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Wireless sensor networks (WSN) are widely used in the various monitoring systems. When implementing WSN-based monitoring systems, there are three important issues to be considered. At first, we should consider a node failure detection method to provide continuous monitoring. Secondly, because sensor nodes use limited battery power, we need an efficient data filtering method to reduce energy consumption. At last, we should consider a data filtering method for reducing processing overhead. The existing Kalman filtering scheme has good performance on data filtering, but it causes too much processing overhead for estimating sensed data. To solve this problem, we, in this paper, propose a sampling-based data filtering scheme based on statistical data analysis. First, our scheme periodically aggregates nodes' survival massages to support node failure detection. Secondly, to reduce energy consumption, our scheme sends the sampled data including node survival massage and perform data filtering based on the messages. Finally, it analyzes the sampled data to estimate filtering range at a server. As a result, each sensor node can use only a simple compare operation for filtering data. Through performance analysis, we show that our scheme outperforms the Kalman filtering scheme in terms of the number of data transmissions.