Automatic ARIMA Time Series Modeling for Data Aggregation in Wireless Sensor Networks

One of the most natural and primary ways of data collection in wireless sensor networks is to periodically report sensed data values from sensor node to aggregator. However, this kind of data collection mechanism comes at the cost of power consumption and packet collision. In this paper, we developed an automatic ARIMA (Auto Regressive Integrated Moving Average) modeling based data aggregation scheme. The main idea behind this scheme is to decrease the number of transmitted data values between sensor nodes and aggregator by using time series prediction model. The proposed scheme can effectively save the precious battery energy of wireless sensor node while keeping the predicted data values of aggregator within application defined error threshold. We show through experiments with real data that the predicted values of our proposed scheme fit the real sensed values very well and fewer messages are transmitted between sensor node and aggregator.

[1]  François-Xavier Standaert,et al.  On the Energy Cost of Communication and Cryptography in Wireless Sensor Networks , 2008, 2008 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications.

[2]  Deborah Estrin,et al.  Impact of network density on data aggregation in wireless sensor networks , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[3]  Gang Liu,et al.  Energy-efficient cooperative data aggregation for wireless sensor networks , 2010, J. Parallel Distributed Comput..

[4]  Jingsha He,et al.  Group-based intrusion detection system in wireless sensor networks , 2008, Comput. Commun..

[5]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[6]  Sunshin An,et al.  Prediction Based Mobile Data Aggregation in Wireless Sensor Network , 2009, GPC.

[7]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[8]  Xiuzhen Cheng,et al.  Aggregation tree construction in sensor networks , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[9]  Pramod K. Varshney,et al.  Data-aggregation techniques in sensor networks: a survey , 2006, IEEE Communications Surveys & Tutorials.

[10]  Hao Chen.  The Construction of Intelligent Home Based on Wireless Sensor Networks , 2012 .

[11]  Jörg Widmer,et al.  In-network aggregation techniques for wireless sensor networks: a survey , 2007, IEEE Wireless Communications.

[12]  Ganesh K. Venayagamoorthy,et al.  Computational Intelligence in Wireless Sensor Networks: A Survey , 2011, IEEE Communications Surveys & Tutorials.

[13]  Sangjoon Park,et al.  Experimental Results of Wireless Sensor Networks Applied for Surveillance Systems , 2012 .

[14]  Guohong Cao,et al.  DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks , 2004, IEEE Transactions on Wireless Communications.

[15]  Cauligi S. Raghavendra,et al.  PEGASIS: Power-efficient gathering in sensor information systems , 2002, Proceedings, IEEE Aerospace Conference.

[16]  Deepak Ganesan,et al.  PRESTO: feedback-driven data management in sensor networks , 2009, TNET.