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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2921
Title: A weighted moving average-based approach for cleaning sensor data
Authors: Lian, Jie
Chen, Lei
Zhuang, Yongzhen
Wang, X. Sean
Keywords: Weighted moving average algorithm
Sensor networks
Data cleaning
Issue Date: 2007
Citation: Proceedings 27th International Conference on Distributed Computing Systems (ICDCS 2007), 25-27 June 2007, Toronto, ON, Canada
Abstract: 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 databefore 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.
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URI: http://hdl.handle.net/1783.1/2921
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