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Reseach Article

Optimizing Energy in WSN Using Evolutionary Algorithm

Published on None 2011 by Nesa Sudha, Dr.M.L Valarmathi, T.Christopahpaul Neyandar
International Conference on VLSI, Communication & Instrumentation
Foundation of Computer Science USA
ICVCI - Number 12
None 2011
Authors: Nesa Sudha, Dr.M.L Valarmathi, T.Christopahpaul Neyandar
a388992e-8128-4029-8c23-a79c8f28a264

Nesa Sudha, Dr.M.L Valarmathi, T.Christopahpaul Neyandar . Optimizing Energy in WSN Using Evolutionary Algorithm. International Conference on VLSI, Communication & Instrumentation. ICVCI, 12 (None 2011), 26-29.

@article{
author = { Nesa Sudha, Dr.M.L Valarmathi, T.Christopahpaul Neyandar },
title = { Optimizing Energy in WSN Using Evolutionary Algorithm },
journal = { International Conference on VLSI, Communication & Instrumentation },
issue_date = { None 2011 },
volume = { ICVCI },
number = { 12 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 26-29 },
numpages = 4,
url = { /proceedings/icvci/number12/2719-1472/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on VLSI, Communication & Instrumentation
%A Nesa Sudha
%A Dr.M.L Valarmathi
%A T.Christopahpaul Neyandar
%T Optimizing Energy in WSN Using Evolutionary Algorithm
%J International Conference on VLSI, Communication & Instrumentation
%@ 0975-8887
%V ICVCI
%N 12
%P 26-29
%D 2011
%I International Journal of Computer Applications
Abstract

Wireless sensor network (WSN) open up new application area such as intelligent environmental and structural monitoring. One of the major challenges in WSN lies in the constraint energy and computation resource available in the sensor nodes. This paper deals with minimizing the energy resource of the wireless sensor nodes and maximizing its life time. When an event is detected in a particular area, all the nodes around the sensing range will collect the data and forward it to the upstream nodes. This makes wastage of energy because all the nodes are involved in sensing, processing and transmitting the same data.. WSN should be energy efficient in term of energy consumption and quality of path that are used to route the packets, towards the data collecting point called sink. Next node selection is based on minimum cost value. The cost depends on link quality residual energy and number of successive transmission. Genetic algorithm is used to optimize the minimum cost function. By using evolutionary optimization method minimum number of nodes is selected to obtain the optimal route.

References
  1. Flavio V.C Martins, Elizabeth F. Wanner and Geralado R.Mateus “A Hybrid Multiobjective Evolutionary approach for Improving the Performance of Wireless Sensor Networks” IEEE Sensor journal 2010
  2. J.Podpora, L.Reznik and G.Von Pless “Intelligent real time adaption for power efficiency in sensor networks” IEEE sensor journal, 2008.
  3. M.Vieira,L Vieira and L.Ruiz “Scheduling nodes in wireless sensor network : A Voronoi Approach”IEEE Internationl conference on computer network,2003.
  4. Q.Wu,N.Rao,S .Iyengar and V. Vaishnavi “On computing mobile agent routs for data fusion in distributed sensor network” IEEE transaction on knowledge and engineering 2004.
  5. F. Zhao, J. Shin and J. Reich, “Information-driven dynamic sensor collaboration for target tracking,” IEEE Signal Processing Magazine, vol. 19, no. 2, pp. 61-72, March 2002.
  6. D. Li, K. Wong, Y. H. Hu and A. Sayeed, “Detection, classification and tracking of targets in distributed sensor networks,” IEEE Signal Processing Magazine, vol. 19, no. 2, March 2002
  7. H. Yang and B. Sikdar “A Protocol for Tracking Mobile Targets using Sensor Networks” Proceedings of IEEE Workshop on Sensor Network Protocols and Applications (In conjunction with IEEE ICC), Anchogare, AK, 05/2003
  8. Croce, Silvio; Marcelloni, Francesco; Vecchio;Massimo, "Reducing poer consumption in wireless sensor networks using novel Approach to Data Aggregation", Computer Journal, volume 51, Number 2,22 March 2008, PP227-239(13). Publisher Oxford university press.
  9. Y. Shang, W. Ruml, Y. Zhang, and M. Fromherz. „Localization from mere connectivity‟. In ACM MobiHoc, pages 201–212, Annapolis, MD, June 2003.
  10. Y. Shang, W. Ruml, Y. Zhang, and M. Fromherz,‟Improved MDSBased Localization‟, In Infocom 2004
  11. Al-Karaki J.N, A.E.Kamal, " Routing Techniques in Wireless Sensor Networks: A Survey," IEEE Wireless Communications, vol.11, No. 6, Dec. 2004, pp. 6-28.
  12. Akyildiz I.F, Su W., Sankarasubramaniam Y., Cayirci E., " A Survey on Sensor Networks", IEEE Communications, Aug.2002, pp. 102-114.
  13. S. Hussain, A. W. Matin, and O. Islam, “Genetic algorithm for energy efficient clusters in wireless sensor networks,” in Fourth International Conference on Information echnology: New Generations (ITNG 2007), April 2007.
  14. D. Turgut, S. K. Das, R. Elmasri, and B. Turgut, “Optimizing clustering algorithm Global Telecommunications Conference , November 2002,
  15. S. Jin, M. Zhou, and A. S. Wu, “Sensor network optimization using a genetic algorithm,” in Proceedings of the 7th World Multiconference on Systemics, Cybernetics and Informatics, 2003.
  16. Sajid Hussain, Abdul Wasey Matin, Obidul Islam, “Genetic Algorithm for Hierarchical Wireless Sensor Networks”, JOURNAL OF NETWORKS, VOL. 2, NO. 5, SEPTEMBER 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Energy routing Genetic Algorithm PSO WSN