Simulation of a Daytime-Based Q-Learning Control Strategy for Environmental Harvesting WSN Nodes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246780" target="_blank" >RIV/61989100:27240/20:10246780 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-50097-9_44" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-50097-9_44</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-50097-9_44" target="_blank" >10.1007/978-3-030-50097-9_44</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Simulation of a Daytime-Based Q-Learning Control Strategy for Environmental Harvesting WSN Nodes

Popis výsledku v původním jazyce

Environmental wireless sensor networks (EWSN) are designed to collect environmental data in remote locations where maintenance options are limited. It is therefore essential for the system to make a good use of the available energy so as to operate efficiently. This paper describes a simulation framework of an EWSN node, which allows to simulate various configurations and parameters before implementing the control system in a physical hardware model, which was developed in our previous study. System operation, namely environmental data acquisition and subsequent data transmission to a network, is governed by a model-free Q-learning algorithm, which do not have any prior knowledge of its environment. Real-life historical meteorological data acquired in the years 2008-2012 in Canada was used to test the capabilities of the control algorithm. The results show that setting of the learning rate is crucial to EWSN node&apos;s performance. When set improperly, the system tends to fail to operate by depleting its energy storage. One of the aspects to consider when improving the algorithm is to reduce the amount of wasted harvested energy. This could be done through tuning of the Q-learning reward signal.

Název v anglickém jazyce

Simulation of a Daytime-Based Q-Learning Control Strategy for Environmental Harvesting WSN Nodes

Popis výsledku anglicky

Environmental wireless sensor networks (EWSN) are designed to collect environmental data in remote locations where maintenance options are limited. It is therefore essential for the system to make a good use of the available energy so as to operate efficiently. This paper describes a simulation framework of an EWSN node, which allows to simulate various configurations and parameters before implementing the control system in a physical hardware model, which was developed in our previous study. System operation, namely environmental data acquisition and subsequent data transmission to a network, is governed by a model-free Q-learning algorithm, which do not have any prior knowledge of its environment. Real-life historical meteorological data acquired in the years 2008-2012 in Canada was used to test the capabilities of the control algorithm. The results show that setting of the learning rate is crucial to EWSN node&apos;s performance. When set improperly, the system tends to fail to operate by depleting its energy storage. One of the aspects to consider when improving the algorithm is to reduce the amount of wasted harvested energy. This could be done through tuning of the Q-learning reward signal.

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

Advances in Intelligent Systems and Computing. Volume 1156

ISBN

978-3-030-50096-2

ISSN

2194-5357

e-ISSN

2194-5365

Počet stran výsledku

10

Strana od-do

432-441

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Ostrava

Datum konání akce

2. 12. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000590145400044