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Method for optimal allocation of network resources based on discrete probability model

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Abstract

The method for optimal allocation of network resources based on discrete probability model is proposed. In order to take into account multiple coverage of the monitored points, the method constructs the discrete probability perception model of the network nodes. The model is introduced into the solution of the node coverage area, and the optimized parameters of the sensor optimization arrangement are used to optimize the layout of the multimedia sensor nodes. After setting the node scheduling standard, the interaction force between the sensor nodes and the points on the curve path is analyzed by the virtual force analysis method based on the discrete probability model At the same time On this basis, the path coverage algorithm based on the moving target is used to optimize the coverage of the wireless sensor network node in order to achieve optimal configuration of network resources. The experimental results show that the proposed method has good convergence and can complete the node coverage process in a short time. The introduction of the node selection criteria and the adoption of the dormant scheduling mechanism greatly improve the energy saving effect and enhance the network resource optimization effect.

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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Jiang, H., Zhang, Y., & Xu, H. (2017). Optimal allocation of cooperative jamming resource based on hybrid quantum-behaved particle swarm optimisation and genetic algorithm. Iet Radar Sonar and Navigation, 11(1), 185–192.

    Article  Google Scholar 

  2. Feng, X., Jie, G. U., & Guan, X. (2017). Optimal allocation of hybrid energy storage for microgrids based on multi-attribute utility theory. Journal of Modern Power Systems and Clean Energy, 6(3), 1–11.

    Google Scholar 

  3. Rui, L. I., Wang, W., & Chen, Z. (2018). Optimal planning of energy storage system in active distribution system based on fuzzy multi-objective bi-level optimization. Journal of Modern Power Systems and Clean Energy, 6(02), 156–169.

    Google Scholar 

  4. Yu, J. I., Sun, Y., & Ming, W. U. (2017). Optimal meter allocation based on observability analysis with consideration of dg uncertainty for distribution network. Electric Power Automation Equipment, 37(3), 26–32.

    Google Scholar 

  5. Balamurugan, C., Saravanan, A., & Babu, P. D. (2017). Concurrent optimal allocation of geometric and process tolerances based on the present worth of quality loss using evolutionary optimisation techniques. Research in Engineering Design, 28(2), 185–202.

    Article  Google Scholar 

  6. Wu, Y. H. (2019). Simulation of cross-layer resource intelligent allocation for multi-objective wireless network. Computer Simulation, 36(02), 281–284.

    Google Scholar 

  7. Xu, X., Hao, J., Yu, L., et al. (2019). Fuzzy optimal allocation model for task–resource assignment problem in a collaborative logistics network. IEEE Transactions on Fuzzy Systems, 27(5), 1112–1125.

    Article  Google Scholar 

  8. Eswara, N., Chakraborty, S., Sethuram, H. P., et al. (2020). Perceptual QoE-Optimal resource allocation for adaptive video streaming. IEEE Transactions on Broadcasting, 66(2), 346–358.

    Article  Google Scholar 

  9. Jie, M., Lin, C., & Li, D. (2018). Road maintenance optimization model based on dynamic programming in urban traffic network. Journal of Advanced Transportation, 2018(4), 1–11.

    Article  Google Scholar 

  10. Hui, Q., & Zhang, H. (2017). Optimal balanced coordinated network resource allocation using swarm optimization. IEEE Transactions on Systems Man and Cybernetics Systems, 45(5), 770–787.

    Google Scholar 

  11. Cordero, A., Jaiswal, J. P., & Torregrosa, J. R. (2019). Stability analysis of fourth-order iterative method for finding multiple roots of non-linear equations. Applied Mathematics and Nonlinear Sciences, 4(1), 43–56.

    Article  MathSciNet  Google Scholar 

  12. Xiong, Z., Wu, Y., Ye, C., Zhang, X., & Xu, F. (2019). Color image chaos encryption algorithm combining crc and nine palace map. Multimedia Tools and Applications, 22(78), 31035–31055.

    Article  Google Scholar 

  13. Li, D., Huang, Z. X., & Lu, J. C. (2017). Research on the concept and mechanism of military information system based on cloud computing architecture. Journal of China Academy of Electronics and Information Technology, 12(04), 365–370.

    Google Scholar 

  14. Brown, T. S., et al. (2018). Analysis of models for viscoelastic wave propagation. Applied Mathematics & Nonlinear Sciences, 3(1), 55–96.

    Article  MathSciNet  Google Scholar 

  15. Zhang, C. H., Zhou, J. W., & Du, C. S. (2017). Review of control strategies of single-phase cascaded h-bridge multilevel inverter for grid-connected photovoltaic systems. Journal of Power Supply, 15(41), 1–8.

    Google Scholar 

  16. Hu, N. J., Zhou, W., & Zheng, J. L. (2018). Preparation and electrochemical performance of porous v2o5 microspheres. Chinese Journal of Power Sources, 42(08), 108–116.

    Google Scholar 

  17. Qu, J. J. (2017). Research on the function of electronic medical record and related problems in hospital informatization management. Automation and Instrumentation, 15(19), 226–227.

    Google Scholar 

  18. Mou, B., Bai, Y., & Patel, V. (2020). Post-local buckling failure of slender and over-design circular cft columns with high-strength materials. Engineering Structures, 210, 110197.

    Article  Google Scholar 

  19. Zeng, H., Teo, K. L., He, Y., et al. (2019). Sampled-data stabilization of chaotic systems based on a t-s fuzzy model. Information Sciences, 483, 262–272.

    Article  MathSciNet  MATH  Google Scholar 

  20. Qu, S., Zhao, L., & Xiong, Z. (2020). Cross-layer congestion control of wireless sensor networks based on fuzzy sliding mode control. Neural Computing and Applications., 32(17), 13505–13520.

    Article  Google Scholar 

  21. Shen, Q. H., Shan, Y. X., & Liu, M. (2019). Preparation of silica@bare silver nanoparticle microspheres and their effects on activity of escherichia coli. Journal of Jilin University (Science Edition), 14(22), 985–988.

    Google Scholar 

  22. Liu, S., Chan, F. T. S., Ran, W., & W. . (2016). Decision making for the selection of cloud vendor: An improved approach under group decision-making with integrated weights and objective/subjective attributes. Expert Systems with Applications, 55, 37–47.

    Article  Google Scholar 

  23. Cao, B., et al. (2020). Hybrid microgrid many-objective sizing optimization with fuzzy decision. IEEE Transactions on Fuzzy Systems, 28(11), 2702–2710.

    Article  Google Scholar 

  24. Fu, X., Fortino, G., Li, W., et al. (2019). WSNs-assisted opportunistic network for low-latency message forwarding in sparse settings. Future Generation Computer Systems, 91, 223–237.

    Article  Google Scholar 

  25. Guo, L. K., & Wang, L. (2017). Deformation of nanoporous copper subjected to high-rate compression at various temperatures. Computer Simulation, 34(27), 237–240.

    Google Scholar 

  26. Cao, B., et al. (2020). A many-objective optimization model of industrial internet of things based on private blockchain. IEEE Network, 34(5), 78–83.

    Article  Google Scholar 

  27. Cao, B., et al. (2020). Quantum-enhanced multiobjective large-scale optimization via parallelism. Swarm and Evolutionary Computation, 57, 100697.

    Article  Google Scholar 

  28. Fu, X., Fortino, G., Pace, P., et al. (2020). Environment-fusion multipath routing protocol for wireless sensor networks. Information Fusion, 53, 4–19.

    Article  Google Scholar 

  29. Fu, X., & Yang, Y. (2020). Modeling and analysis of cascading node-link failures in multi-sink wireless sensor networks. Reliability Engineering and System Safety, 197, 106815.

    Article  Google Scholar 

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Correspondence to Zhengqiang Song.

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Song, Z., Hao, G. Method for optimal allocation of network resources based on discrete probability model. Wireless Netw 28, 2743–2754 (2022). https://doi.org/10.1007/s11276-021-02727-7

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  • DOI: https://doi.org/10.1007/s11276-021-02727-7

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