Efficient Data Collection and Event Boundary Detection in Wireless Sensor Networks Using Tiny Models
Using wireless geosensor networks (WGSN), sensor nodes often monitor a phenomenon that is both continuous in time and space. However, sensor nodes take discrete samples, and an analytical framework inside or outside the WSN is used to analyze the phenomenon. In both cases, expensive communication is used to stream a large number of data samples to other nodes and to the base station. In this work, we explore a novel alternative that utilizes predictive process knowledge of the observed phenomena to minimize upstream communication. Often, observed phenomena adhere to a process with predictable behavior over time. We present a strategy for developing and running so-called ’tiny models’ on individual sensor nodes that capture the predictable behavior of the phenomenon; nodes now only communicate when unexpected events are observed. Using multiple simulations, we demonstrate that a significant percentage of messages can be reduced during data collection.
KeywordsSensors wireless sensor network model continuous phenomenon tiny models process modeling prediction autonomous
Unable to display preview. Download preview PDF.
- 1.Nittel, S.: A survey of geosensor networks: advances in dynamic environmental monitoring. Accepted for publication: Sensors Journal (2009)Google Scholar
- 2.CrossbowTech: Imote2, http://www.xbow.com/Products/productdetails.aspx?sid=253 (Visited 10. 04. 2009)
- 3.CrossbowTech: Micaz, http://www.xbow.com/Products/productdetails.aspx?sid=164 (Visited 10.04.2009)
- 6.Levis, P., Madden, S., Polastre, J., Szewczyk, R., Whitehouse, K., Woo, A., Gay, D., Hill, J., Welsh, M., Brewer, E., Culler, D.: TinyOS An operating system for sensor networks: Ambient Intelligence, vol. 2, pp. 115–148. Springer, Heidelberg (2005)Google Scholar
- 7.Duckham, M., Nittel, S., Worboys, M.: Monitoring dynamic spatial fields using responsive geosensor networks. In: ACM-GIS 2005, Bremen, Germany (2005)Google Scholar
- 8.Jin, G., Nittel, S.: Supporting spatio-temporal queries in wireless sensor networks by tracking deformable 2D objects. In: ACM-GIS 2008, Los Angles, CA (2008)Google Scholar
- 9.Stefanidis, A., Nittel, S.: GeoSensor Networks, p. 296. CRC Press, Boca Raton (2005)Google Scholar
- 10.Huang, H., Hartman, J., Hurst, T.: Data-centric routing in sensor networks using biased walk. In: 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks(SECON 2006), vol. 1, pp. 1–9 (2006)Google Scholar
- 11.Ditzel, M., Langendoen, M.: D3 Data-centric data dissemination in wireless sensor networks. In: European Conference on Wireless Technology, Paris, France, October 2005 (2005)Google Scholar
- 12.Nagayama, T., Spencer, B., Agha, G., Mechitov, K.: Model-based data aggregation for structural monitoring employing smart sensors. In: Proceedings of the Third International Conference on Networked Sensing Systems (INSS 2006), May 31- June 2, pp. 203–210 (2006)Google Scholar
- 13.Deshpande, A., Guestrin, C., Madden, S., Hellerstein, J., Hong, W.: Model-driven data acquisition in sensor networks. In: Proceedings of the 30th International Conference on Very Large Databases (VLDB), Toronto, Canada, vol. 30, pp. 588–599 (2004)Google Scholar