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A weighted moving average-based approach for cleaning sensor data

Authors Zhuang, Y. HKUST affiliated (currently or previously)
Chen, L. View this author's profile
Wang, X.S.
Lian, J.
Issue Date 2007
Source Proceedings - International Conference on Distributed Computing Systems , 2007
Summary Nowadays, wireless sensor networks have been widely used in many monitoring applications. Due to the low quality of sensors and random effects of the environments, however, it is well known that the collected sensor data are noisy. Therefore, it is very critical to clean the sensor data before using them to answer queries or conduct data analysis. Popular data cleaning approaches, such as the moving average, cannot meet the requirements of both energy efficiency and quick response time in many sensor related applications. In this paper, we propose a hybrid sensor data cleaning approach with confidence. Specifically, we propose a smart weighted moving average (WMA) algorithm that collects confidence data from sensors and computes the weighted moving average. The rationale behind the WMA algorithm is to draw more samples for a particular value that is of great importance to the moving average, and provide higher confidence weight for this value, such that this important value can be quickly reflected in the moving average. Based on our extensive simulation results, we demonstrate that, compared to the simple moving average (SMA), our WMA approach can effectively clean data and offer quick response time. © 2007 IEEE.
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Language English
Format Conference paper
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