Abstract
The reliability of wireless sensor network is affected by the credibility of nodes. With the addition of random nodes and increase of coverage, the reliability decreases. In order to improve the reliability of wireless sensor network, a reliability analysis method of intelligent computing-oriented wireless sensor network is proposed. In this method, a node optimization deployment model of wireless sensor network is established; The adaptive rotation scheduling method is adopted for optimal design of networking routing of wireless sensor network; the sensor quantization fusion tracking method is adopted for quantification of the credibility of sensor gird points; then the reliability of wireless sensor network is measured based on the analysis results with quantization fusion to improve the reliability of wireless sensor network, so as to achieve optimal network design. The simulation results show that with the proposed method in construction of wireless sensor network, the reliability of network is good. With the increase of bit error probability and packet length, the energy cost of network is gradually smaller and smaller, and the minimum energy cost can approach to 0.01 kJ, which indicates that the anti-attack ability of the proposed method is strong. When the number of data forwarding is 700, the success reception rate can reach 100%. Therefore, the proposed method is of good application value in wireless sensor network networking.
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Acknowledgments
This work is supported by National Natural Science Foundation of China under grant no. 51409090, and 41471427;The Fundamental Research Funds for the Central Universities(no.2010B08614). Special Basic Research Key Fund for Central Public Scientific Research Institutes (no.Y515018, Y516004, Y517017, Y517018); Technology Foundation for Selected Overseas Chinese Scholar, Ministry of Personnel of China (no.Rq515001).
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Xu, J., Liu, Y. & Meng, Y. Analysis and simulation of reliability of wireless sensor network based on node optimization deployment model. Cluster Comput 22 (Suppl 3), 7585–7591 (2019). https://doi.org/10.1007/s10586-018-2322-9
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DOI: https://doi.org/10.1007/s10586-018-2322-9