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Graph cut based clustering for cognitive radio ad hoc networks without common control channels

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Abstract

Clustering is an efficient tool to improve the routing and data transmission performance in large scale networks. However, in cognitive radio ad hoc networks (CRAHNs), clustering design is challenging due to the dynamic spectrum access and the blind information environment. In this paper, we propose a novel distributed clustering algorithm for CRAHNs, where neither a dedicated common control channel (CCC) nor prior topology information is required. First, a neighbor discovery protocol without relying on CCC is proposed to construct the local topology. Then, we model the network as a undirected graph and formulate the clustering process as a graph cut problem. We design a mincut based heuristic algorithm to approximate the optimal clustering solution. After this, we also present a synchronize protocol to achieve the global consistency of cluster memberships. Finally, we propose a proactive cluster maintenance mechanism to reduce the interferences caused by PU activities. We validate our work through comparisons with other clustering methods. The simulation results show that, by adjusting the cluster structure according to the changing spectrum, the proposed method reduces the interference and improves the network efficiency.

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References

  1. Akyildiz, I. F., Lee, W. Y., & Chowdhury, K. R. (2009). Crahns: Cognitive radio ad hoc networks. Ad Hoc Networks, 7(5), 810–836.

    Article  Google Scholar 

  2. Akyildiz, I. F., Lee, W. Y., Vuran, M. C., & Mohanty, S. (2006). Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50(13), 2127–2159.

    Article  MATH  Google Scholar 

  3. Banerjee, S., & Khuller, S. (2001). A clustering scheme for hierarchical control in multi-hop wireless networks. In INFOCOM 2001. Twentieth annual joint conference of the IEEE computer and communications societies. Proceedings (Vol. 2, pp. 1028–1037). IEEE.

  4. Bany Salameh, H. A., & El-Attar, M. F. (2015). Cooperative OFDM-based virtual clustering scheme for distributed coordination in cognitive radio networks. IEEE Transactions on Vehicular Technology, 64(8), 3624–3632.

    Article  Google Scholar 

  5. Boykov, Y., & Kolmogorov, V. (2004). An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9), 1124–1137.

    Article  MATH  Google Scholar 

  6. Cabric, D., Mishra, S. M., & Brodersen, R. W. (2004). Implementation issues in spectrum sensing for cognitive radios. In Conference record of the thirty-eighth Asilomar conference on signals, systems and computers, 2004 (Vol. 1, pp. 772–776). IEEE.

  7. Chen, T., Zhang, H., Maggio, G. M., & Chlamtac, I. (2007). Cogmesh: A cluster-based cognitive radio network. In DySPAN 2007. 2nd IEEE international symposium on new frontiers in dynamic spectrum access networks, 2007 (pp. 168–178). IEEE.

  8. Chen, T., Zhang, H., Maggio, G. M., & Chlamtac, I. (2007). Topology management in cogmesh: A cluster-based cognitive radio mesh network. In IEEE International Conference on communications, 2007. ICC’07 (pp. 6516–6521). IEEE.

  9. Dai, Y., Wu, J., & Xin, C. (2013). Virtual backbone construction for cognitive radio networks without common control channel. In INFOCOM, 2013 Proceedings IEEE (pp. 1456–1464). IEEE.

  10. Driouch, E., Ajib, W., & Assi, C. (2016). Power control and clustering in heterogeneous cellular networks. Wireless Networks. doi:10.1007/s11276-016-1299-7.

  11. Estrin, D., Govindan, R., Heidemann, J., & Kumar, S. (1992). Next century challenges: Scalable coordination in sensor networks. In Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking (pp. 263–270). London: ACM.

  12. Lazos, L., Liu, S., & Krunz, M. (2009). Spectrum opportunity-based control channel assignment in cognitive radio networks. In 6th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks, 2009. SECON’09 (pp. 1–9). IEEE.

  13. Lin, C. R., & Gerla, M. (1997). Adaptive clustering for mobile wireless networks. IEEE Journal on Selected Areas in Communications, 15(7), 1265–1275.

