TY - GEN
T1 - A new data filtering scheme based on statistical data analysis for monitoring systems in wireless sensor networks
AU - Hong, Seung Tae
AU - Chang, Jae Woo
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Data Filtering
KW - Monitoring System
KW - Wireless Sensor Network
UR - https://www.scopus.com/pages/publications/81555198138
U2 - 10.1109/HPCC.2011.90
DO - 10.1109/HPCC.2011.90
M3 - Conference paper
AN - SCOPUS:81555198138
SN - 9780769545387
T3 - Proc.- 2011 IEEE International Conference on HPCC 2011 - 2011 IEEE International Workshop on FTDCS 2011 -Workshops of the 2011 Int. Conf. on UIC 2011- Workshops of the 2011 Int. Conf. ATC 2011
SP - 635
EP - 640
BT - Proc.- 2011 IEEE International Conference on HPCC 2011 - 2011 IEEE International Workshop on FTDCS 2011 - Workshops of the 2011 Int. Conf. on UIC 2011- Workshops of the 2011 Int. Conf. ATC 2011
T2 - 13th IEEE International Workshop on FTDCS 2011, the 8th International Conference on ATC 2011, the 8th International Conference on UIC 2011 and the 13th IEEE International Conference on HPCC 2011
Y2 - 2 September 2011 through 4 September 2011
ER -