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Reseach Article

Investigation of Spatio Temporal Associations in Wireless Sensor Networks

by T. Abirami, P. Thangaraj
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 15
Year of Publication: 2012
Authors: T. Abirami, P. Thangaraj
10.5120/5768-7988

T. Abirami, P. Thangaraj . Investigation of Spatio Temporal Associations in Wireless Sensor Networks. International Journal of Computer Applications. 42, 15 ( March 2012), 19-27. DOI=10.5120/5768-7988

@article{ 10.5120/5768-7988,
author = { T. Abirami, P. Thangaraj },
title = { Investigation of Spatio Temporal Associations in Wireless Sensor Networks },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 15 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 19-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number15/5768-7988/ },
doi = { 10.5120/5768-7988 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:21.724632+05:30
%A T. Abirami
%A P. Thangaraj
%T Investigation of Spatio Temporal Associations in Wireless Sensor Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 15
%P 19-27
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Wireless sensor network applications involve discovering patterns from observed events. Generally, prior knowledge of the patterns is not available. The data collected by the sensors are delivered to the sink and offline analyses on the data to extract patterns are conducted. This large volume of data collected affects the performance of the sensor network negatively due to the large communication overhead. The large overhead is a serious obstacle for deploying long lived and large scale sensor networks. In this paper, data mining techniques like Association mining to discover frequent patterns, and their spatial and temporal properties is studied. As the association mining is applied in-network, patterns and not the raw data streams are forwarded to the sink, thus reducing the communication overhead is reduced significantly. In this paper, it is proposed to investigate the association of data received in the sink from various nodes across the network.

References
  1. AGRAWAL, R. , IMIELINSKI, T. , and SWAMI, A. , 1993, Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207-216.
  2. R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules. In VLDB 1994, Santiago de Chile, Chile, September 1994.
  3. J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation. In SIGMOD 2000, Dallas, USA, May 2000.
  4. Fernando Berzal, Juan C. Cubero, Nicolas Marín, José-María Serrano, "TBAR: An efficient method for association rule mining in relational databases," Data & Knowledge Engineering, Vol. 37, No. 1, 2001, pp. 47-64.
  5. K. Koperski and J. Han. Discovery of Spatial Association Rules in Geographic Information Databases. In Proc, 4th International Symposium on Large spatial databases, pg:47-66, Portland , Maine, Aug 195.
  6. Roemer, K. : Distributed mining of spatio-temporal event patterns in sensor networks. In: Proc. of the 1st Euro-American Wkshp. on Middleware for Sensor Networks (EAWMS). (2006)
  7. F. Verhein and S. Chawla, "Mining spatio-temporal association rules, sources, sinks, stationary regions and thoroughfares in object mobility databases," in DASFAA, 2006.
  8. F. Tao, F. Murtagh, and M. Farid. Weighted association rule mining using weighted support and signi?cance framework. In Proc. 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 661–666, Washington DC, 2003.
  9. K. K. Loo, I. Tong, B. Kao, and D. Cheung, "Online Algorithms for Mining Inter-Stream Associations From Large Sensor Networks," In: PAKDD, 2005.
  10. Boukerche, A. ; Samarah, S. ; , "An Efficient Data Extraction Mechanism for Mining Association Rules from Wireless Sensor Networks," Communications, 2007. ICC '07. IEEE International Conference on , vol. , no. , pp. 3936-3941, 24-28 June 2007
  11. Intel Lab Sensor Data. http://berkeley. intel-research. net/labdata/.
  12. T. Sche?er. Finding association rules that trade support optimally against con?dence. Intelligent Data Analysis, 9(4):381 – 395, 1995.
Index Terms

Computer Science
Information Sciences

Keywords

Wireless Sensor Networks Data Mining Association Rules Spatio-temporal Patterns