    Article  Google Scholar 

  14. Lin, Z., Liu, H., Chu, X., & Leung, Y. W. (2011). Jump-stay based channel-hopping algorithm with guaranteed rendezvous for cognitive radio networks. In INFOCOM, 2011 proceedings IEEE (pp. 2444–2452). IEEE.

  15. Park, J.-H., Nam, Y., & Chung, J.-M. (2015). Sustainability enhancement multihop clustering in cognitive radio sensor networks. International Journal of Distributed Sensor Networks. doi:10.1155/2015/574340.

  16. Qian, L. P., Zhang, S., Zhang, W., & Zhang, Y. J. (2015). System utility maximization with interference processing for cognitive radio networks. IEEE Transactions on Communications, 63(5), 1567–1579.

    Article  Google Scholar 

  17. Rauniyar, A., & Shin, S. Y. (2015). A novel energy-efficient clustering based cooperative spectrum sensing for cognitive radio sensor networks. International Journal of Distributed Sensor Networks, 2015, 91.

    Google Scholar 

  18. Sohn, I., Lee, J. H., & Lee, S. H. (2016). Low-energy adaptive clustering hierarchy using affinity propagation for wireless sensor networks. IEEE Communications Letters, 20(3), 558–561.

    Article  Google Scholar 

  19. Tyagi, S., Tanwar, S., Kumar, N., & Rodrigues, J. J. (2015). Cognitive radio-based clustering for opportunistic shared spectrum access to enhance lifetime of wireless sensor network. Pervasive and Mobile Computing, 22, 90–112.

    Article  Google Scholar 

  20. Xu, Y., Wang, J., Wu, Q., Anpalagan, A., & Yao, Y. D. (2012). Opportunistic spectrum access in cognitive radio networks: Global optimization using local interaction games. IEEE Journal of Selected Topics in Signal Processing, 6(2), 180–194.

    Article  Google Scholar 

  21. Younis, O., & Fahmy, S. (2004). Distributed clustering in ad-hoc sensor networks: A hybrid, energy-efficient approach. In INFOCOM 2004. Twenty-third annual joint conference of the IEEE computer and communications societies, (Vol. 1). IEEE.

  22. Zhang, J., Zhao, H., Cao, L., & Chen, Y. (2015). Robust clustering for cognitive radio ad hoc networks with group mobility. In 2015 IEEE/CIC international conference on communications in China (ICCC) (pp. 1–6). IEEE.

  23. Zhao, J., Zheng, H., & Yang, G.-H. (2005). Distributed coordination in dynamic spectrum allocation networks. In First IEEE international symposium on new frontiers in dynamic spectrum access networks, 2005. DySPAN 2005 (pp. 259–268). IEEE.

  24. Zhao, J., Zheng, H., & Yang, G. H. (2007). Spectrum sharing through distributed coordination in dynamic spectrum access networks. Wireless Communications and Mobile Computing, 7(9), 1061–1075.

    Article  Google Scholar 

  25. Zhao, N., & Sun, H. (2011). Robust power control for cognitive radio in spectrum underlay networks. TIIS, 5(7), 1214–1229.

    Article  Google Scholar 

  26. Zhao, N., Yu, F. R., Sun, H., & Li, M. (2016). Adaptive power allocation schemes for spectrum sharing in interference alignment (IA)-based cognitive radio networks.

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Authors and Affiliations

  1. Department of Computer, Wuhan University, Wuhan, Hubei, China

    Huyin Zhang, Ning Xu, Fang Xu & Zhiyong Wang

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  1. Huyin Zhang
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  2. Ning Xu
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  3. Fang Xu
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Correspondence to Huyin Zhang.

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Zhang, H., Xu, N., Xu, F. et al. Graph cut based clustering for cognitive radio ad hoc networks without common control channels. Wireless Netw 24, 209–221 (2018). https://doi.org/10.1007/s11276-016-1329-5

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