Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10248100" target="_blank" >RIV/61989100:27240/22:10248100 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.techscience.com/cmc/v70n2/44649" target="_blank" >https://www.techscience.com/cmc/v70n2/44649</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/cmc.2022.019705" target="_blank" >10.32604/cmc.2022.019705</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

Popis výsledku v původním jazyce

Nowadays, there is a significant need for maintenance free modern Internet of things (IoT) devices which can monitor an environment. IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network (LPWAN). LPWAN is a promising communications technology which allows machine to machine (M2M) communication and is suitable for small mobile embedded devices. The paper presents a novel data-driven self-learning (DDSL) controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices. The DDSL algorithm is based on a modified Q-learning algorithm which allows energy efficient data-driven behavior of mobile embedded IoT devices. The aim of the DDSL algorithm is to dynamically set operation duty cycles according to the estimation of future collected data values, leading to effective operation of power-aware systems. The presented novel solution was tested on a historical data set and compared with a fixed duty cycle reference algorithm. The root mean square error (RMSE) and measurements parameters considered for the DDSL algorithm were compared to a reference algorithm and two independent criteria (the performance score parameter and normalized geometric distance) were used for overall evaluation and comparison. The experiments showed that the novel DDSL method reaches significantly lower RMSE while the number of transmitted data count is less than or equal to the fixed duty cycle algorithm. The overall criteria performance score is 40% higher than the reference algorithm base on static confirmation settings.

Název v anglickém jazyce

Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

Popis výsledku anglicky

Nowadays, there is a significant need for maintenance free modern Internet of things (IoT) devices which can monitor an environment. IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network (LPWAN). LPWAN is a promising communications technology which allows machine to machine (M2M) communication and is suitable for small mobile embedded devices. The paper presents a novel data-driven self-learning (DDSL) controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices. The DDSL algorithm is based on a modified Q-learning algorithm which allows energy efficient data-driven behavior of mobile embedded IoT devices. The aim of the DDSL algorithm is to dynamically set operation duty cycles according to the estimation of future collected data values, leading to effective operation of power-aware systems. The presented novel solution was tested on a historical data set and compared with a fixed duty cycle reference algorithm. The root mean square error (RMSE) and measurements parameters considered for the DDSL algorithm were compared to a reference algorithm and two independent criteria (the performance score parameter and normalized geometric distance) were used for overall evaluation and comparison. The experiments showed that the novel DDSL method reaches significantly lower RMSE while the number of transmitted data count is less than or equal to the fixed duty cycle algorithm. The overall criteria performance score is 40% higher than the reference algorithm base on static confirmation settings.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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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

2022

Kód důvěrnosti údajů

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

Název periodika

CMC-Computers Materials &amp; Continua

ISSN

1546-2218

e-ISSN

1546-2226

Svazek periodika

70

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

18

Strana od-do

2601-2618

Kód UT WoS článku

000705060700028

EID výsledku v databázi Scopus

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