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

作　　者：Michal Prauzek Tereza Paterova Jaromir Konecny Radek Martinek 

机构地区：[1]VSB–Technical University of Ostrava,Department of Cybernetics and Biomedical Engineering,Ostrava,70800,Czech Republic

出　　处：《Computers, Materials & Continua》2022年第2期2601-2618,共18页计算机、材料和连续体（英文）

基　　金：This work was supported by the project SP2021/29,“Development of algorithms and systems for control,measurement and safety applications VII”of the Student Grant System,VSB-TU Ostrava.This work was also supported by the European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems project,Project Number CZ.02.1.01/0.0/0.0/16_019/0000867 under the Operational Programme for Research;Development and Education.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N◦856670.

摘　　要：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 smallmobile 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 lowerRMSE 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.

关 键 词：5G and beyond wireless IOT LPWAN M2M Q-LEARNING 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] TP391.44[自动化与计算机技术—控制科学与工程]