A Cluster Head Rotating Threshold for Cognitive Radio Network

Gui Sen Xu; Xue Zhi Tan

doi:10.4028/www.scientific.net/AMR.171-172.211

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 171-172A Cluster Head Rotating Threshold for Cognitive...

A Cluster Head Rotating Threshold for Cognitive Radio Network

Abstract:

Cluster-based topology is an energy-efficient topology control method in cognitive radio networks. The cluster head node depletes energy faster than cluster member node and the rotation of cluster head node is needed to balance the energy consumption for the whole network. Aimed at the problem of unbalanced residual energy of each node caused by cluster heads and cluster members in wireless cognitive radio network, a novel algorithm named cluster head rotating threshold (CHRT) is presented. In the proposed algorithm, rotation energy threshold is estimated using cluster head real-time energy load. The simulation results show that comparing with LEACH and EDAC, CHRT can realize the more network lifetime than the others.

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Periodical:

Advanced Materials Research (Volumes 171-172)

Pages:

211-214

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.171-172.211

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Online since:

December 2010

Authors:

Gui Sen Xu, Xue Zhi Tan

Keywords:

Clustering, Cognitive Radio Network, Energy-Driven, Rotating Threshold

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References

[1] Y. R. Kondareddy, P. Agrawal, and K. Sivalingam, in: Cognitive Radio Network setup without a Common Control Channel, edited by Military Communications Conference (2008).

DOI: 10.1109/milcom.2008.4753398

Google Scholar

[2] X. F. Yan, Y. G. Sun and Y. L. Wang, in: A Heuristic Algorithm for Minimum Connected Dominating Set with Maximal Weight in Ad Hoc Networks, edited by GCC2003 (2003).

DOI: 10.1007/978-3-540-24680-0_116

Google Scholar

[3] Y. Tang and M. T. Zhou: Maximal Independent Set Based Distributed Algorithm for Minimum Connected Dominating Set. Acta Electronica Sinica. Vol. 35 (2007), pp.868-874.

Google Scholar

[4] W. R. Heinzelman, A. Chandrakasan and H. Balakrishman, in: Energy-Efficient Communication Protocol for Wireless Microsensor Networks, edited by IEEE HICSS (2000).

Google Scholar

[5] Y. C. Wang, Q. C. Zhao and D. Z. Zheng, in: Energy-driven adaptive clustering data collection protocol in wireless sensor networks, edited by International Conference on Intelligent Macaronis and Automation (2004).

DOI: 10.1109/icima.2004.1384266

Google Scholar

[6] P. Pawelczak, S. Pollin, H. So: Performance analysis of multichannel medium access control algorithms for opportunistic spectrum access. IEEE Trans. Veh. Technology. Vol. 58 (2008), p.3014–3031.

DOI: 10.1109/tvt.2008.2009350

Google Scholar

[7] S. Bandyopahyay and E. J. Coyle, in: An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks, edited by IEEE INFOCOM (2003).

Google Scholar

[8] H. Sarvanko, M. Mustonen, in: Cooperative and noncooperative spectrum sensing techniques using Welch's periodogram in cognitive radios, edited by in Proc. Int. Workshop CogArt, (2008).

DOI: 10.1109/cogart.2008.4509987

Google Scholar

[9] J. H. Chang and L. Tassiulas, in: Energy Conserving Routing in Wireless Ad Hoc Networks, edited by INFOCOM (2000).

Google Scholar

[10] V. Mhatre and C. Rosenberg: Design Guidelines for Wireless Sensor Networks: Communication, Clustering and Aggregation. Elsevier Ad Hoc Networks Journal. Vol. 2 (2004), pp.45-63.

DOI: 10.1016/s1570-8705(03)00047-7

Google Scholar