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An Experimental Performance Evaluation with Xenomai for WSN

WSN을 위한 Xenomai의 실험적 성능평가

  • 손태영 (호서대학교 컴퓨터공학부) ;
  • 임성락 (호서대학교 컴퓨터공학부)
  • Received : 2016.09.22
  • Accepted : 2017.01.06
  • Published : 2017.01.31

Abstract

Structures like bridges or buildings need to be checked continuously to diagnose their safety. However, it is extremely difficult for the people who access such structures to check all areas directly. To overcome this problem, there is a lot of active research into structural health monitoring (SHM) with wireless sensor nodes (WSNs). In this paper, for more accurate checking of SHM with WSNs, we experimentally compare and evaluate the performance of Xenomai, which provides real-time processing under the traditional Linux kernel. For this purpose, we patch Xenomai into the traditional Linux kernel of a commercial embedded board, Raspberry Pi, and implement a task that periodically reads vibration data of the z-axis from an accelerometer in order to analyze the natural frequency of cantilever beams. Reading the data from the traditional Linux kernel with the same method, we analyze the natural frequency of the cantilever beams using Smart Office Analyzer. Finally, to review the validity of Xenomai for WSNs, we obtain vibration data on the z-axis from the accelerometer via wired network and compared and analyzed them the same way.

다리 혹은 건물과 같은 구조물들은 그들의 안전상태를 진단하기 위하여 지속적으로 점검할 필요가 있다. 그러나 사람이 이러한 구조물의 모든 지점을 직접적으로 접근하여 점검해야 하는 치명적인 어려움이 있다. 이러한 어려움을 극복하기 위하여 오늘날에는 WSN(Wireless Sensor Node)를 이용한 SHM(Structural Health Monitoring)에 대한 많은 연구가 활발히 이루어지고 있다. 본 논문에서는 WSN을 이용한 SHM에서 보다 정밀한 점검을 위하여 실시간 처리를 제공하는 Xenomai의 성능을 기존 리눅스 커널과 실험적으로 비교 평가하였다. 이를 위하여 상용 임베디드 보드인 라즈베리 파이(Raspberry Pi) 보드의 기존 리눅스 커널에 Xenomai를 패치 시키고, 캔틸레버 빔(cantilever beam)의 고유 주파수(natural frequency)를 분석하기 위하여 가속도 센서로부터 z-축 진동 데이터를 주기적으로 읽어 들이는 태스크를 구현하였다. 동일한 방법으로 기존 리눅스 커널에서 데이터를 측정한 후, Smart Office Analyzer를 이용하여 캔틸레버 빔의 고유 주파수를 분석하였다. 마지막으로, WSN을 위한 Xenomai의 타당성을 검토하기 위하여 가속도 센서의 z-축 진동 데이터를 유선으로 측정하여 동일한 방법으로 비교 분석하였다.

Keywords

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