location:Home > 2021 Vol.4 Dec.No4 > Research on Network Data Transmission Algorithm Based on Symmetric Fuzzy Competition in Cloud Computing Environment

2021 Vol.4 Dec.No4

  • Title: Research on Network Data Transmission Algorithm Based on Symmetric Fuzzy Competition in Cloud Computing Environment
  • Name: Lijuan Zhang,Lun A
  • Company: Department of Computer and Information Engineering,Inner Mongolia Vocational College of Chemical engineering,Hohhot 010070,China
  • Abstract:

    Aiming at the problems of low data transmission rate and high energy consumption in traditional network data transmission algorithms, a network data transmission algorithm based on symmetrical fuzzy competition is proposed. A video denoising method based on lognormal distribution model in Surfacelet domain is introduced. Monte Carlo method is used to estimate the probability distribution of noise in Surfacelet transform domain, and then the threshold corresponding to each coefficient is calculated to classify the coefficients. Then the prior ratio and likelihood ratio of the coefficients are calculated to denoise the interference signal in the communication channel. On the basis of the above, the likelihood ratio probability of ST coefficients is fitted by the lognormal distribution model. By calculating the shrinkage factor of ST coefficients through the prior ratio and likelihood ratio, the shrinkage of coefficients is processed, and the dynamic fuzzy feature selection algorithm based on feature variable weight is used to extract the features of the data in the communication channel. On the basis of the above, the discrete sample spectrum characteristics of network transmission data are calculated by using fuzzy competitive learning, and the feature extraction of clustering samples and the construction of information model are realized. Particle swarm optimization (PSO) is used for clustering optimization to complete network data transmission based on symmetrical fuzzy competition in cloud computing environment. The experimental results show that the proposed method can effectively reduce the energy consumption of network data transmission and improve the data transmission rate.


  • Keyword: Cloud Computing Environment; Symmetric Fuzzy Competition; Network Data Transmission Algorithm
  • DOI: 10.12250/jpciams2021090800
  • Citation form: Lijuan Zhang,Lun A.Research on Network Data Transmission Algorithm Based on Symmetric Fuzzy Competition in Cloud Computing Environment [J]. Computer Informatization and Mechanical System,2021,Vol.4,pp.1-8
Reference:

[1]Zhang, D., Chen, Z., Ren, J., Zhang, N., Awad, M., & Zhou, H., et al. 2016. Energy harvesting-aided spectrum sensing and data transmission in heterogeneous cognitive radio sensor network. IEEE Transactions on Vehicular Technology, PP(99), 1-1.

[2]Salwe, S. S., & Naik, K. K. 2017. Discrete image data transmission in heterogeneous wireless network using vertical handover mechanism  Iet Image Processing, 11(7), 550-558.

[3]Widgren, S., Engblom, S., Bauer, P., Frössling, J., Emanuelson, U., & Lindberg, A. 2016. Data-driven network modelling of disease transmission using complete population movement data: spread of vtec o157 in swedish cattle. Veterinary Research, 47(1), 81.

[4]Vijayakumar, P., Azees, M., Kannan, A., & Deborah, L. J. 2016. Dual authentication and key management techniques for secure data transmission in vehicular ad hoc networks. IEEE Transactions on Intelligent Transportation Systems, 17(4), 1015-1028.

[5]Andres-Maldonado, P., Ameigeiras, P., Prados-Garzon, J., Navarro-Ortiz, J., & Lopez-Soler, J. M. 2017. Narrowband iot data transmission procedures for massive machine-type communications. IEEE Network,31(6), 8-15.

[6]Tao, H, J., Zhang, Y, H.,& Tang, F., 2017 Massive network data transmission energy optimization method.Journal of Shenyang University of Technology, 39(1):99-103.

[7]Leng, P.,Cao, J., & Chen, W, T., 2017 LTE network data transmission optimization algorithm based on TDMA fluctuation tracking mechanism. Journal of Quantum Electronics, 34(4):499-506.

[8]Zhang, W., Hao, M., & Xu, Z. 2016. Communication optimization for rdma-based science data transmission tools. Journal of Supercomputing, 72(9), 1-16.

[9]Ye, R., Boukerche, A., Wang, H., Zhou, X., & Yan, B. 2016. Resident: a reliable residue number system-based data transmission mechanism for wireless sensor networks. Wireless Networks, 24(3), 1-14.

[10]Zeng, P., & Jin, M. 2018. Peak load forecasting based on multi-source data and day-to-day topological network. Iet Generation Transmission & Distribution, 12(6), 1374-1381.

[11]Li, H, Q., & Ye, H, J., 2017 Suitable for low-latency communication data link anti-interference transmission architecture. Journal of China Academy of Electronics and Information Technology, 12(3):289-294.

[12]Gao, L, X., Wu, X, S., et al. 2017 Summary of Research on Electromagnetic Induction Non-contact Power Transmission Technology. Journal of Power Supply, 15(2):166-178.

[13]Wang, X., Dou, R, G., & Liu, F, G., 2016 Research on Improvement of Wired Transmission Technology in Communication Engineering. Journal of Power Supply,  33(4):175-176.

[14]Tian, X,Y., & Chen, Z, Y., 2016 Research on High Dimensional Data Mining Technology in Big Data Environment. Automation & Instrumentation, (3):100-101.

[15]Wang, M, C., & Zhang, Y, C., 2017 Simulation Research on Multipath Optimization Selection of Network Data Transmission. Computer Simulation, 34(6):179-182.

 


Tsuruta Institute of Medical Information Technology
Address:[502,5-47-6], Tsuyama, Tsukuba, Saitama, Japan TEL:008148-28809 fax:008148-28808 Japan,Email:jpciams@hotmail.com,2019-09-16