The Community for Technology Leaders
RSS Icon
Issue No.07 - July (2008 vol.19)
pp: 865-877
In this paper, we propose a comprehensive framework for mining Wireless Ad-hoc Sensor Networks (WASNs) that is able to extract patterns regarding the sensors' behaviors. The main goal of determining behavioral patterns is to use these to generate rules that will improve the WASN's Quality of Service by participating in the resource management process or compensating for the undesired side effects of wireless communication. The proposed framework consists of a formal definition of sensor behavioral patterns and sensor association rules, a novel representation structure named the Positional Lexicographic Tree (PLT) that is able to compress the data gathered for the mining process and thus allows the fast and efficient mining of sensor behavioral patterns, as well as a distributed data extraction mechanism to prepare the data required for mining sensor behavioral patterns. To report on the performance of the mining framework, several experiments have been conducted to evaluate the PLT structure and the proposed data extraction mechanism.
Performance Evaluation, Sensor Networks, Distributed data mining
Azzedine Boukerche, Samer Samarah, "A Novel Algorithm for Mining Association Rules in Wireless Ad Hoc Sensor Networks", IEEE Transactions on Parallel & Distributed Systems, vol.19, no. 7, pp. 865-877, July 2008, doi:10.1109/TPDS.2007.70789
[1] A. Boukerche, Handbook of Algorithms for Wireless Networking and Mobile Computing. Chapman & Hall/CRC, 2005.
[2] M. Ould-Khaoua and M. Takai, Proc. Second ACM Int'l Workshop Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (PE-WASUN), 2005.
[3] A. Boukerche, R.W. Pazzi, and R.B. Araujo, “Fault-Tolerant Wireless Sensor Network Routing Protocols for the Supervision of Context-Aware Physical Environments,” J. Parallel and Distributing Computing, vol. 66, no. 4, 2006.
[4] R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules between Sets of Items in Large Databases,” Proc. ACM SIGMOD '93, May 1993.
[5] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. 20th Int'l Conf. Very Large Data Bases (VLDB '94), Sept. 1994.
[6] J.S. Park, M. Chen, and P.S. Yu, “An Effective Hash-Based Algorithm for Mining Association Rules,” Proc. ACM SIGMOD'95, May 1995.
[7] S. Brin, R. Motwani, J.D. Ullman, and S. Tsur, “Dynamic Itemset Counting and Implication Rules for Market Basket Data,” Proc. ACM SIGMOD '97, May 1997.
[8] A. Savasere, E. Omiecinski, and S.B. Navathe, “An Efficient Algorithm for Mining Association Rules in Large Databases,” Proc. 21st Int'l Conf. Very Large Data Bases (VLDB '95), Sept. 1995.
[9] B. Goethals, , 2007.
[10] Intel Lab Data, /, 2007.
[11] G. Grahne and J. Zhu, “Efficiently Using the Prefix-Trees in Mining Frequent Itemsets,” Proc. Workshop Frequent Itemset Mining Implementations (FIMI '03), Nov. 2003.
[12] M. EL-Hajj and O.R. Zaiane, “Non-Recursive Generation of Frequent K-Itemset from Frequent Pattern Tree Representation,” Proc. Workshop Frequent Itemset Mining Implementations (FIMI), 2003.
[13] J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang, “H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases,” Proc. First IEEE Int'l Conf. Data Mining (ICDM), 2001.
[14] J. Han, J. Pei, Y. Yin, and R. Mao, “Mining Frequent Patternswithout Candidate Generation: A Frequent-Pattern TreeApproach,” Data Mining and Knowledge Discovery, vol. 8, no. 1, pp. 53-87, 2004.
[15] “Frequent Itemset Mining Implementations,” Proc. IEEE ICDM Workshop on Frequent Itemset Mining Implementations (FIMI '04),, 2004.
[16] R.P. Gopalan and Y.G. Sucahyo, “TreeITL-Mine: Mining Frequent Itemsets Using Pattern Growth, Tid Intersection, and Prefix Tree,” Proc. 15th Australian Joint Conf. Artificial Intelligence (AI'02), Dec. 2002.
[17] 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 (PAKDD '05), May 2005.
[18] K. Römer, “Distributed Mining of Spatio-Temporal Event Patternsin Sensor Networks,” Proc. Euro-American Workshop Middleware for Sensor Networks (EAWMS '06), June 2006.
[19] M. Halatchev and L. Gruenwald, “Estimating Missing Values inRelated Sensor Data Streams,” Proc. 11th Int'l Conf. Management of Data (COMAD '05), Jan. 2005.
[20] 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 (IPSN '06), Apr. 2006.
[21] P. Desnoyers, D. Ganesan, H. Li, and P. Shenoy, “PRESTO: APredictive Storage Architecture for Sensor Networks,” Proc. 10thWorkshop Hot Topics in Operating Systems (HotOS'05), June 2005.
[22] I.F. Akyildiz and E.P. Stuntebeck, “Wireless Underground Sensor Networks: Research Challenges,” Ad Hoc Networks, vol. 4, no. 6, pp. 669-686, 2006.
[23] 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.
[24], 2007.
[25] Y.G. Sucahyo and R.P. Gopalan, “CT-ITL: Efficient Frequent Item Set Mining Using a Compressed Prefix Tree with PatternGrowth,” Proc. 14th Australasian Database Conf. (ADC), 2003.
[26] G.S. Manku and R. Motwani, “Approximate Frequency Counts over Streaming Data,” Proc. 28th Int'l Conf. Very Large Data Bases (VLDB '02), Aug. 2002.
36 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool