An Additive NHPP-Based Reliability Model of Wireless Sensor Networks

Abstract:

As the Wireless Sensor Networks (WSNs) are widely implemented in various fields recent years, the quality of WSNs has been increasingly concerned. WSNs can usually be divided into sub-nets, which assumed to work or fail independently. Through the failure data of those sub-nets, the additive NHPP model for reliability evaluation is composed, and then the maximum likelihood estimation is applied to estimate the unknown parameters in the model. Finally, the simulation shows that the additive NHPP model is better than general NHPP model under certain circumstances.

• Periodical: Applied Mechanics and Materials (Volumes 602-605)
• Pages: 3206-3212
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.602-605.3206
• Online since: August 2014
• Authors: Bo Zhao*, Jian Feng Yang, Ming Zhao, Qi Li, Yan Liu
• Keywords: Maximum Likelihood-Estimation (MLE), NHPP, Reliability, Wireless Sensor Network (WSN)

References

[1] Akyildiz I F, Su W, Sankarasubramaniam Y, et al. Wireless sensor networks: a survey[J]. Computer networks, 2002, 38(4): 393-422.

DOI: 10.1016/s1389-1286(01)00302-4

[2] Akyildiz I F, Vuran M C. Wireless sensor networks[M]. John Wiley & Sons, (2010).

[3] Duane J T. Learning curve approach to reliability monitoring[J]. Aerospace, IEEE Transactions on, 1964, 2(2): 563-566.

DOI: 10.1109/ta.1964.4319640

[4] Crow, L. H., Reliability Analysis for Complex, Repairable Systems, SIAM Reliability and Biomeiry, Philadelphia, 1974: 379-410.

[5] Goel A L, Okumoto K. A time dependent error detection rate model for software reliability and other performance measures[J]. IEEE Transactions on Reliability, 1979, 28(3): 206-211.

DOI: 10.1109/tr.1979.5220566

[6] Xie M. Software reliability modelling[M]. World Scientific, (1991).

[7] Zhao M, Xie M. EM algorithms for estimating software reliability based on masked data[J]. Microelectronics Reliability, 1994, 34(6): 1027-1038.

DOI: 10.1016/0026-2714(94)90067-1

[8] Zhao M. Superposition of Power-Law Models for Hardware/Software System Reliability Data Analysis[J]. International Journal of Reliability, Quality and Safety Engineering, 2003, 10(02): 121-130.

DOI: 10.1142/s0218539303001044

[9] Yamada S. Software Reliability Modeling: Fundamentals and Applications[M]. Springer Briefs in Statistics, Springer Japan, (2014).

[10] Crow L H. Evaluating the reliability of repairable systems[C]. Proceedings of Annual Reliability and Maintainability Symposium, IEEE, 1990: 275-279.

DOI: 10.1109/arms.1990.67969

[11] Krivtsov V V. Practical extensions to NHPP application in repairable system reliability analysis[J]. Reliability Engineering & System Safety, 2007, 92(5): 560-562.

DOI: 10.1016/j.ress.2006.05.002

[12] Guo H, Mettas A, Sarakakis G, et al. Piecewise NHPP models with maximum likelihood estimation for repairable systems[C]/Reliability and Maintainability Symposium (RAMS), 2010 Proceedings-Annual. IEEE, 2010: 1-7.

DOI: 10.1109/rams.2010.5448029

[13] Dias De Oliveira M, Colosimo E A, Gilardoni G L. Power law selection model for repairable systems[J]. Communications in Statistics-Theory and Methods, 2013, 42(4): 570-578.

DOI: 10.1080/03610926.2011.609954

[14] Van Dyck J, Verdonck T. Precision of power-law NHPP estimates for multiple systems with known failure rate scaling[J]. Reliability Engineering & System Safety, 2014, 126: 143-152.

DOI: 10.1016/j.ress.2014.01.019

[15] Shrestha A, Xing L, Liu H. Modeling and evaluating the reliability of wireless sensor networks[C]. Reliability and Maintainability Symposium, 2007. RAMS'07. Annual. IEEE, 2007: 186-191.

DOI: 10.1109/rams.2007.328105

[16] B. Zhang, J.X. Zhang: Applied Stochastic Processes[M]. Tsinghua University Press. Beijing (2004).

[17] Ohba M. Software reliability analysis models[J]. IBM Journal of Research and Development, 1984, 28(4): 428-443.

DOI: 10.1147/rd.284.0428

[18] Yamada S, Osaki S, Narihisa H. A software reliability growth model with two types of errors[J]. RAIRO-Operations Research-Recherche Opérationnelle, 1985b, 19(1): 87-104.

DOI: 10.1051/ro/1985190100871