The Community for Technology Leaders
RSS Icon
Issue No.06 - June (2011 vol.60)
pp: 879-889
Samer Samarah , Yarkouk University, Jordan and University of Ottawa, Ottawa
Azzedine Boukerche , University of Ottawa, Ottawa
Alexander Shema Habyalimana , University of Ottawa, Ottawa
Recently, Knowledge Discovery Process has proven to be a promising tool for extracting the behavioral patterns of sensor nodes, from wireless sensor networks. In this paper, we propose a new kind of behavioral pattern, named Target-based Association Rules (TARs). TARs aim to discover the correlation among a set of targets monitored by a wireless sensor network at a border area. The major application of the Target-based Rules is to predict the location (target) of a missed reported event. Different data preparation mechanisms for accumulating the data needed for extracting TARs have been proposed. We refer to these mechanisms as Al-Node, Schedule-Buffer, and Fused-Schedule-Buffer. Several experiment studies have been conducted to evaluate the performance of the three proposed data preparation mechanisms. Results show that the Fused-Schedule-Buffer scheme outperforms the selected schemes in terms of energy consumption.
Wireless sensor networks, behavioral patterns, data mining.
Samer Samarah, Azzedine Boukerche, Alexander Shema Habyalimana, "Target Association Rules: A New Behavioral Patterns for Point of Coverage Wireless Sensor Networks", IEEE Transactions on Computers, vol.60, no. 6, pp. 879-889, June 2011, doi:10.1109/TC.2010.227
[1] I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A Survey on Sensor Networks," IEEE Comm. Magazine, vol. 40, no. 8, pp. 102-114, Aug. 2002.
[2] F. Zhao and L.J. Guibas, Wireless Sensor Networks: An Information Processing Approach. Morgan Kaufmann Publishers, 2002.
[3] P. Leone, S.E. Nikoletseas, and J.D.P. Rolim, "Stochastic Models and Adaptive Algorithms for Energy Balance in Sensor Networks," Theory Computing Systems, vol. 47, no. 2, pp. 433-453, 2010.
[4] A. Boukerche and S. Samarah, "A Novel Algorithm for Mining Association Rules in Wireless Ad Hoc Sensor Networks," IEEE Trans. Parallel Distributed Systems, vol. 19, no. 7, pp. 865-877, July 2008.
[5] K. Akkaya and M. Younis, "A Survey on Routing Protocols for Wireless Sensor Networks," Ad Hoc Networks, vol. 3, no. 3, pp. 325-349, 2005.
[6] W.R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy Efficient Communication Protocol for Wireless Microsensor Networks," Proc. 33rd Hawaii Int'l Conf. System Sciences, pp. 8020-8030, Jan. 2000.
[7] C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva, "Directed Diffusion for Wireless Sensor Networking," IEEE/ACM Trans. Networking, vol. 11, no. 1, pp. 2-16, Feb. 2003.
[8] M. Cardei and D.-Z. Du, "Improving Wireless Sensor Network Lifetime through Power Aware Organization," Wireless Networks, vol. 11, no. 3, pp. 333-340, May 2005.
[9] S. Madden, M.J. Franklin, J.M. Hellerstein, and W. Hong, "Tag: A Tiny Aggregation Service for Ad-Hoc Sensor Networks," ACM SIGOPS Operating Systems Rev., vol. 36, no. SI, pp. 131-146, 2002.
[10] S. Samarah, A. Boukerche, and R. Yonglin, "Coverage-Based Sensor Association Rules for Wireless Vehicular Ad Hoc and Sensor Networks," Proc. IEEE Global Telecomm. Conf., pp. 1-5, Dec. 2008.
[11] S. Samarah and A. Boukerche, "Chronological Tree-A Compressed Structure for Mining Behavioral Patterns in Wireless Sensor Networks," J. Interconnected Networks, vol. 9, pp. 255-276, 2008.
[12] S. Samarah, A.S. Habyalimana, and A. Boukerche, "Target-Based Association Rules for Point-of-Coverage Wireless Sensor Networks," Proc. 14th IEEE Symp. Computers and Comm., pp. 938-943, 2009.
[13] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, "From Data Mining to Knowledge Discovery: An Overview," Advances in Knowledge Discovery and Data Mining, pp. 1-34, The MIT Press, 1996.
[14] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, "From Data Mining to Knowledge Discovery in Databases," AI Magazine, vol. 17, no. 3, pp. 37-54, 1996.
[15] A. Leon-Garcia, Probability and Random Processes for Electrical Engineering, third ed. Prentice Hall, 2008.
[16] G. Mathur, P. Desnoyers, D. Ganesan, and P. Shenoy, "Ultra-Low Power Data Storage for Sensor Networks," Proc. Fifth IEEE/ACM Conf. Information Processing in Sensor Networks, pp. 374-381, Apr. 2006.
[17] Toshiba 128-MBIT (16M8BITS/8Mx16BITS) CMOS NAND E2PROM" Datasheet: TC58DVM72A1FT00, http://www. 37378494_1.pdf, Feb. 2008.
[18] J. Han and M. Kamber, Data Mining: Concepts and Techniques, second ed. Morgan Kaufmann Publishers, 2006.
[19] R. Agrawal, T. Imielinski, and A. Swami, "Mining Association Rules between Sets of Items in Large Databases," Proc. ACM SIGMOD Conf. Management of Data, pp 207-216, May 1993.
[20] C. Ordonez, C. Santana, and D. Braal, "Discovering Interesting Association Rules in Medical Data," Proc. ACM SIGMOD Workshop Research Issues in Data Mining and Knowledge Discovery, 2000.
[21] K.K. Loo, I. Tong, B. Kao, and D. Chenung, "Online Algorithms for Mining Inter-Stream Associations from Large Sensor Networks," Proc. Ninth Pacific-Asia Conf. Knowledge Discovery and Data Mining, May 2005.
[22] K. Romer, "Distributed Mining of Spatio-Temporal Event Patterns in Sensor Networks," Proc. Int'l Conf. Distributed Computing in Sensor Systems (EAWMS/DCOSS) June 2006.
[23] G.S. Manku and R. Motwani, "Approximate Frequency Counts over Streaming Data," Proc. Int'l Conf. Very Large Databases (VLDB '02), Aug. 2002.
[24] M. Halatchev and L. Gruenwald, "Estimating Missing Values in Related Sensor Data Streams," Proc. 11th Int'l Conf. Management of Data, 2005.
[25] R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules," Proc. 20th Int'l Conf. Very Large Databases, pp. 487-499, Sept. 1994.
36 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool