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Nonlinear dimensionality reduction method of scheduling frequent information in wireless networks based on multilevel mapping

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Abstract

In order to reduce the redundant data deletion accuracy and bit error rate of wireless network scheduling information, make the reduced dimension information uniformly distributed on the manifold, and eliminate noise holes, a nonlinear dimensionality reduction method for wireless network scheduling frequent information based on multi-level mapping is proposed in this study. Gaussian mixture model is used to analyze the characteristics of redundant network scheduling frequent information data, and the feature coding is quantized and decomposed. On this basis, fractional Fourier transform is used to delete redundant network scheduling frequent information data. The multi-level mapping theory is introduced, and the isometric mapping algorithm is used to implement nonlinear dimensionality reduction processing for the wireless network scheduling frequent information after deleting redundant data. Aiming at the problem of reducing the effect of nonlinear dimensionality reduction under the condition of sparse data, the local linear embedding algorithm is used for secondary dimensionality reduction. Experimental results show that the proposed method can effectively delete redundant data under the conditions of dense and sparse data, and the error rate of redundant data deletion is less than 1.5%; Moreover, the integrity of the data distribution on the manifold in the low dimensional embedded space can be avoided.

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All authors declared that the manuscript has no associated data.

References

  1. Njiwa, R. K., Pierson, G., & Voignier, A. (2020). Coupling bem and the local point interpolation for the solution of anisotropic elastic nonlinear, multi-physics and multi-fields problems. International Journal of Computational Methods, 17(09), 654–661.

    Article  MathSciNet  MATH  Google Scholar 

  2. Peng, Y., Zhou, T., & Li, J. (2020). Surrogate modeling immersed probability density evolution method for structural reliability analysis in high dimensions. Mechanical Systems and Signal Processing, 152(5), 4–12.

    Google Scholar 

  3. She, B., Tian, F., Liang, W., & Zhang, G. (2018). Nonlinear model for condition monitoring and fault detection based on nonlocal kernel orthogonal preserving embedding. Shock and Vibration, 18(5), 1–16.

    Article  Google Scholar 

  4. Fu, C., Liu, Y., & Xiao, Z. (2018). Interval differential evolution with dimension-reduction interval analysis method for uncertain optimization problems. Applied Mathematical Modelling, 69(15), 441–452.

    MathSciNet  MATH  Google Scholar 

  5. Xu, S., Feng, L., Liu, S., & Qiao, H. (2020). Self-adaption neighborhood density clustering method for mixed data stream with concept drift. Engineering Applications of Artificial Intelligence, 89(18), 1034–1039.

    Google Scholar 

  6. Piorecky, M., Koudelka, V., Strobl, J., Brunovsky, M., & Krajca, V. (2019). Artifacts in simultaneous hdeeg/fmri imaging: A nonlinear dimensionality reduction approach. Sensors, 19(20), 4454–4457.

    Article  Google Scholar 

  7. Park, J. W., Cho, H., & Lee, I. (2020). Selective dimension reduction method (DRM) to enhance accuracy and efficiency of most probable point (MPP)–based DRM. Structural and Multidisciplinary Optimization, 61(3), 999–1010.

    Article  MathSciNet  Google Scholar 

  8. Das, P., Moll, M., Stamati, H., Kavraki, L., & Clementi, C. (2018). Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction. Proceedings of the National Academy of Sciences of the United States of America, 46(26), 9885–9890.

    Google Scholar 

  9. Mohsenivatani, M., Liu, Y., Derakhshani, M., Parsaeefard, S., & Lambotharan, S. (2020). Completion-time-driven scheduling for uplink NOMA-enabled wireless networks. IEEE Communications Letters, 15(9), 1456–1463.

    Google Scholar 

  10. Ye, J. Q., & Kang, J. (2019). Hyperspectral Data Reduction Method Simulation in Deep Confidence Network Environment. Computer Simulation, 36(06), 267–270.

    Google Scholar 

  11. Bossner, S., Devisscher, T., Suljada, T., Ismail, C. J., Sari, A., & Mondamina, N. W. (2019). Barriers and opportunities to bioenergy transitions: An integrated, multilevel perspective analysis of biogas uptake in Bali. Biomass & bioenergy, 122(25), 457–465.

    Article  Google Scholar 

  12. Liu, S., Wang, S., Liu, X., Dai, J., Muhammad, K., Gandomi, A. H., Ding, W., Hijji, M., & Albuquerque, V. H. C. (2022). Human Inertial Thinking Strategy: A Novel Fuzzy Reasoning Mechanism for IoT-Assisted Visual Monitoring. IEEE Internet of Things Journal, online first,. https://doi.org/10.1109/JIOT.2022.3142115

    Article  Google Scholar 

  13. Sukojo, B. M., & Prastika, C. (2018). Landslide hazard analysis in tuban regency using multilevel satellite imagery processing for landslide potential mapping. IOP Conference Series Earth and Environmental Science, 165(1), 1202–1208.

