Dynamic hybrid fault models and the applications to wireless sensor networks (WSNs)

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Dynamic hybrid fault models and the applications to wireless sensor networks (WSNs)

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International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems archive
Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems table of contents

Vancouver, British Columbia, Canada

SESSION: Wireless sensor networks table of contents

Pages 100-108

Year of Publication: 2008

ISBN:978-1-60558-235-1

Authors

Zhanshan (Sam) Ma	University of Idaho, Moscow, ID, USA
Axel W. Krings	University of Idaho, Moscow, ID, USA

Sponsors

ACM: Association for Computing Machinery
SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

ACM New York, NY, USA

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ABSTRACT

In this paper, we introduce a new concept termed dynamic hybrid fault models together with the mathematic models and approaches for applying the new concept to reliability and fault tolerance analyses. It extends the traditional hybrid fault models and their relevant constraints in agreement algorithms with survival analysis and evolutionary game theory. The new dynamic hybrid fault models (i) transform hybrid fault models into time and covariate dependent models; (ii) make real-time prediction of reliability more realistic and allows for real-time prediction of fault-tolerance; (iii) set the foundations for integrating hybrid fault models with reliability and survivability analyses by introducing evolutionary game modeling; (iv) extend evolutionary game theory in its modeling of the survivals of game players. The application domain is wireless sensor network (WSN), but the large part of the modeling architecture also applies to general engineering reliability and network survivability.

REFERENCES

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	1	M. H. Azadmanesh , R. M. Kieckhafer, Exploiting Omissive Faults in Synchronous Approximate Agreement, IEEE Transactions on Computers, v.49 n.10, p.1031-1042, October 2000 [doi>10.1109/12.888039]
	2	Cox, D. R. 1972. Regression models and life tables. J. R. Stat. Soc. Ser. B. 34:184--220.
	3	Hofbauer, J. and K. Sigmund. 1998. Evolutionary Games and Population Dynamics. Cambridge University Press. 323pp.
	4	Kalbfleisch, J. D., and R. L. Prentice, 2002. The Statistical Analysis of Failure Time Data. Wiley-InterScience, 2nd ed. 462pp.
	5	Klein, J. P. and M. L. Moeschberger. 2003. Survival analysis techniques for censored and truncated data. Springer.
	6	Kot, M. 2001. Elements of Mathematical Ecology. Cambridge University Press. 453pp.
	7	Krings, A. W. and Z. S. Ma. 2006. Fault-Models in Wireless Communication: Towards Survivable Ad Hoc Networks, MILCOM 2006, Military Communications Conference, 23-25 October, 2006. 7 pages.
	8	Leslie Lamport , Robert Shostak , Marshall Pease, The Byzantine Generals Problem, ACM Transactions on Programming Languages and Systems (TOPLAS), v.4 n.3, p.382-401, July 1982 [doi>10.1145/357172.357176]
	9	Lawless, J. F. 2003. Statistical models and methods for lifetime data. John Wiley & Sons. 2nd ed. 630pp.
	10	Ma, Z. S. and E. J. Bechinski. 2008. A Survival-Analysis based Simulation Model for Russian Wheat Aphid Population Dynamics. Ecological Modeling 2008, 216(2):323--332
	11	Ma, Z. S. and A. W. Krings. 2008a. Survival Analysis Approach to Reliability, Survivability and Prognostics and Health Management. Proceedings of 2008 Proc. IEEE-AIAA AeroSpace Conf. BigSky, Montana, March 1-8, 2008. 20pages.
	12	Ma, Z. S. and A. W. Krings. 2008b. Competing Risks Analysis of Reliability, Survivability, and Prognostics and Health Management (PHM), Proc. IEEE-AIAA AeroSpace Conference, March 1-8, Big Sky, MT, 2008. 20pages.
	13	Ma, Z. S. and A. W. Krings. 2008c. Multivariate Survival Analysis (I): Shared Frailty Approaches to Reliability and Dependence Modeling", Proc. IEEE-AIAA AeroSpace Conference, March 1-8, Big Sky, MT, 2008. 21pages.
	14	Ma, Z. S. and A. W. Krings. 2008d. Bio-Robustness and Fault Tolerance: A New Perspective on Reliable, Survivable and Evolvable Network Systems, Proc. IEEE-AIAA AeroSpace Conference, March 1-8, Big Sky, MT, 2008. 20 pages.
	15	Ma, Z. S. 2008. New Approaches to Reliability and Survivability with Survival Analysis, Dynamic Hybrid Fault Models and Evolutionary Game Theory. Ph.D. Dissertation, University of Idaho.
	16	Martinussen, T. and T. H. Scheike. 2006. Dynamic Regression Models for Survival Data. Springer. 466pp.
	17	Meyer, F. J. and D. K. Pradhan, 1987. Consensus with dual failure modes," Tech. Rep., Dep. Elec. Comput. Eng., Univ. Massachusetts, Amherst, MA, Apr. 1987.
	18	M. Pease , R. Shostak , L. Lamport, Reaching Agreement in the Presence of Faults, Journal of the ACM (JACM), v.27 n.2, p.228-234, April 1980 [doi>10.1145/322186.322188]
	19	Thambidurai, P. and Y.-K. Park, 1988. Interactive Consistency with Multiple Failure Modes, Proc. 7th Symp. on Reliable Distributed Systems, Columbus, OH, pp. 93--100, Oct. 1988.
	20	Therneau, T. and P. Grambsch. 2000. Modeling Survival Data: Extending the Cox Model. Springer.
	21	Vincent, T. L. and J. L. Brown. 2005. Evolutionary Game Theory, Natural Selection and Darwinian Dynamics. Cambridge University Press. 382pp.