Authors: Bo Zhao; Jianfeng Yang; Ming Zhao; Qi Li; Yan Liu
Addresses: State Key Lab of Networking & Switching Tech, Beijing University of Posts and Telecommunications, Beijing 100876, China ' Faculty of Information Engineering, Guizhou Institute of Technology, Guiyang 550003, China ' Faculty of Engineering and Sustainable Development, University of Gävle, Gävle 801 76, Sweden ' School of Computer, Beijing University of Posts and Telecommunications, Beijing 100876, China ' State Key Lab of Networking & Switching Tech, Beijing University of Posts and Telecommunications, Beijing 100876, China
Abstract: 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.
Keywords: masked data; WSNs; wireless sensor networks; NHPP; non-homogeneous Poisson process; reliability; MLE; maximum likelihood estimation; network reliability; reliability modelling; WSN reliability; subnets; network failure; simulation; random masking.
International Journal of Sensor Networks, 2015 Vol.17 No.4, pp.217 - 223
Available online: 26 May 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article