This article extends the finite Markov chain imbedding approach to evaluate the system reliability of a dynamic k-out-of-n:F system operating under two cyclic alternating conditions, and this article presents a method to obtain the optimal replacement interval of the system according to age-based replacement policy. In terms of the “dynamic k-out-of-n:F” model, we regard the system as two distinct k-out-of-n:F systems with different values of k while the system is operating under each condition. We apply the proposed model to analyze the reliability of a wireless sensor network, whose sensors switching into sleep state to save energy and shifting into listen state to detect the information alternatively. We also obtain the optimal replacement interval to redeploy the wireless sensor networks.
HawkesAGCuiLRZhengZH.
Modeling the evolution of system reliability performance under alternative environments. IIE Trans2011; 43(11): 761–772.
2.
ChungWK.
An availability calculation for k-out-of-n redundant system with common-cause failures and replacement. Microelectron Reliab1980; 20(4): 517–519.
3.
BaiDSYunWYChungSW.
Redundancy optimization of k-out-of-n systems with common-cause failures. IEEE T Reliab1991; 40(1): 56–59.
4.
SheJPechtMG.
Reliability of a k-out-of-n warm-standby system. IEEE T Reliab1992; 41(1): 72–75.
5.
KoutrasMV.
On a Markov chain approach for the study of reliability structures. J Appl Probab1996; 33(2): 357–367.
6.
TianZZuoMJYamRC.
Multi-state k-out-of-n systems and their performance evaluation. IIE Trans2008; 41(1): 32–44.
7.
HuangJZuoMJWuY.
Generalized multi-state k-out-of-n: G systems. IEEE T Reliab2000; 49(1): 105–111.
8.
ZuoMJTianZ.
Performance evaluation of generalized multi-state k-out-of-n systems. IEEE T Reliab2006; 55(2): 319–327.
9.
EryilmazSSarikayaK.
Modeling and analysis of weighted-k-out-of-n: G system consisting of two different types of components. Proc IMechE, Part O: J Risk and Reliability, 2014; 228(3): 265–271.
10.
WuJSChenRJ.
An algorithm for computing the reliability of weighted-k-out-of-n systems. IEEE T Reliab1994; 43(2): 327–328.
11.
WuJ-SChenR-J.
Efficient algorithms for k-out-of-n and consecutive weighted-k-out-of-n:F system. Microelectron Reliab1996; 36(4): 537.
12.
ChenYYangQ.
Reliability of two-stage weighted-k-out-of-n systems with components in common. IEEE T Reliab2005; 54(3): 431–440.
13.
CoitDWChatwattanasiriNWattanapongsakornN.
Dynamic k-out-of-n system reliability with component partnership. Reliab Eng Syst Safe2015; 138: 82–92.
14.
EryilmazSXieM.
Dynamic modeling of general three-state k-out-of-n: G systems: permanent-based computational results. J Comput Appl Math2014; 272: 97–106.
15.
EryilmazS.
Mixture representations for three-state systems with three-state components. IEEE T Reliab2015; 64(2): 829–834.
16.
LimTJ.
A stochastic regime switching model for the failure process of a repairable system. Reliab Eng Syst Safe1998; 59(2): 225–238.
17.
RavishankerNLiuZRayBK.
NHPP models with Markov switching for software reliability. Comput Stat Data An2008; 52(8): 3988–3999.
18.
SangwanASinghRP.
Survey on coverage problems in wireless sensor networks. Wireless Pers Commun2015; 80(4): 1475–1500.
19.
SelavoLWoodACaoQ. LUSTER: wireless sensor network for environmental research. In: Proceedings of the 5th international conference on embedded networked sensor systems, Sydney, NSW, Australia, 6–7 November 2007, pp.103–116. New York: ACM.
20.
StankovicJAWoodADHeT.
Realistic applications for wireless sensor networks. In: NikoletseasSRolimJDP (eds) Theoretical aspects of distributed computing in sensor networks. Berlin, Heidelberg: Springer2011, pp.835–864.
21.
WoodADStankovicJVironeG.
Context-aware wireless sensor networks for assisted living and residential monitoring. IEEE Network2008; 22(4): 26–33.
22.
HeTKrishnamurthySLuoL.
VigilNet: an integrated sensor network system for energy-efficient surveillance. ACM T Sensor Network2006; 2(1): 1–38.
23.
VicairePHeTCaoQ.
Achieving long-term surveillance in VigilNet. ACM T Sensor Network2009; 5(1): 9:1–39.
24.
YeWHeidemannJEstrinD.
Medium access control with coordinated adaptive sleeping for wireless sensor networks. IEEE ACM T Network2004; 12(3): 493–506.
25.
WangJBWangJYChenM. Reliability analysis for a data flow in event-driven wireless sensor networks using a multiple sending transmission approach. EURASIP J Wirel Comm2013; 2013(1): 1–11.
26.
BruneoDPuliafitoAScarpaM.
Energy control in dependable wireless sensor networks: a modeling perspective. Proc IMechE, Part O: J Risk and Reliability2011; 225(O4): 424–434.
27.
FuJCKoutrasMV.
Distribution theory of runs: a Markov chain approach. J Am Stat Assoc1994; 89(427): 1050–1058.
28.
ChangGJCuiLRHwangFK. Reliabilities for (n, f, k) systems. Stat Probab Lett1999; 43(3): 237–242.
29.
CuiLRKuoWLiJL. On the dual reliability systems of (n, f, k) and <n, f, k>. Stat Probab Lett2006; 76(11): 1081–1088.
30.
ZhaoXCuiLR.
Reliability evaluation of generalized multi-state k-out-of-n systems based on FMCI approach. Int J Syst Sci2010; 41(12): 1437–1443.
31.
ChangYMHuangTH.
Reliability of a 2-dimensional k-within consecutive-r×s-out-of-m×n: F system using finite Markov chains. IEEE T Reliab2010; 59(4): 725–733.
32.
CuiLRLinCDuSJ.
m-consecutive-k, l-out-of-n. IEEE T Reliab2015; 64(1): 386–393.
33.
LinCCuiLRCoitDW. Reliability modeling on consecutive-kr-out-of-nr: F linear zigzag structure and circular polygon structure. IEEE T Reliab2016; 65(3): 1509–1521.
34.
AmbuhlSMarquisLKofoedJP. Operation and maintenance strategies for wave energy converters. Proc IMechE, Part O: J Risk and Reliability2015; 229(5): 417–441.
35.
TingaTJanssenR.
The interplay between deployment and optimal maintenance interval for complex multi-component systems. Proc IMechE, Part O: J Risk and Reliability2013; 227(3): 227–240.
36.
SongSLCoitDWFengQM. Reliability analysis for multi-component systems subject to multiple dependent competing failure processes. IEEE T Reliab2014; 63(1): 331–345.
