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Analysis of wireless sensor networks with sleep mode and threshold activation

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Abstract

In order to reduce the energy consumption of wireless sensor networks and control the workload of necessary topology maintenance, the sleep mode and the threshold activation process in the energy saving strategy are considered. Combining with practice, factors such as environmental interferences and physical damages are considered. A repairable M/M/2 vacation queueing model with negative customers, feedback, N-strategy and working breakdown is established. Using quasi birth-and-death process and Gauss-Seidel iterative method, the expressions of performance indicators are given. Then, using MATLAB software for numerical analysis, the influence of system parameters on performance indicators is analysed. Finally, the social optimal parameters are found by constructing benefit functions. Under certain conditions, when \(\lambda =0.64\) and \({{\beta }_{1}}=1.1\), the social benefit F can take the maximum value \(F=12.7729\).

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References

  1. Feng, Y. F. (2019). Analysis of wireless sensor network technology development. Information Systems Engineering, 5, 38. in Chinese.

    Google Scholar 

  2. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.

    Article  Google Scholar 

  3. Gaber, T., Abdelwahab, S., Elhoseny, M., & Hassanien, A. E. (2018). Trust-based secure clustering in WSN-based intelligent transportation systems. Computer Networks, 146, 151–158.

    Article  Google Scholar 

  4. Chen, Z. J., Shi, Z., & Guo, Q. Y. (2013). Design of wireless sensor network node for carbon monoxide monitoring. Telecommunication Systems, 53(1), 47–53.

    Article  Google Scholar 

  5. Liu, M. (2016). Analysis of wireless sensor network technology development. Information and Communications, 6, 286–287. in Chinese.

    Google Scholar 

  6. Jagriti, & Lobiyal, D. K. (2018). Energy consumption reduction in S-MAC protocol for wireless sensor network. Procedia Computer Science, 143, 757–764.

    Article  Google Scholar 

  7. Ramadan, K. F., Dessouky, M. I., Abd-Elnaby, M., & Abd El-Samie, F. E. (2018). Node-power-based MAC protocol with adaptive listening period for wireless sensor networks. AEU-International Journal of Electronics and Communications, 84, 46–56.

    Article  Google Scholar 

  8. Yan, Z. W., Goswami, P., Mukherjee, A., Yang, L. X., Routray, S., & Palai, G. (2019). Low-energy PSO-based node positioning in optical wireless sensor networks. Optik, 181, 378–382.

    Article  Google Scholar 

  9. Phoemphon, S., So-In, C., & Leelathakul, N. (2020). A hybrid localization model using node segmentation and improved particle swarm optimization with obstacle-awareness for wireless sensor networks. Expert Systems with Applications,. https://doi.org/10.1016/j.eswa.2019.113044.

    Article  Google Scholar 

  10. Tang, B. Q., & Zhang, L. H. (2013). Optimization of energy multi-path routing protocol in wireless sensor networks. Journal of Systems Engineering and Electronics, 35(12), 2607–2612.

    Google Scholar 

  11. Mohamed, R. E., Ghanem, W. R., Khalil, A. T., Elhoseny, M., Sajjad, M., & Mohamed, M. A. (2018). Energy efficient collaborative proactive routing protocol for Wireless Sensor Network. Computer Networks, 142, 154–167.

    Article  Google Scholar 

  12. Rizvi, S., Qureshi, H. K., Khayam, S. A., Rakocevic, V., & Rajarajan, M. (2012). A1: Anenergy efficient topology control algorithm for connected area coverage in wireless sensor networks. Journal of Network and Computer Applications, 35, 597–605.

    Article  Google Scholar 

  13. Qureshi, H. K., Rizvi, S., Saleem, M., Khayam, S. A., Rakocevic, V., & Rajarajan, M. (2011). Poly: A reliable and energy efficient topology control protocol for wireless sensor networks. Computer Communications, 34, 1235–1242.

    Article  Google Scholar 

  14. Idrees, A. K., Deschinkel, K., Salomon, M., & Couturier, R. (2018). Multiround distributed lifetime coverage optimization protocol in wireless sensor networks. The Journal of Supercomputing, 74(5), 1949–1972.

    Article  Google Scholar 

  15. Mirzaie, M., & Mazinani, S. M. (2017). MCFL: An energy efficient multi-clustering algorithm using fuzzy logic in wireless sensor network. Wireless Networks, 24(6), 2251–2266.

    Article  Google Scholar 

  16. Gao, T., Song, J. Y., Ding, J. H., & Wang, D. Q. (2017). Clustering algorithm based on fuzzy comprehensive evaluation for wireless sensor networks. International Journal of Wireless Information Networks, 24(1), 1–13.

    Article  Google Scholar 

  17. Panag, T. S., & Dhillon, J. S. (2018). Dual head static clustering algorithm for wireless sensor networks. AEU-International Journal of Electronics and Communications, 88, 148–156.

