IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
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Machine learning-based Multi-modal Sensing IoT Platform Resource Management

머신러닝 기반 멀티모달 센싱 IoT 플랫폼 리소스 관리 지원

  • Received : 2022.01.31
  • Accepted : 2022.03.17
  • Published : 2022.04.30
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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.

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Acknowledgement

본 논문은 2021년도 정부 (과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임 (No.2018-0-01456, (총괄+세부) 지능 기반 초소형 disposable IoT 동적 자율 구성 및 실행 인프라 기술). 본 논문은 2020년도 정부 (교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임 (No.2020R1I1A3A04037409).

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