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Wireless sensor network reliability modelling based on masked data
Beijing University of Posts and Telecommunications, Beijing, China .
Guizhou Institute of Technology, Guiyang, China .
Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Industriell ekonomi. Högskolan i Gävle, Centrum för logistik och innovativ produktion.
Beijing University of Posts and Telecommunications, Beijing, China .
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2015 (engelsk)Inngår i: International Journal of Sensor Networks (IJSNet), ISSN 1748-1279, E-ISSN 1748-1287, Vol. 17, nr 4, s. 217-223Artikkel i tidsskrift (Fagfellevurdert) Published
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. 

sted, utgiver, år, opplag, sider
2015. Vol. 17, nr 4, s. 217-223
Emneord [en]
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.
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Identifikatorer
URN: urn:nbn:se:hig:diva-19949DOI: 10.1504/IJSNET.2015.069584ISI: 000358693100002Scopus ID: 2-s2.0-84930432797OAI: oai:DiVA.org:hig-19949DiVA, id: diva2:838635
Tilgjengelig fra: 2015-07-01 Laget: 2015-07-01 Sist oppdatert: 2018-12-03bibliografisk kontrollert

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