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http://dx.doi.org/10.14372/IEMEK.2022.17.2.93

Machine learning-based Multi-modal Sensing IoT Platform Resource Management  

Lee, Seongchan (Soonchunhyang University)
Sung, Nakmyoung (KETI)
Lee, Seokjun (KETI)
Jun, Jaeseok (Soonchunhyang University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.17, no.2, 2022 , pp. 93-100 More about this Journal
Abstract
In this paper, we propose a machine learning-based method for supporting resource management of IoT software platforms in a multi-modal sensing scenario. We assume that an IoT device installed with a oneM2M-compatible software platform is connected with various sensors such as PIR, sound, dust, ambient light, ultrasonic, accelerometer, through different embedded system interfaces such as general purpose input output (GPIO), I2C, SPI, USB. Based on a collected dataset including CPU usage and user-defined priority, a machine learning model is trained to estimate the level of nice value required to adjust according to the resource usage patterns. The proposed method is validated by comparing with a rule-based control strategy, showing its practical capability in a multi-modal sensing scenario of IoT devices.
Keywords
Machine learning; Multi-modal sensing; IoT platform; Resource management; oneM2M standard;
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Times Cited By KSCI : 3  (Citation Analysis)
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