CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Energy Efficient Wireless Sensor Network using Genetic Algorithm based Association Rules

by T. Abirami, P. Priakanth
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 91 - Number 10
Year of Publication: 2014
Authors: T. Abirami, P. Priakanth
10.5120/15915-4783

T. Abirami, P. Priakanth . Energy Efficient Wireless Sensor Network using Genetic Algorithm based Association Rules. International Journal of Computer Applications. 91, 10 ( April 2014), 8-12. DOI=10.5120/15915-4783

@article{ 10.5120/15915-4783,
author = { T. Abirami, P. Priakanth },
title = { Energy Efficient Wireless Sensor Network using Genetic Algorithm based Association Rules },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 91 },
number = { 10 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 8-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume91/number10/15915-4783/ },
doi = { 10.5120/15915-4783 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:12:21.609886+05:30
%A T. Abirami
%A P. Priakanth
%T Energy Efficient Wireless Sensor Network using Genetic Algorithm based Association Rules
%J International Journal of Computer Applications
%@ 0975-8887
%V 91
%N 10
%P 8-12
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Wireless Sensor Networks (WSN) usually contains thousands or hundreds of sensors which are randomly deployed. Sensors are powered by battery, which is an important issue in sensor networks, since routing consumes a lot of energy. Such nodes are deployed in thousands to form a network with capacity to report to a data collection sink (base station). An efficient routing scheme in sensor network is also important. Networking unattended sensor nodes are expected to have significant impact on the efficiency of many military and civil applications such as combat field surveillance, security and disaster management. Genetic algorithm (GA) based data aggregation trees are used where the sensors receive data from neighboring nodes, aggregate the incoming data packets, and forward the aggregated data to a suitable neighbor. GA is used to create energy efficient data aggregation trees. In this work, the amount of data sent to sink is reduced using association rule mining and in turn to further reduce the energy consumption of the network; optimal routes are chosen to transmit data to the sink based on energy consumption. The proposed method is able to discover the association rules to make predictive analysis on node failure, asymmetric links. The rules found form the basis for coding solutions in the proposed genetic algorithm. GA is applied to generate balanced and energy efficient data aggregation spanning trees for wireless sensor networks. E-Span, which is an energy-aware spanning tree algorithm and Lifetime-Preserving Tree (LPT) are used to create data aggregation trees. The proposed GA extends network lifetime.

References
  1. F. Zhao and L. J. Guibas. Wireless Sensor Networks: an Information Processing Approach. Morgan Kaufmann publisher, 2002.
  2. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. "Wireless Sensor Networks: A Survey" IEEE Transactions on Systems, Man and Cybernetics (B), Vol. 38(4), pp:394-422, 2002.
  3. C. S. R. Murthy, and B. S. Manoj. Ad Hoc Wireless Networks Architecture and Protocols. Prentice Hall PTR, New Jersey, 2004.
  4. C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva. "Directed Diffusion for Wireless Sensor Networking", IEEE/ACM Transactions on Networking, Vol. 11, no. 1, pp: 2-16, 2003.
  5. Intel Lab Sensor Data. http://berkeley. intel-research. net/labdata/.
  6. Sotiris Kotsiantis, Dimitris Kanellopoulos, 2006, "Association Rules Mining: A Recent Overview", GESTS International Transactions on Computer Science and Engineering, Vol. 32 (1), pp. 71-82
  7. 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.
  8. R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules. In VLDB 1994, Santiago de Chile, Chile, September 1994.
  9. J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation. In SIGMOD 2000, Dallas, USA, May 2000
  10. Zaki, M. J. : Scalable Algorithms for Association Mining, IEEE Transactions on Knowledge and Data Engineering, 12(3), 2000, 372–390.
  11. Mata, J. , Alvarez, J. , Riquelme, J. : An Evolutionary Algorithm to Discover Numeric Association Rules, ACM Symposium on Applied Computing, Madrid, Spain, March 2002.
  12. Mata, J. , Alvarez, J. , Riquelme, J. : Mining Numeric Association Rules with Genetic Algorithms, 5th International Conference on Arti?cial Neural Networks and Genetic Algorithms, Taipei, Taiwan, April 2001.
  13. Alippi, C. , Anastasi, G. , Di Francesco, M. and Roveri, M. "An Adaptive Sampling Algorithm for Effective Energy Management in Wireless Sensor Networks with Energy-Hungry Sensors", Instrumentation and Measurement, IEEE Transactions on, Vol. 59, No. 2, pp. 335-344, 2010.
  14. Alippi, G. , Anastasi, C. , Galperti, F. , Mancini, M. and Roveri "Adaptive Sampling for Energy Conservation in Wireless Sensor Networks for Snow Monitoring Applications", Proc. of IEEE International Workshop on Mobile Ad-hoc and Sensor Systems for Global and Homeland Security (MASS-GHS 2007), Pisa (Italy), October 8, 2007.
  15. Boukerche, A. and Samarah, S. "An Efficient Data Extraction Mechanism for Mining Association Rules from Wireless Sensor Networks", Communications, ICC '07. IEEE International Conference on, Vol. 2, No. 2, pp. 3936 - 3941, 24-28 June 2007.
  16. Chong, S. K. , Gaber, M. M. , Krishnaswamy, S. and Loke, S. W. "Energy Conservation in Wireless Sensor Networks: A Rule-Based Approach", Knowledge and Information Systems, Vol. 28, No. 3,pp. 579-614, 2011.
  17. Fernando Berzal, Juan Cubero, C. , Nicolas Marín and José-María Serrano "TBAR: An Efficient Method for Association Rule Mining in Relational Databases", Data and Knowledge Engineering, Vol. 37, No. 1, pp. 47- 64, 2001.
  18. Kandris, D. , Tsioumas, P. , Tzes, A. , Nikolakopoulos, G. and Vergados, D. D. "Power conservation through energy efficient routing in wireless sensor networks", Sensors, Vol. 9, No. 9, pp. 7320-7342, 2009.
  19. Maraiya, K. , Kant, K. and Gupta, N. "Wireless Sensor Network: A Review on Data Aggregation", International Journal of Scientific and Engineering Research, Vol. 2, No. 4, pp. 1-6, 2011.
  20. Venturini, G. "Sia: a Supervised Inductive Algorithm with Genetic Search for Learning Attrib-ute based Concepts", in: Proceedings of the European Conference on Machine Learning, of Lecture Notes in Computer Science, Viena, Austria, Vol. 667, pp. 280–296, April 1993.
  21. Vergados, D. J. , Pantazis, N. and Vergados, D. Energy-Efficient Route Selection Strategies for Wireless Sensor Networks", Mobile Netw. Appl. , Vol. 13, pp. 285–296, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Wireless sensor networks genetic algorithm energy efficient data aggregation Trees Association Rules

Powered by PhDFocusTM