    Google Scholar 

  14. De Leseleuc, S., Weber, S., Lienhard, V., Barredo, D., Buechler, H. P., & Lahaye, T. (2018). Accurate mapping of multilevel rydberg atoms on interacting spin-1/2 particles for the quantum simulation of Ising models. Physical Review Letters, 120(11), 1136–1144.

    Article  Google Scholar 

  15. Persmark, A., Wemrell, M., Zettermark, S., Leckie, G., Subramanian, S. V., & Merlo, J. (2019). Precision public health: Mapping socioeconomic disparities in opioid dispensations at Swedish pharmacies by multilevel analysis of individual heterogeneity and discriminatory accuracy (Maihda). PLoS ONE, 14(8), 223–229.

    Article  Google Scholar 

  16. Liu, S., Bai, W., Liu, G., Li, W., & Srivastava, H. M. (2018). Parallel fractal compression method for big video data. Complexity, 18(6), 676–681.

    MATH  Google Scholar 

  17. Kuzmenko, E. I., Frolov, A. A., & Silaev, A. V. (2018). Geoinformational mapping of landscapes in the northwestern part of western Siberia using the Hansen mosaic dataset. Geography & Natural Resources, 39(2), 175–181.

    Article  Google Scholar 

  18. Salvato, G., Richter, F., Sedeno, L., Bottini, G., & Paulesu, E. (2020). Building the bodily self-awareness: Evidence for the convergence between interoceptive and exteroceptive information in a multilevel kernel density analysis study. Human Brain Mapping, 41(1), 175–181.

    Google Scholar 

  19. Hernández-Yumar, A., Wemrell, M., Abásolo, I. A., López-Valcárcel, B. G., Leckie, G., & Merlo, J. (2018). Socioeconomic differences in body mass index in Spain: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy. PLoS ONE, 13(12), 5–7.

    Article  Google Scholar 

  20. Yang, Y., Wen, H., Fan, M., He, L., Xie, M., Chen, R., Norambuena, M., & Rodríguez, J. (2020). Multiple-voltage-vector model predictive control with reduced complexity for multilevel inverters. IEEE Transactions on Transportation Electrification, 6(1), 105–117.

    Article  Google Scholar 

  21. Shuai, L., Xiyu, X., Yang, Z., Khan, M., & Weina, F. (2022). A reliable sample selection strategy for weakly-supervised visual tracking. IEEE Transactions on Reliability, online first, 110(19), 2346–2352.

    Google Scholar 

  22. Lin, Y., Tu, Y., Dou, Z., Chen, L., & Mao, S. (2020). Contour Stella image and deep learning for signal recognition in the physical layer. IEEE Transactions on Cognitive Communications and Networking, 7(1), 34–46.

    Article  Google Scholar 

  23. Liu, S., Huang, S., Wang, S., Muhammad, K., Bellavista, P. & Ser, J. D. (2023). Visual tracking in complex scenes: A location fusion mechanism based on the combination of multiple visual cognition flows. Information Fusion. https://doi.org/10.1016/j.inffus.2023.02.005

    Article  Google Scholar 

  24. Liu, P., Zhu, X., Hu, X., Xiong, A., & Wu, R. (2019). Local tangent space alignment and relevance vector machine as nonlinear methods for estimating sensory quality of tea using NIR spectroscopy. Vibrational Spectroscopy, 103(62), 1029–1036.

    Google Scholar 

  25. Xiao, P., Zhong, Y., Dan, L., & Bing, D. (2019). Accurate recovery of 3d local field in frp laminated beam based on asymptotic dimension reduction model. Construction and Building Materials, 207(20), 357–372.

    Article  Google Scholar 

  26. Shuai, L., Peng, G., Yating, L., Weina, Fu., & Weiping, D. (2023). Multi-modal fusion network with complementarity and importance for emotion recognition. Information Sciences, 619, 679–694.

    Article  Google Scholar 

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Acknowledgements

Authors would like to acknowledge contribution to this research from the Rector of the Silesian University of Technology, Gliwice, Poland under pro-quality grant no. 09/010/RGJ22/0068.

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Author notes

  1. Jian-zhao Sun and Marcin Woźniak have been equally contributed to this work.

Authors and Affiliations

  1. School of Computer Engineering, Henan Institute of Economics and Trade, Zhengzhou, 450000, Henan, China

    Jian-zhao Sun

  2. Medical Department, Zhengzhou No. 7 People’s Hospital, Zhengzhou, 450000, Henan, China

    Kun Yang

  3. Faculty of Applied Mathematics, Silesian University of Technology, 44100, Gliwice, Poland

    Marcin Woźniak

Authors
  1. Jian-zhao Sun
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  2. Kun Yang
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  3. Marcin Woźniak
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Contributions

All authors contributed to the study conception and design. Material preparation was performed by J-zS and KY, data collection and analysis were performed by MW. The first draft of the manuscript was written by J-zS and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jian-zhao Sun.

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Sun, Jz., Yang, K. & Woźniak, M. Nonlinear dimensionality reduction method of scheduling frequent information in wireless networks based on multilevel mapping. Wireless Netw 29, 2897–2907 (2023). https://doi.org/10.1007/s11276-023-03236-5

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