Distributed diagnosis algorithms for regular interconnected structures

Somani, Arun K.; Agarwal, V.K.

doi:10.1109/12.256447

Cited by 39 publications

(11 citation statements)

References 15 publications

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“…As pointed out before, all the nodes in the faulty set located by the precise diagnosis strategy are faulty; while the faulty set located by the pessimistic diagnosis strategy may contain at most one fault-free node. To increase the degree of diagnosability of a multiprocessor system, Somani et al proposed the t=k-diagnosis strategy [19][20][21]. Under this strategy, the tested faulty set may contain at most k fault-free nodes (k P 0).…”

Section: Discussionmentioning

confidence: 98%

Diagnosable evaluation of DCC linear congruential graphs under the PMC diagnostic model

Fan

Yang

Zhou

et al. 2009

Information Sciences

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Section: Discussionmentioning

confidence: 98%

Diagnosable evaluation of DCC linear congruential graphs under the PMC diagnostic model

Fan

Yang

Zhou

et al. 2009

Information Sciences

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“…When this approach is applied to regular systems, the computation time of fault diagnosis can be significantly reduced. In addition, Somani and Agarwal [30] developed a distributed diagnosis algorithm for regular systems based on the concept of local diagnosis. Later, Altmann et al [2] addressed an event-driven distributed approach to multiprocessor diagnosis, and Masuyama and Miyoshi [24] presented a nonadaptive distributed system-level diagnosis method for computer networks.…”

Section: The State-of-the-artmentioning

confidence: 99%

“…Under the PMC diagnosis model, Dahbura and Masson [12] proposed a polynomial-time algorithm with time complexity OðN 2:5 Þ to identify all the faulty processors in a system with N processors. In this paper, we present a novel method to diagnose a conditionally faulty system by applying the concept behind the local diagnosis, introduced by Somani and Agarwal [30], and formalized by Hsu and Tan [18]. The goal of local diagnosis is to identify the fault status of any single processor correctly.…”

mentioning

confidence: 99%

Conditional-Fault Diagnosability of Multiprocessor Systems with an Efficient Local Diagnosis Algorithm under the PMC Model

Lin

Kung

Tan

2011

IEEE Trans. Parallel Distrib. Syst.

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Diagnosis is an essential subject for the reliability of multiprocessor systems. Under the PMC diagnosis model, Dahbura and Masson [12] proposed a polynomial-time algorithm with time complexity OðN 2:5 Þ to identify all the faulty processors in a system with N processors. In this paper, we present a novel method to diagnose a conditionally faulty system by applying the concept behind the local diagnosis, introduced by Somani and Agarwal [30], and formalized by Hsu and Tan [18]. The goal of local diagnosis is to identify the fault status of any single processor correctly. Under the PMC diagnosis model, we give a sufficient condition to estimate the local diagnosability of a given processor. Furthermore, we propose a helpful structure, called the augmenting star, to efficiently determine the fault status of each processor. For an N-processor system in which every processor has an Oðlog NÞ degree, the time complexity of our algorithm to diagnose any given processor is Oððlog NÞ 2 Þ, provided that each processor can construct an augmenting star structure of full order in time Oððlog NÞ 2 Þ and the time for a processor to test another one is constant. Therefore, the time totals to OðNðlog NÞ 2 Þ for diagnosing the whole system.

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“…Here the expression c ij = 1 means node i and node j are possibly in different states. Next, the tendency status (possibly a faulty LF or possibly a good LG ) is determined according to following formula [14]:

T_{i} = {\begin{matrix} L F & i f ∑_{j \in N_{i}} c_{i j} \geq ⌈ | N_{i} | / 2 ⌉ \\ L G & otherwise \end{matrix}

where ⌈| N i |⌉ is the number of one-hop neighbors of node i . The formula states that a sensor is deemed to be possibly good only if there are less than ⌈| N i |/2⌉ neighbors whose test results are 1.…”

Section: Uncertainty-based Fault Detection Mechanismmentioning

confidence: 99%

An Uncertainty-Based Distributed Fault Detection Mechanism for Wireless Sensor Networks

Yang

Gao

Zhou

et al. 2014

Sensors

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Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio.

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Distributed diagnosis algorithms for regular interconnected structures

Cited by 39 publications

References 15 publications

Diagnosable evaluation of DCC linear congruential graphs under the PMC diagnostic model

Diagnosable evaluation of DCC linear congruential graphs under the PMC diagnostic model

Conditional-Fault Diagnosability of Multiprocessor Systems with an Efficient Local Diagnosis Algorithm under the PMC Model

An Uncertainty-Based Distributed Fault Detection Mechanism for Wireless Sensor Networks

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