Combinatorial analysis of body sensor networks subject to probabilistic competing failures

Wang, Yujie; Xing, Liudong; Wang, Honggang; Levitin, Gregory

doi:10.1016/j.ress.2015.06.005

Cited by 43 publications

(28 citation statements)

References 30 publications

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“…In Wang et al, the methodology developed in Xing et al was extended to address the probabilistic competing failures considered in this work. However, like the other existing works on deterministic competing failures, the work of Wang et al assumed that propagated failures can propagate instantaneously. In practice, however, this is often not true for real‐world systems (eg, attacks, corrosions take time to propagate their effects) .…”

Section: Related Workmentioning

confidence: 99%

“…A major challenge to reliability modeling of the communication layer is to address probabilistic competing failure effects and random failure propagation time. Specifically, to realize long distance transmissions, relay nodes, which transmit signals gathered from the related sensors to the sink node, are often deployed in the communication layer . When a relay fails, its dependent sensors may increase their transmission power to enable a direct transmission to the sink node with a certain probability related to the level of remaining power at each sensor; if a sensor's remaining power is not sufficient for supporting a direct link to the sink, the sensor becomes isolated.…”

Section: Introductionmentioning

confidence: 99%

“…When a relay fails, its dependent sensors may increase their transmission power to enable a direct transmission to the sink node with a certain probability related to the level of remaining power at each sensor; if a sensor's remaining power is not sufficient for supporting a direct link to the sink, the sensor becomes isolated. Such behavior between relays and sensors is referred to as probabilistic function dependence . On one hand, if a sensor experiences a propagated failure (eg, from jamming attacks, which are launched by continually transmitting interference signals from a compromised component to the sink device so as to block legitimate communications) that spreads out to the entire system before the relay failure, the whole communication layer is affects and thus the smart home system will have an outage.…”

Section: Introductionmentioning

confidence: 99%

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A hierarchical combinatorial reliability model for smart home systems

Zhao

Xing

Zhang

et al. 2017

Quality & Reliability Eng

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As an application of the Internet of Things, smart home systems have received significant attentions in recent years due to their precedent advantages, eg, in ensuring efficient electricity transmission and integration with renewable energy. This paper proposes a hierarchical and combinatorial methodology for modeling and evaluating reliability of a smart home system. Particularly, the proposed methodology encompasses a multi-valued decision diagram-based method for addressing phased-mission, standby sparing, and functional dependence behaviors in the physical layer; and a combinatorial procedure based on the total probability theorem for addressing probabilistic competing failure behavior with random propagation time in the communication layer. The methods are applicable to arbitrary types of time-to-failure and time-to-propagation distributions for system components. A detailed case study of an example smart home system is performed to demonstrate applications of the proposed method and effects of different component parameters on the system reliability. (IoT) is an internetworking of myriad of "smart" objects and devices augmented with sensors and actuators (such as cell phones, smart appliances, electrical devices, etc.). [1][2][3] In other words, an IoT smart system incorporates sensing, actuation, and control functions for monitoring and analyzing a situation, and Abbreviations: EMS, Energy management system; FCE, Failure competition event; FDEP, Functional dependence; HSP, Hot spare; IFP, Isolation factor pair; IoT, Internet of Things; LF, Local failure; LS, Local sensing failure; LT, Local transmitting failure; MDD, Multi-valued decision diagram; PDEP, Probabilistic function dependence; PDF, Probability density function; PFDC, Probabilistic function dependence case; PFGE, Propagated failure with global effect; PMS, Phased mission system; PT, Propagation time. Notations: h, Phase number; f iP (t), f iLS (t), f iLT (t), PDF of time-to-PFGE/LS/LT of component i; f iPT (t), PDF of time-to-propagation of component i; FCE j , Failure competition event j, j = 1,2,3; ψ i , The failure function of the i-th phase in the physical layer; ψ xi;h , Unreliability of component i at the end of h; I, Union of set of components causing PFGE and set of components being isolated under an FCE; P FCEj t ð Þ, Probability of no PFGE to components in I; q iLS (t), q iLT (t), Occurrence probability of LS, LT to component i; q iP (t), Occurrence probability of PFGE to component i; q iS (t), q iT (t), Occurrence probability of LS, LT given that no PFGE to i; Q FCEj t ð Þ, Failure probability given that no PFGE happens to components in I; T h , Duration of phase h; x i = 0, Component i functions in all the phases; x i = h, Component i fails during h given that i is operational at the beginning of h; X iP , X iLS , X iLT , Event that component i has a PFGE, LS, LT; X iPT , Event that the propagation time is less than the time difference between the relay LT and the component i PFGE; U comm (t), Unreliability of the communicat...

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hierarchical combinatorial reliability model for smart home systems

Zhao

Xing

Zhang

et al. 2017

Quality & Reliability Eng

Self Cite

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“…If the router malfunctions, the computers connected to it become inaccessible with certainty . As an example of the probabilistic failure isolation (PFI), sensors in a wireless sensor network communicate their sensed information with the base station through a relay node . If the relay malfunctions, the sensors are not necessarily isolated; each sensor may increase its transmitting power to enable a direct transmission to the base station without going through the relay, with probability being dependent on the remaining battery power of the sensor.…”

Section: Introductionmentioning

confidence: 99%

“…For example, different combinatorial methods have been suggested to address the competing failures in the reliability analysis of different types of function‐dependent systems, extending from binary‐state to multistate systems, from single‐phase to multiphase systems, from single LF type to multiple LF types, from PFs with global effects to PFs with selective effects, from disjoint LF‐PF to dependent or independent LF‐PF relationship, and from single FDEP group to multiple FDEP groups . However, these existing models have one or multiple of the following restrictive assumptions, including PFs have zero or negligible propagation time (PT), competitions of multiple FDEP groups are uncorrelated, and the failure isolation effect must be deterministic . This paper relaxes those assumptions by proposing a reliability model for systems undergoing correlated competitions between failure propagation and PFI effects, and having random failure PT for dependent components.…”

Section: Introductionmentioning

confidence: 99%

Reliability modeling of correlated competitions and dependent components with random failure propagation time

Xing

Zhao

Wang

et al. 2020

Quality & Reliability Eng

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Function dependence takes place in systems where the malfunction of certain trigger component(s) causes other system components (referred to as dependent components) to become unusable or inaccessible. Systems undergoing function dependence often exhibit diverse statuses due to competitions in the time domain between propagated failure from a dependent component and local failure of the corresponding trigger component. If the former wins (ie, occurring first), a failure propagation effect is induced, crashing the entire system. If the latter wins, a failure isolation effect may be induced quarantining the damage from the propagated failure (the isolation effect can occur in a deterministic or probabilistic manner depending on applications). Existing models addressing such competitions have restrictive assumptions such as uncorrelated competitions from multiple function dependence groups, zero or negligible failure propagation time, and deterministic failure isolation effect. This paper advances the state of the art by proposing a combinatorial reliability model for systems undergoing correlated, probabilistic competitions and random failure propagation time for dependent components. A case study of a wireless body area network system for patient monitoring is performed to illustrate the proposed methodology and effects of different model parameters on the system reliability.

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Dynamic System Reliability

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Combinatorial analysis of body sensor networks subject to probabilistic competing failures

Cited by 43 publications

References 30 publications

A hierarchical combinatorial reliability model for smart home systems

A hierarchical combinatorial reliability model for smart home systems

Reliability modeling of correlated competitions and dependent components with random failure propagation time

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