A Machine-learning-enabled Context-driven Control Mechanism for Software-defined Smart Home Networks

Ru, Huang; Chu, Xiaoli; Zhang, Jie; Hu, Yu; Yan, Huaicheng

doi:10.18494/sam.2019.2298

Cited by 7 publications

(1 citation statement)

References 41 publications

(51 reference statements)

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“…Mobile monitoring devices have many research challenges aimed at ultra-low power consumption demands [19][20][21][22]. To address these constraints, there is a significant need for smart software control algorithms using machine learning principles for automated and intelligent device management [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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

Prauzek¹,

Paterova²,

Konecny³

et al. 2022

Computers, Materials &Amp; Continua

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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.

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