Spectrum Aware Cluster Formation Scheme for Cognitive Radio Sensor Network

  • Authors

    • Noorhayati Mohamed Noor
    • Norashidah Md Din
    • Zolidah Kasiran
    2018-08-13
    https://doi.org/10.14419/ijet.v7i3.15.17511
  • cognitive radio, reclustering, energy, fuzzy, network lifetime,
  • In Cognitive Radio Sensor Network (CRSN), a cognitive radio sensor node operated on a dynamic spectrum allocation with limited computational and energy resource. A cognitive radio sensor node must vacate an occupied channel degrading its performance due to reclustering as the common channel no longer available. Furthermore, energy is mostly consumed during data transmission mechanism. Clustering is the best architecture model to minimize energy consumption among the nodes. With the objective of a robust cluster while maximizing network lifetime, a fuzzy logic technique is proposed. A metric named relative common channel is also proposed. The fuzzy logic combines two input parameters, the relative common channel and residual energy to elect the best suitable cluster head to minimize reclustering and maximize the network lifetime. The performance of the proposed algorithm is compared with LEACH, SAFCA and CogLEACH. The results show that the CRSN has more extended network lifetime and more balanced energy consumption attributed to the robust cluster formation.

     

  • References

    1. [1] S. Yinbiao, K. Lee, P. Lanctot, F. Juanbin, H. Hao, B. Chow, J.-P. Desbenoit, G. Stephan, L. Hui, X. Guodong, S. Chen, D. Faulk, T. Kaiser, H. Satoh, O. Jinsong, W. Shou, Z. Yan, S. Junping, Y. Haibin, Z. Peng, L. Dong, and W. Qui, ‘Internet of Things: Wireless Sensor Networks’, Int. Electron. Commision, no. December, pp. 1–78, 2014.

      [2] Y. Wu and M. Cardei, ‘Multi-channel and cognitive radio approaches for wireless sensor networks’, vol. 94, pp. 30–45, 2016.

      [3] G. Xu, X. Tan, S. Wei, S. Guo, and B. Wang, ‘An energy-driven adaptive cluster-head rotation algorithm for cognitive radio network’, Proc. - 2010 1st Int. Conf. Pervasive Comput. Signal Process. Appl. PCSPA 2010, pp. 138–141, 2010.

      [4] S. Kim, S. Mcloone, S. K. S. Mcloone, and J. B. S. Lee, ‘Cognitively-Inspired Artificial Bee Colony Clustering for Cognitive Wireless Sensor Networks Cognitive Wireless Sensor Networks’, no. April, 2017.

      [5] R. M. Eletreby, H. M. ElSayed, and M. M. Khairy, ‘CogLEACH: A spectrum aware clustering protocol for cognitive radio sensor networks’, in International Conference on Cognitive Radio Oriented Wireless Networks and Communications., 2014, pp. 179–184.

      [6] E. Pei, H. Han, Z. Sun, B. Shen, and T. Zhang, ‘LEAUCH: low-energy adaptive uneven clustering hierarchy for cognitive radio sensor network cognitive radio sensor networks’’, EURASIP J. Wirel. Commun. Netw., vol. 2015, no. 1, p. 122, 2015.

      [7] H. Zhang, Z. Zhang, H. Dai, R. Yin, and X. Chen, ‘Distributed Spectrum-Aware Clustering in Cognitive Radio Sensor Networks’, 2014.

      [8] S. Salim, S. Moh, D. Choi, and I. Chung, ‘An energy-efficient and compact clustering scheme with temporary support nodes for cognitive radio sensor networks’, Sensors (Basel)., vol. 14, no. 8, pp. 14634–14653, 2014.

      [9] A. Rauniyar and S. Y. Shin, ‘A Novel Energy-Efficient Clustering Based Cooperative’, Int. J. Distrib. Sens. Netw, vol. 2015, 2015.

      [10] M. Ozger, E. Fadel, and O. B. Akan, ‘Event-to-Sink Spectrum-Aware Clustering in Mobile Cognitive Radio Sensor Networks’, IEEE Trans. Mob. Comput., vol. 15, no. 9, pp. 2221–2233, 2016.

      [11] T. Ahmed, ‘Clustering in Cognitive Radio for Multimedia Streaming over Wireless Sensor Networks’, pp. 1186–1192, 2015.

      [12] X. Y. Wang and A. Wong, ‘Multi-Parametric Clustering for Sensor Node Coordination in Cognitive Wireless Sensor Networks’, PLoS One, vol. 8, no. 2, 2013.

      [13] M. Ozger and O. B. Akan, ‘Event-driven spectrum-aware clustering in cognitive radio sensor networks’, in Proceedings - IEEE INFOCOM, 2013, pp. 1483–1491.

      [14] J.-M. Kim, S.-H. Park, Y.-J. Han, and T.-M. Chung, ‘CHEF: Cluster Head Election mechanism using Fuzzy logic in Wireless Sensor Networks’, 2008 10th Int. Conf. Adv. Commun. Technol., vol. 1, pp. 654–659, 2008.

      [15] N.M.Noor and N.M.Din, ‘Spectrum Aware Fuzzy Clustering Algorithm for Cognitive Radio Sensor Networks’, J. Fundam. Appl. Sci., vol. 9, no. 4s, pp. 359–383, 2017.

      [16] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, ‘An application-specific protocol architecture for wireless microsensor networks’, IEEE Trans. Wirel. Commun., vol. 1, no. 4, pp. 660–670, 2002.

  • Downloads

  • How to Cite

    Mohamed Noor, N., Md Din, N., & Kasiran, Z. (2018). Spectrum Aware Cluster Formation Scheme for Cognitive Radio Sensor Network. International Journal of Engineering & Technology, 7(3.15), 105-109. https://doi.org/10.14419/ijet.v7i3.15.17511