An Efficient Mining Behavioral Pattern using Associated Correlated Bit Vector Matrix for in Wireless Sensor Network

  IJPTT-book-cover
 
International Journal of P2P Network Trends and Technology (IJPTT)          
 
© 2018 by IJPTT Journal
Volume-8 Issue-2
Year of Publication : 2018
Authors : Kaki Hemasai, K. Jagdeeshwara Rao

MLA

Kaki Hemasai, K. Jagdeeshwara Rao "An Efficient Mining Behavioral Pattern using Associated Correlated Bit Vector Matrix for in Wireless Sensor Network". International Journal of P2P Network Trends and Technology (IJPTT).V8 (2) 14-18 March to April 2018. ISSN:2249-2615. www.ijpttjournal.org. Published by Seventh Sense Research Group.

Abstract

Now a day’s wireless sensor network interesting research area for discovering behavioural patterns WSNs can be used for predicting the source of future events. By knowing the source of future event, we can detect the faulty nodes easily from the network. Behavioural patterns also can identify a set of temporally correlated sensors. This knowledge can be helpful to overcome the undesirable effects (e.g., missed reading) of the unreliable wireless communications. It may be also useful in resource management process by deciding which nodes can be switched safely to a sleep mode without affecting the coverage of the network. Association rule mining is the one of the most useful technique for finding behavioural patterns from wireless sensor network. Data mining techniques have recent years received a great deal of attention to extract interesting behavioural patterns from sensors data stream. One of the techniques for data mining is tree structure for mining behavioural patterns from wireless sensor network. By implementing the tree structure will face the problem of time taking for finding frequent patterns. By overcome that problem we are implementing associated correlated bit vector matrix for finding behavioural patterns of nodes in a wireless sensor network. By implementing this concept we can overcome time complexity and also get most correlated patterns of wireless sensor networks.

References

[1]. M. M. Rashid, I. Gondal and J. Kamruzzaman, (2013 )‟Mining associated sensor patterns for data stream of wireless sensor networks,‟ in Proc. 8th ACM Workshop Perform. Monitoring Meas. Heterogeneous Wireless Wired Netw., , pp. 91–98
[2]. Jiinlong, Xu Conglfu, Cben Weidong, Pan Yunhe,” Survey of the Study on Frequent Pattern Mining in Data Streams”, 2004 IEEE International Conference on Systems, Man and Cybernetics.
[3]R. Agrawal and R. Srikant, “Fast algorithms for Mining Association Rules”, 20th International Conference on Very Large Data Base, pp. 487–499, May1994.
[4] Md. Mamunur Rashid, Iqbal Gondal and Joarder Kamruzzaman, “Share-Frequent Sensor Patterns Mining from Wireless Sensor Network Data”, IEEE Transaction Parallel Distribution System, 2014.
[5].Imielienskin T. and Swami A. Agrawal R., "Mining Association Rules Between set of items in large databases," in Management of Data, 1993, p. 9.
[6] H. Mannila, R. Srikant, H. Toivonen, and A. Inkeri R. Agrawal, "Fast Discovery of Association Rules," in Advances in Knowledge Discovery and Data Mining, 1996, pp. 307-328.
[7] M. Chen, and P.S. Yu J.S. Park, "An Effective Hash Based Algorithm for Mining Association Rules," in ACM SIGMOD Int'l Conf. Management of Data, May, 1995.
[8] R. Motwani, J.D. Ullman, and S. Tsur S. Brin, "Dynamic Item set Counting And Implication Rules For Market Basket Data," ACM SIGMOD, International Conference on Management of Data, vol. 26, no. 2, pp. 55–264, 1997.

Keywords
Data Mining, Association rule Mining, Wireless Sensor Network, Behaviour Pattern, Associated Correlated Frequent Patterns.