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  • 1.
    Zhao, B.
    et al.
    Beijing University of Posts and Telecommunications, Beijing, China .
    Yang, J.
    Guizhou Institute of Technology, Guiyang, China .
    Zhao, Ming
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Industrial economics. University of Gävle, Center for Logistics and Innovative Production.
    Li, Q.
    Beijing University of Posts and Telecommunications, Beijing, China .
    Liu, Y.
    Beijing University of Posts and Telecommunications, Beijing, China .
    Wireless sensor network reliability modelling based on masked data2015In: International Journal of Sensor Networks (IJSNet), ISSN 1748-1279, E-ISSN 1748-1287, Vol. 17, no 4, p. 217-223Article in journal (Refereed)
    Abstract [en]

    This paper studies the reliability modelling of wireless sensor networks (WSNs) with the masked data that are often observed in practice. The masked data are the system failure data when exact subsystems or components causing system failures cannot be identified. When the masked data are observed, however, it is difficult to estimate the WSN reliability since the failure processes of the subnets cannot be decomposed into simple subsystem processes. In this paper, an additive non-homogeneous poisson process (NHPP) model is proposed to describe the failure process of the WSN with subnets. The maximum likelihood estimation (MLE) procedure is developed to estimate the parameters in the proposed model. By applying the given procedure, the WSN reliability estimate can be relatively easy to obtain. A numerical example based on simulation data with random masking is also provided to illustrate the applicability of the methodology. 

  • 2.
    Zhao, Ming
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Management, Industrial Design and Mechanical Engineering, Industrial Management. University of Gävle, Center for Logistics and Innovative Production.
    Yang, Janfieng
    School of Data Science, Department of Data Science, Guizhou Institute of Technology, Guiyang, 550003, Chin.
    Zhao, Bo
    Department of Mathematics, Reliability Center of Guizhou Province, Guizhou University, Guiyang, 550025, China.
    Wu, Zhenpeng
    Department of Mathematics, Reliability Center of Guizhou Province, Guizhou University, Guiyang, 550025, China.
    Copula-based reliability modelling of wireless sensor networks with dependent failures2019In: International Journal of Sensor Networks (IJSNet), ISSN 1748-1279, E-ISSN 1748-1287, Vol. 31, no 2, p. 90-98Article in journal (Refereed)
    Abstract [en]

    Wireless sensor networks (WSNs) have widely been applied in various industries and business fields covering large geographical regions. It is therefore important to be able to model, assess and predict the reliability of WSNs since the failures can have a great effect on the monitoring or control systems that are normally depending on the WSNs. In this paper, the general WSN reliability models are developed by deleting the independent assumption of component or subsystem failures and are consequently more reasonable to characterise the failure process of WSNs. The methodology in the proposed WSN reliability models is to consider that the failure times of subsystems are dependent variables and their joint distribution is obtained by binding their marginal failure distributions together through a copula function. For specific Frank copula functions, the Star-based WSN reliability models are derived and their properties are also discussed in this paper. 

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