    Article  Google Scholar 

  18. Abid, B., Nguyen, T. T., & Seba, H. (2015). New data aggregation approach for time-constrained wireless sensor networks. The Journal of Supercomputing, 71(5), 1678–1693.

    Article  Google Scholar 

  19. Nisha, U. N., & Basha, A. M. (2018). Triangular fuzzy-based spectral clustering for energy-efficient routing in wireless sensor network. The Journal of Supercomputing. https://doi.org/10.1007/s11227-018-2357-y.

    Article  Google Scholar 

  20. Gu, Y. L., Xu, X., Du, J., & Qian, H. Y. (2013). Anycast routing algorithm based on zone-disjoint scheme for wireless sensor networks. Journal of Systems Engineering and Electronics, 35(10), 2086–2092.

    Google Scholar 

  21. Wang, J., & Chen, Y. (2018). Research and improvement of wireless sensor network secure data aggregation protocol based on SMART. International Journal of Wireless Information Networks, 25(3), 232–240.

    Article  MathSciNet  Google Scholar 

  22. Vahabi, S., Eslaminejad, M., & Dashti, S. E. (2019). Integration of geographic and hierarchical routing protocols for energy saving in wireless sensor networks with mobile sink. Wireless Networks, 25, 2953–2961.

    Article  Google Scholar 

  23. Ren, X. Y., & Chen, C. X. (2017). Adaptive access control strategy of cognitive radio wireless sensor network. Journal of Systems Engineering and Electronics, 39(9), 2100–2108.

    MATH  Google Scholar 

  24. Chiasserini, C. F., & Garetto, M. (2006). An analytical model for wireless sensor networks with sleeping nodes. IEEE Transactions on Mobile Computing, 5(12), 1706–1718.

    Article  Google Scholar 

  25. Jiang, F. C., Huang, D. C., Yang, C. T., & Leu, F. Y. (2012). Lifetime elongation for wireless sensor network using queue-based approaches. The Journal of Supercomputing, 59(3), 1312–1335.

    Article  Google Scholar 

  26. Fahimi, M., & Ghasemi, A. (2015). Joint spectrum load balancing and handoff management in cognitive radio networks: A non-cooperative game approach. Wireless Networks, 22(4), 1161–1180.

    Article  Google Scholar 

  27. Kryvinska, N. (2004). Intelligent network analysis by closed queuing models. Telecommunication Systems, 27(1), 85–98.

    Article  Google Scholar 

  28. Xu, Y., Liu, J., Shen, Y., Li, X., & Jiang, X. (2015). On throughput capacity of large-scale ad hoc networks with realistic buffer constraint. Wireless Networks, 23(1), 193–204.

    Article  Google Scholar 

  29. Gao, S., Wang, J. T., & Zhang, D. R. (2013). Discrete-time GI\(^X\)/Geo/1/\(N\) queue with negative customers and multiple working vacations. Journal of the Korean Statistical Society, 42(4), 515–528.

    MathSciNet  MATH  Google Scholar 

  30. Chang, F. M., Liu, T. H., & Ke, J. C. (2018). On an unreliable-server retrial queue with customer feedback and impatience. Applied Mathematical Modelling, 55, 171–182.

    Article  MathSciNet  MATH  Google Scholar 

  31. Kim, B. K., & Lee, D. H. (2014). The M/G/1 queue with disasters and working breakdowns. Applied Mathematical Modelling, 38(5–6), 1788–1798.

    Article  MathSciNet  MATH  Google Scholar 

  32. Sun, W., Li, S. Y., & Cheng-Guo, E. (2016). Equilibrium and optimal balking strategies of customers in Markovian queues with multiple vacations and N-policy. Applied Mathematical Modelling, 40(1), 284–301.

    Article  MathSciNet  MATH  Google Scholar 

  33. Yue, W. Y., Takahashi, Y., & Takagi, H. (2009). Advances in queueing theory and network applications. New York: Springer.

    Book  MATH  Google Scholar 

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Acknowledgements

This work was supported in part by the Key Foundation of Higher Education Science and Technology Research of Hebei Province under Grant No. ZD2017079, National Natural Science Foundation of China under Grant Nos. 61973261, 61872311, Natural Science Foundation of Hebei Province under Grant Nos. A2020203010, A2018203088.

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Authors and Affiliations

  1. School of Science, Yanshan University, Qinhuangdao, 066004, China

    Zhanyou Ma, Xiangran Yu, Shanshan Guo & Yang Zhang

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  1. Zhanyou Ma
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  2. Xiangran Yu
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Correspondence to Xiangran Yu.

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Ma, Z., Yu, X., Guo, S. et al. Analysis of wireless sensor networks with sleep mode and threshold activation. Wireless Netw 27, 1431–1443 (2021). https://doi.org/10.1007/s11276-020-02512-y

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