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Copula-based reliability modelling of wireless sensor networks with dependent failures
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.
School of Data Science, Department of Data Science, Guizhou Institute of Technology, Guiyang, 550003, Chin.
Department of Mathematics, Reliability Center of Guizhou Province, Guizhou University, Guiyang, 550025, China.
Department of Mathematics, Reliability Center of Guizhou Province, Guizhou University, Guiyang, 550025, China.
2019 (English)In: International Journal of Sensor Networks (IJSNet), ISSN 1748-1279, E-ISSN 1748-1287, Vol. 31, no 2, p. 90-98Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
InderScience Publishers, 2019. Vol. 31, no 2, p. 90-98
Keywords [en]
Control system, Copula function, Dependence, Failure time, Joint distribution, Reliability model, Star-based WSN, Subsystem, Wireless sensor network, WSN
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:hig:diva-30739DOI: 10.1504/IJSNET.2019.102185ISI: 000485658600003Scopus ID: 2-s2.0-85072250185OAI: oai:DiVA.org:hig-30739DiVA, id: diva2:1358285
Available from: 2019-10-07 Created: 2019-10-07 Last updated: 2019-10-11Bibliographically approved

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Zhao, Ming

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