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

Michal Prauzek,Jaromir Konecny,Tereza Paterova,Radek Martinek

doi:10.32604/cmc.2022.019705

Michal Prauzek, Jaromir Konecny + Show 2 more

Open Access

https://doi.org/10.32604/cmc.2022.019705

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Abstract

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.

Highlights

The article deals with the design and application of the control algorithm for a prototype of an efficient Low-Cost, Low-Power, Low Complexity—hereinafter (L-CPC) bidirectional communication system for the reading and configuration of embedded devices
The data-driven self-learning (DDSL) controller provides approximately 40% higher performance score (PS) than the best reference algorithm, with the exception of the degree of polynomial N = 5, which provides only 23% higher PS
The exception is the algorithm with the degree of polynomial 3, where the γ setting is lower than others

Summary

Introduction

The article deals with the design and application of the control algorithm for a prototype of an efficient Low-Cost, Low-Power, Low Complexity—hereinafter (L-CPC) bidirectional communication system for the reading and configuration of embedded devices. Monitoring mobile devices are usually deployed as remote data collection systems which can obtain parameters of interest from various application areas (Environmental monitoring, Smart Homes, Smart Cities, Smart Metering, etc.) [5,6,7,8,9,10,11,12,13]. This area relates to Internet of things (IoT) domain, that allows data transmission from mobile embedded devices to internet clouds [14,15].

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