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Cluster Computing

, Volume 22, Supplement 3, pp 7649–7656 | Cite as

Communication scheduling method of big data in Internet of Things based on decision feedback equalization and spread spectrum modulation technology

  • Zhi-gang Mo
  • Zheng XieEmail author
Article
  • 72 Downloads

Abstract

In order to improve the accuracy of communication transmission of big data in Internet of Things and reduce output bit error rate, a communication scheduling method of big data in Internet of things based on decision feedback equalization and spread spectrum modulation technology is proposed in this paper. In this method, a model of big data communication channel in Internet of Things is constructed, and the autocorrelation matched filtering method is used for multi-path interference suppression to communication of big data in Internet of Things; the decision feedback equalization method is used for channel equalization design of communication scheduling, and the adaptive filter is used to compensate distorted output samples; the spread spectrum modulation technology is used to improve the bandwidth of communication scheduling channel of big data and to correct frequency characteristics, so as to optimize the communication scheduling of big data in Internet of Things. The simulation results show that when the proposed method is used for communication scheduling of big data in Internet of Things, the amplitude-frequency response is improved by 300 m and the output bit error rate is reduced by 34%, which effectively improves the communication quality of big data.

Keywords

Internet of things Big data Communication scheduling Equalization Channel Filter 

Notes

Acknowledgements

This work is supported by Advanced Rail Transit Major Project under the Major Research Schedule of the 13th Five-Year Plan (No. 2016YFB1200401-102B and 2016YFB1200506).

References

  1. 1.
    Xing, S.N., Liu, F.A., Zhao, X.H.: Parallel high utility pattern mining algorithm based on cluster partition. J. Comput. Appl. 36(8), 2202–2206 (2016)Google Scholar
  2. 2.
    Zhong, K., Peng, H., Ge, L.D.: Blind equalization based on FABA-SISO for continuous phase modulation signals over time-varying frequency-selective fading channels. JEIT 37(11), 2672–2677 (2015)Google Scholar
  3. 3.
    Wang, K., Tian, C.D., Shi, Q.P., et al.: Demodulation method to suppress the influence of laser intensity disturbance in optical fiber hydrophone system. Infrared Laser Eng. 42(6), 1593–1600 (2013)Google Scholar
  4. 4.
    Sun, S.S., Wang, S., Fan, Z.F.: Flow scheduling cost based congestion control routing algorithm for data center network on software defined network architecture. J. Comput. Appl. 36(7), 1784–1788 (2016)Google Scholar
  5. 5.
    Deng, G., Gong, Z.H., Wang, H.: Characteristics research on modern data center network. J. Comput. Res. Dev. 51(2), 395–407 (2014)Google Scholar
  6. 6.
    Guo, X.Y.: Simulation and analysis on uncertain attenuation property of underwater acoustic signal for oil field pipe. Comput. Simul. 31(3), 118–121 (2014)Google Scholar
  7. 7.
    Tian, G., Yang, Z.W., Zhu, J.T., Zhang, W., Luo, W.Y.: Vibration characteristics and acoustic chaos analysis of ultrasonic infrared thermal wave test. Infrared Laser Eng. 45(3), 304003–304003 (2016)CrossRefGoogle Scholar
  8. 8.
    Chen, D., Ke, X.Z., Zhang, L.: Laser intermodulation distortion and characteristic under the turbulence channel. Acta Photonica Sinica 45(2), 0206007 (2016)CrossRefGoogle Scholar
  9. 9.
    Dou, H.J., Wang, Q.L., Zhang, X.: A joint estimation algorithm of TDOA and FDOA based on wavelet threshold de-noising and conjugate fuzzy function. JEIT 38(5), 1123–1128 (2016)Google Scholar
  10. 10.
    Xia, J., Jiang, G.B., Zhao, C.J.: Numerical study on thulium-doped mode-locked fiber laser with high modulation depth of saturable absorber. Laser Technol. 40(4), 571–575 (2016)Google Scholar
  11. 11.
    Müller, L.F., Oliveira, R.R., et al.: Survivor: an enhanced controller placement strategy for improving SDN survivability. In: IEEE Global Communications Conference (GLOBECOM), Austin, pp. 1909–1915 (2014)Google Scholar
  12. 12.
    Choi, J.S.: Design and implementation of a PCE-based software-defined provisioning framework for carrier-grade MPLS-TP networks. Photonic Netw. Commun. 29(1), 96–105 (2014)CrossRefGoogle Scholar
  13. 13.
    Lahiri, B.B., Bagavathiappan, S., Reshmi, P.R., Philip, J., Jayakumar, T., Raj, B.: Quantification of defects in composites and rubber materials using active thermography. Infrared Phys. Technol. 55(2–3), 191–199 (2012)CrossRefGoogle Scholar
  14. 14.
    Nan, H., Zhang, P., Tong, S.F., Chen, C.Y.: Performance analysis of free space coherent optical communication in atmosphere turbulence with tracking error. Acta Photonica Sinica 44(8), 0806003 (2015)CrossRefGoogle Scholar
  15. 15.
    Man, R.J., Liang, X.C.: Traffic flow forecasting based on multiscale wavelet support vector machine. Comput. Simul. 30(11), 156–159 (2013)Google Scholar
  16. 16.
    Hu, L.N., Cheng, H.Y., Chen, F.: Big data background data communication scheduling method research. Comput. Meas. Control 25(5), 176–179 (2017)Google Scholar
  17. 17.
    Xiao, L.Q.: High-speed optical fiber communication in data transmission large data reasonable scheduling model design. Laser J. 37(5), 112–116 (2016)Google Scholar
  18. 18.
    Mccanne, S.: Method and apparatus for scheduling a heterogeneous communication flow. PLoS ONE 8(1), 258–264 (2015)Google Scholar
  19. 19.
    Salhieh, A., Schwiebert, L.: Apparatus and method for scheduling in a broadband radio communication system. Int. J. Stroke 8(7), 560–565 (2015)Google Scholar
  20. 20.
    Zhang, Q., Chen, Z., Yang, L.T.: A nodes scheduling model based on Markov chain prediction for big streaming data analysis. Int. J. Commun Syst 28(9), 1610–1619 (2015)CrossRefGoogle Scholar
  21. 21.
    Dong, Q., Niyato, D., Wang, P., et al.: Deferrable load scheduling under imperfect data communication channel. Wirel. Commun. Mob. Comput. 15(17), 2049–2064 (2015)CrossRefGoogle Scholar
  22. 22.
    Gao, W., Kwong, S., Sang, H.: Low-cost memory data scheduling method for reconfigurable FFT bit-reversal circuits. Electron. Lett. 51(3), 217–219 (2015)CrossRefGoogle Scholar
  23. 23.
    Reda, W., Suresh, L., Canini, M., et al.: BRB: betteR batch scheduling to reduce tail latencies in cloud data stores. Acm Sigcomm Comput. Commun. Rev. 45(4), 607–608 (2015)CrossRefGoogle Scholar
  24. 24.
    Bojan, N.M., Zilberman, N., Antichi, G., et al.: Extreme data-rate scheduling for the data center. Acm Sigcomm Comput. Commun. Rev. 45(4), 351–352 (2015)CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.School of Civil Engineering & MechanicsHuazhong University of Science and TechnologyWuhanChina
  2. 2.College of ManagementHunan City UniversityYiyangChina

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