Computer Engineering 2020

ISBN

978-1-72815-443-5

ISSN

0840-7789

e-ISSN

2576-7046

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

London

Datum konání akce

30. 8. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—