DECISION TREE BASED LOCALIZATION IN WIRELESS SENSOR NETWORKS

Authors

  • M.G. Kavitha Assistant Professor.
  • S. SendhilNathan Associate Professor University College of Engineering Pattukkottai, Rajamadam-614701 TamilNadu, India

DOI:

https://doi.org/10.24297/jac.v12i22.122

Keywords:

WSN, Sensor, Lifetime, Decision Tree, Energy.

Abstract

Localization in Wireless Sensor Network (WSN) plays a vital role in applications such as military, medical, healthcare, civil and environmental applications etc. Since all the sensor nodes in wireless sensor network are battery powered it is highly required to effectively utilize the sensor nodes in such a way that the lifetime of WSN is higher. Due to the limited availability of battery power in sensor nodes, energy consumption, computation speedup and memory consumption of localization algorithms are to be considered. In this paper a novel decision tree based approach (DTBL) for locating the nodes in WSN is discussed. The proposed approach is energy efficient in nature and high level of accuracy is obtained when compared with other localization techniques.

Downloads

Download data is not yet available.

Author Biographies

M.G. Kavitha, Assistant Professor.

Department of CSE.

S. SendhilNathan, Associate Professor University College of Engineering Pattukkottai, Rajamadam-614701 TamilNadu, India

Department of Physics

References

1. Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato, and Hwee-Pink Tan, “Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications”, cs.NI, March 2015.

2. Z. Merhi, M. Elgamel, and M. Bayoumi, “A lightweight collaborative fault tolerant target localization system for wireless sensor networks,” IEEE Transactions on Mobile Computing, vol. 8, no. 12, pp. 1690– 1704, 2009.

3. E. Cayirci, H. Tezcan, Y. Dogan, and V. Coskun, “Wireless sensor networks for underwater surveillance systems,” Ad Hoc Networks, vol. 4, no. 4, pp. 431–446, 2006.

4. Lin Gu, Dong Jia, Pascal Vicaire, “Lightweight Detection and Classification for Wireless Sensor Networks in Realistic Environments”, Sensys’05, November 2005.
5. T. He, S. Krishnamurthy, J. A. Stankovic, T. F. Abdelzaher, L. Luo, R. Stoleru, T. Yan, L. Gu, J. Hui, and B. Krogh. An energy-efficient surveillance system using wireless sensor networks. In Proc. of Intl. Conf. on Mobile Systems, Applications, and Services (MobiSys), June 2004.
6. Honeywell magnetometers.
http://www.ssec.honeywell.com/magnetic/.
7. Mica2 mote. http://www.xbow.com/Products/productsdetails.
aspx?sid=72.
8. R. Brooks, P. Ramanathan, and A. Sayeed. Distributed target classification and tracking in sensor networks. Proceedings of the IEEE, 91(8):1163–1171, 2003.

Downloads

Published

2016-12-15

How to Cite

Kavitha, M., & SendhilNathan, S. (2016). DECISION TREE BASED LOCALIZATION IN WIRELESS SENSOR NETWORKS. JOURNAL OF ADVANCES IN CHEMISTRY, 12(22), 5403–5407. https://doi.org/10.24297/jac.v12i22.122

Issue

Vol. 12 No. 22 (2016)

Section

Articles

License

Creative Commons License All articles published in Journal of Advances in Linguistics are licensed under a Creative Commons Attribution 4.0 International License.