A New Data Filtering Scheme Based on Statistical Data Analysis for Monitoring Systems in Wireless Sensor Networks

Recently, wireless sensor networks (WSN) are actively used for various monitoring systems. While 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 new data filtering scheme based on statistical data analysis. First, the proposed scheme periodically aggregates nodes´ survival massages to support node failure detection. Secondly, to reduce energy consumption, the proposed scheme sends the sample data including node survival massage and perform data filtering based on the messages. Finally, it analyzes the sample 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 the proposed scheme outperforms the Kalman filtering scheme in terms of the number of messages transmission.