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Cluster Computing

, Volume 22, Supplement 2, pp 5063–5069 | Cite as

Research on the optimal cluster number of energy efficiency based on the block model of opportunistic signal

  • Tiancheng WangEmail author
Article
  • 43 Downloads

Abstract

According to the WiFi, acoustic or visible light opportunity signals in Wireless Sensor Networks (WSNs), we propose a block Compartmental model based on optimal cluster number (Compartmental Modelling). The block model is a fading model, which reflects the attenuation of the opportunity signal with the propagation distance. In order to reduce the overall energy consumption, the optimal number of clusters is calculated by using the different order of the Taylor series expansion of the block model. Finally, a real experimental platform is established by using mobile phone, wireless access point, sound and light signal to analyze the optimal number of clusters. The experimental data showed that compared with the Exponential model and the logarithmic Log model, the energy consumption of CML decreased by about 6 and 8% respectively. In addition, the energy efficiency of the visible light signal is nearly 12% compared to the WiFi harmonic signal.

Keywords

Wireless sensor network Energy efficiency Cluster Opportunity signal Block model 

References

  1. 1.
    Song, G.H., Chao, M.Y., Yang, B.W., et al.: TLR: a traffic light-based intelligent routing strategy for NGEO satellite IP networks. IEEE Trans. Wirel. Commun. 13(6), 3380–3393 (2014)Google Scholar
  2. 2.
    Nishiyama, H., Tada, Y., Kato, N., et al.: Toward optimized traffic distribution for efficient network capacity utilization in two-layered satellite networks. IEEE Trans. Veh. Technol. 62(3), 1303–1313 (2013)Google Scholar
  3. 3.
    Zhou, D., Chen, S., Dong, S.: Network traffic prediction based on ARFIMA model. Int. J. Comput. Sci. Issues 9(6), 84–87 (2013)Google Scholar
  4. 4.
    Ojeda, L.L., Kibangou, A.Y., De Wit, C.C.: Adaptive Kalman filtering for multi-step ahead traffic flow prediction, pp. 4724–4729. In: American Control Conference (2013)Google Scholar
  5. 5.
    Xiao, H., Sun, H., Ran, B.: Fuzzy-neural network traffic prediction framework with wavelet decomposition. Transp. Res. Rec. J Transp. Res. Board 1836(1), 16–20 (2003)Google Scholar
  6. 6.
    Hong, W.C.: A hybrid support vector machine regression for exchange rate prediction. Int. J. Inf. Manag. Sci. 17(2), 19–32 (2006)Google Scholar
  7. 7.
    Quan, T., Liu, X., Liu, Q.: Weighted least squares support vector machine local region method for nonlinear time series prediction. Appl. Soft Comput. 10(2), 562–566 (2010)Google Scholar
  8. 8.
    Xu, B., Han, D.: LS-SVM combination prediction technique based on prediction correlation and its application. In: Foundations of Intelligent Systems, pp. 559–566. Springer, Berlin (2014)Google Scholar
  9. 9.
    Lu, Z., Zhou, C., Wu, J., et al.: Integrating granger causality and vector auto-regression for traffic prediction of large-scale WLANs. KSII Trans. Internet Inf. Syst. 10(1), 136–151 (2016)Google Scholar
  10. 10.
    Romirermaierhofer, P., Schiavone, M., D’Alconzo, A.: Device-specific traffic characterization for root cause analysis in cellular networks, pp. 64–78. In: International Workshop on Traffic Monitoring and Analysis, TMA (2015)Google Scholar
  11. 11.
    Sakurai, Y., Papadimitriou, S., Faloutsos, C.: BRAID: stream mining through group lag correlations, pp. 599–610. In: ACM SIGMOD International Conference on Management of Data, Baltimore, Maryland, USA, June. DBLP (2005)Google Scholar
  12. 12.
    Wang, H., Hu, D.: Comparison of SVM and LS-SVM for regression, pp. 279–283. In: International Conference on Neural Networks and Brain, 2005. Icnn&b. IEEE (2005)Google Scholar
  13. 13.
    Siuly, S., Li, Y.: Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 20(4), 526 (2012)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringChangzhou UniversityChangzhouChina

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