Energy Efficient Computation of Data Fusion in Wireless Sensor Networks Using Cuckoo Based Particle Approach (CBPA)
Manian Dhivya, Murugesan Sundarambal, Loganathan Nithissh Anand
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DOI: 10.4236/ijcns.2011.44030   PDF    HTML     8,340 Downloads   17,095 Views   Citations

Abstract

Energy efficient communication is a plenary issue in Wireless Sensor Networks (WSNs). Contemporary energy efficient optimization schemes are focused on reducing power consumption in various aspects of hardware design, data processing, network protocols and operating system. In this paper, optimization of network is formulated by Cuckoo Based Particle Approach (CBPA). Nodes are deployed randomly and organized as static clusters by Cuckoo Search (CS). After the cluster heads are selected, the information is collected, aggregated and forwarded to the base station using generalized particle approach algorithm. The Generalized Particle Model Algorithm (GPMA) transforms the network energy consumption problem into dynamics and kinematics of numerous particles in a force-field. The proposed approach can significantly lengthen the network lifetime when compared to traditional methods.

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M. Dhivya, M. Sundarambal and L. Anand, "Energy Efficient Computation of Data Fusion in Wireless Sensor Networks Using Cuckoo Based Particle Approach (CBPA)," International Journal of Communications, Network and System Sciences, Vol. 4 No. 4, 2011, pp. 249-255. doi: 10.4236/ijcns.2011.44030.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] F. Akyildiz, W. Su, W. Sankarasubramaniam and E. Cayirci, “A Survey on Sensor Networks,” IEEE Communication Magazine, Vol. 40, No. 8, August 2002, pp. 102-114.
[2] F. P. Ferentinos, T. A. Tsilgiridis, “Adaptive Design Optimization of Wireless Sensor Networks Using Genetic Algorithms,” Computer Networks, Vol. 51, No. 4, 2007, pp. 1031- 1051.
[3] M. Dhivya, M. Sundarambal and L. N. Anand, “A Review of Energy Efficient Protocols for Wireless Sensor Networks,” Proceedings of 1st International Conference on Modeling, Control, Automation and Communication, Tamilnadu, 20-21 December 2010, pp. 273-278.
[4] D. Wei, “Clustering Algorithms for Sensor Networks and Mobile Ad Hoc Networks to Improve Energy Efficiency,” Ph.D. Thesis, University of Cape Town, Rondebosch, September 2007.
[5] A. G. Akojwar and R. M. Patrikar, “Improving Life Time of Wireless Sensor Networks Using Neural Network Based Classification Techniques with Cooperative Routing,” International Journal of Communications, Vol. 2, No. 1, 2008, pp.75-86.
[6] T. F. Shih, “Particle Swarm Optimization Algorithm for Energy-Efficient Cluster-Based Sensor Networks,” IEICE Transactions on Fundamentals, Vol. E89-A, No. 7, 2006, pp. 1950- 1958.
[7] Q. Zhao and L. Tong, “Energy-Efficient Information Retrieval for Correlated Source Reconstruction in Sensor Networks,” IEEE Transactions on Wireless Communications, Vol. 6, No. 1, 2007, pp. 157-165. doi:10.1109/TWC.2007.04885
[8] W. Heinzelman, A. Chandrakasan and H. Balakrishnan, “An Application-Specific Protocol Architecture for Wireless Microsensor Networks,” IEEE Transactions on Wireless Communications, Vol. 1, No. 4, 2002, pp. 660-670.
[9] N. Aslam, S. Sivakumar, W. Phillips and W. Robertson, “Energy Efficient Cluster Formation Using a Multi-Criterion Optimization Technique for Wireless Sensor Networks,” Proceedings of IEEE International Conference on Consumer Communications and Networking, Las Vegas, 11-13 January 2007, pp. 650-654.
[10] O. Younis and S. Fahmy, “Heed: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks,” Transactions on Mobile Computing, Vol. 3, No. 4, 2004, pp. 660-669.
[11] X.-S. Yang and S. Deb, “Cuckoo Search via Levy Flights,” Proceedings of World Congress on Nature & Biologically Inspired Computing, New Delhi, 9-11 December 2009, pp. 210-214.
[12] D. X. Shuai, X. Wang and R Gong, “A Generalized Particle Model for Social Coordination and Autonomy in MAS,” Proceedings of the 2nd IEEE International Conference on Services Systems and Services Management, Chongqing, 13-15 June 2005, pp. 985-990.
[13] X. Feng, F. C. M. Lau and D. X. Shuai, “A New Generalized Particle Approach to Parallel Bandwidth Allocation,” Computer Communications, Vol. 29, No. 18, 2006, pp. 3933-3945.
[14] D. X. Shuai, Q. Shuai, Y. M. Dong and L. J. Huang, “Problem-Solving in Multi-Agent Systems: A Novel Gen- eralized Particle Model,” Proceedings of the 1st IEEE International Multi-Symposiums on Computer and Computational Sciences, Hangzhou, 20-24 June 2006, pp. 322-329.
[15] D. X. Shuai and X. Feng, “Distributed Problem Solving in Multiagent Systems: A Spring Net Approach,” IEEE Intelligent Systems, Vol. 20, No. 4, 2005, pp. 66-74.
[16] D. X. Shuai, B. Zhang, C. P. Lu, “A New Generalized Particle Dynamics Model for Software Cybernetics,” Proceedings of IEEE International Computer Software and Applications Conference, Chicago, 17-21 September 2006, pp. 240-245.
[17] A. Chakraborty, K. Chakraborty, S. K. Mitra and M. K. Naskar, “An Energy Efficient Scheme for Data Gathering in Wireless Sensor Networks Using Particle Swarm Optimization,” Journal of Applied Computer Science, Vol. 6, No. 3, 2009, pp. 9-13.

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