Abstract
As a smart combination of cognitive radio networks and wireless sensor networks, recently introduced cognitive radio sensor network (CRSN) poses new challenges to the design of topology maintenance techniques for dynamic primary-user activities. This paper aims to provide a solution to the energy-efficient spectrum-aware CRSN clustering problem. Specifically, we design the clustered structure, establish a network-wide energy consumption model and determine the optimal number of clusters. We then employ the ideas from constrained clustering and propose both a centralized spectrum-aware clustering algorithm and a distributed spectrum-aware clustering (DSAC) protocol. Through extensive simulations, we demonstrate that DSAC can effectively form clusters under a dynamic spectrum-aware constraint. Moreover, DSAC exhibits preferable scalability and stability with its low complexity and quick convergence under dynamic spectrum variation.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Tian J F, Zheng X Y, Hu H L, et al. A survey of next generation mobile communications research in China. Chin Sci Bull, 2011, 56: 2875–2888
Akan O, Karli O, Ergul O, et al. Cognitive radio sensor networks. IEEE Network, 2009, 23: 34–40
Baddour K E, Ureten O, Willink T J. Efficient clustering of cognitive radio networks using affinity propagation. In: Proceedings of the 18th Internatonal Conference on Computer Communications and Networks, 2009 Aug 2–6, San Francisco. Washington DC: IEEE, 2009. 1–6
Zhang J Z, Wang F, Yao F Q, et al. Cluster-based distributed topology management in cognitive radio ad hoc networks. In: Proceedings of International Conference on Computer Application and System Modeling, 2010 Oct 22–24, Taiyuan. New York: ASME Press, 2010. 544–548
Heinzelman W B, Chandrakasan A P, Balakrishnan H. An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun, 2009, 1: 660–670
Younis O, Fahmy S. HEED a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput, 2004, 3: 366–379
Gong Y L, Chen G, Tan L S. A balanced serial k-means based clustering protocol for wireless sensor networks. In: Proceedings of the 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008 Oct 12–14, Dalian. Washington DC: IEEE, 2008. 1–6
Zhang H Z, Zhang Z Y, Chen X M, et al. Energy efficient joint source and channel sensing in cognitive radio sensor networks. In: Proceedings of International Conference on Communications, 2011 June 5–9, Kyoto. Washington DC: IEEE, 2011. 1–6
Wagstaff K, Cardie C, Rogers S, et al. Constrained k-means clustering with background knowledge. In: Proceedings of the 18th International Conference on Machine Learning, 2001 June 28–30, Williamstown. San Francisco: Morgan Kaufmann Publishers, 2001. 577–584
Klein D, Kamvar S D, Manning C D. From instance-level constraints to space-level constraints: Making the most of prior. In: Proceedings of the 19th International Conference on Machine Learning, 2002 July 8–12, Sydney. San Francisco: Morgan Kaufmann Publishers, 2002. 307–314
Zhang H Z, Zhang Z Y, Dai H Y, et al. Distributed spectrum-aware clustering in cognitive radio sensor networks. In: Proceedings of IEEE Globecom 2011 Conference, 2011 Dec 5–9, Houston. Washington DC: IEEE, 2011. 1–6
Author information
Authors and Affiliations
Corresponding author
Additional information
This article is published with open access at Springerlink.com
Rights and permissions
This article is published under an open access license. Please check the 'Copyright Information' section either on this page or in the PDF for details of this license and what re-use is permitted. If your intended use exceeds what is permitted by the license or if you are unable to locate the licence and re-use information, please contact the Rights and Permissions team.
About this article
Cite this article
Zhang, H., Zhang, Z. & Yuen, C. Energy-efficient spectrum-aware clustering for cognitive radio sensor networks. Chin. Sci. Bull. 57, 3731–3739 (2012). https://doi.org/10.1007/s11434-012-5254-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11434-012-5254-4