Solar Driven Self-learning Duty Cycle Control Algorithm for Maintenance-free Environmental Data Acquisition Nodes

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10250219" target="_blank" >RIV/61989100:27240/22:10250219 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2405896322003548" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2405896322003548</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ifacol.2022.06.039" target="_blank" >10.1016/j.ifacol.2022.06.039</a>

Result language
angličtina
Original language name
Solar Driven Self-learning Duty Cycle Control Algorithm for Maintenance-free Environmental Data Acquisition Nodes
Original language description
Environmental wireless sensor network (EWSN) nodes are ordinarily implemented as embedded devices which use low-power and low-cost microcontrollers whose duty cycle is controlled by a machine learning method. This article updates a previously designed Q-learning (QL) algorithm to reduce the number of failed operational duty cycles in solar-powered EWSN nodes. The innovation is based on modification of the QL learning process and was tested and verified on an EWSN node model using a 5-year dataset of solar irradiance values. By designing a new learning algorithm, the number of failures in operational cycles was significantly reduced, specifically by a factor of ten compared to the reference algorithm. Copyright (C) 2022 The Authors.
Czech name
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Czech description
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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

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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