Browse > Article
http://dx.doi.org/10.3745/KIPSTD.2010.17D.3.185

An Efficient Adaptive Sampling Technique based on the Kalman Filter for Sensor Monitoring  

Kim, Min-Kee (한국기술교육대학교 컴퓨터공학과)
Min, Jun-Ki (한국기술교육대학교 컴퓨터공학부)
Publication Information
The KIPS Transactions:PartD / v.17D, no.3, 2010 , pp. 185-192 More about this Journal
Abstract
In sensor network environments, each sensor measures the physical environments according to the sampling period, and transmits a sensor reading to the base station. Thus, the sample period influences against importance resources such as a network bandwidth, and a battery power. In this paper, we propose new adaptive sampling technique that adjusts the sampling period of a sensor with respect to the features of sensor readings. The proposed technique predicts a future readings based on KF (Kalman Filter). By using the differences of actual readings and estimated reading, we identify the importance of sensor readings, and then, we adjust the sampling period according to the importance. In our experiments, we demonstrate the effectiveness of our technique.
Keywords
WSN; Adaptive Sampling; Kalman Filter;
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. Sharaf, J. Beaver, A. Labrinidis and P. Chryanthis, “Tina:A scheme for temporal coherencyaware in- network aggregation,” In Proceedings of ACM Intl. Conf. on Data Engineering for Wireless and mobile Access, Sept. 2003.   DOI
2 A. D. Marbini and L. E. Sacks. “Adaptive sampling mechanisms in sensor networks,” In London Communications Symposium, 2003.
3 R, Dantu, K. Abbas, M. O'Neill II and A. Mikler “Data centric modeling of environmental sensor networks,” Global Telecommunications Conf. 2004.   DOI
4 G. Welch and G. Bishop, “An Introduction to the Kalman Filter,” ACM SIGGRAPH Intl. Conf. on Computer Graphics and Interactive Techniques, August 2001.
5 W. R. Heinzelman, A. Chandrakasan and H. Balakrishnan “Energy-Efficient Communication Protocol for Wireless Microsensor Networks,” In proceedings of IEEE Intl. Conf. June 2000.
6 S. Gandhi, S. Nath, S. Suri, and J. Liu. GAMPS “Compressing multi sensor data by grouping and amplitude scaling,” ACM SIGMOD Intl. Conf. on Management of Data, pp.171-182, 2009.
7 C. Siyao, J. Li. “Sampling based ($\delta,\epsilon$)-approximate aggregation algorithm for sensor networks,” In Intl. Conf. on Distributed Computing Systems (ICDCS), 2009.
8 I. Lazaridis, Q. Han, X. Yu, S. Mehrotra, N. Venkatasubramanian, D. V. Kalashnikov, and W. Yang, “QUASAR: Quality aware sensing architecture,” ACM SIGMOD, Vol.33, No.1, pp.26-31, March. 2004.   DOI   ScienceOn
9 P. Bonnet, J. E. Gehrke, and P. Seshadri, “Towards sensor database systems,” In proceedings of Second Intl. Conf. on Mobile Data Management, Jan. 2001.
10 S. R. Madden, M. J. Pranklin and J. M. Hellerstein, “TinyDB: An Acquisitional Query Processing System for Sensor Networks,” ACM TODS, Vol.30, No.1, pp.122-173, 2005.   DOI   ScienceOn
11 A. Bharathidasan and V. A. S. Ponduru, “Sensor networks:an overview,” IEEE, Vol.22, pp.20–23, 2003.   DOI   ScienceOn
12 I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “Survey on sensornetworks,” IEEE Communications Magazine, Vol.40, No.8, pp.102-116, August. 2002   DOI   ScienceOn
13 S. Goel, T. Imielinski, “Precision based monitoring in sensor networks: Taking lessons form MPEG Computer Communication Review,” Vol.40, No 5, pp.82-95, 2001.
14 N. Tatbul, U. Cetintemel, S. Zdonik, M. Cherniack, and M. Stonebraker, “Load shedding in a data stream manager,” In proceedings of VLDB Intl. Conf. on VLDB, September 2003.
15 C. Olston, J. Jiang, and J. Widom, “Adaptive filters for continuous queries over distributed data streams,” In proceedings of ACM SIGMOD Intl. Conf. on Management of Data, June 2003.   DOI
16 D. Tulone, and S. Madden, “PAQ: Time Series Forecasting For Approximate Query Answering,” In proceedings of LNCS, Vol.3868, pp.21-37, 2006
17 A. Jain, E. Y. Chang, and Y. Wang “Adaptive Stream Resource anagement Using Kalman Filters,” In proceedings of SIGMOD Intl. Conf. June. 2004.   DOI