센서 모니터링을 위한 칼만필터 기반의 효율적인 적응적 샘플링 기법
An Efficient Adaptive Sampling Technique based on the Kalman Filter for Sensor Monitoring
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초록
센서 네트워크 환경에서는 각 센서는 정의된 샘플링 주기에 따라서 외부 환경을 측정하고 측정된 값을 기지국으로 전송한다. 따라서, 샘플링 주기는 대역폭, 전력량 등 센서들의 중요 자원의 소비에 지대한 영향을 끼친다. 본 논문에서는 측정값 특성에 따라서 센서의 샘플링 주기를 조절하는 새로운 적응적 샘플링 기법을 제안한다. 제안하는 기법은 KF (Kalman-Filter)에 기반하여 미래의 측정값을 예측한다. 그리고, 실측값과 예측값의 차이에 따라서 센서 측정값들의 중요도를 파악하고 이에 따라서 샘플링 주기를 변화시킨다. 실험에서 제안하는 기법의 효과성을 보였다.
Abstract
In sensor network environments, each sensor measures the physical environments according to the sampling period, and transmits a sensor reading to the base station. Thus, the sample period influences against importance resources such as a network bandwidth, and a battery power. In this paper, we propose new adaptive sampling technique that adjusts the sampling period of a sensor with respect to the features of sensor readings. The proposed technique predicts a future readings based on KF (Kalman Fiter). By using the differences of actual readings and estimated reading, we identify the importance of sensor readings, and then, we adjust the sampling period according to the importance. In our experiments, we demonstrate the effectiveness of our technique.
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