Fault Detection in Digraphs via Local Diagnosis Structures in Comparison Model.

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Bibliographic Details
Title:	Fault Detection in Digraphs via Local Diagnosis Structures in Comparison Model.
Authors:	ZHANG, XIAWEI¹, LV, YALI², CHENG, EDDIE³, LIPTÁK, LÁSZLÓ³, LIN, CHENG-KUAN⁴ cklin@nycu.edu.tw
Source:	Journal of Information Science & Engineering. May2026, Vol. 42 Issue 3, p599-614. 16p.
Subjects:	Directed graphs, Fault diagnosis, Algorithms, Simulation methods & models, Hypercube networks (Computer networks)
Abstract:	Local diagnosability is an effective approach to assess system diagnosability by evaluating each processor individually. According to the basic definition of diagnosis, the underlying topology of existing local comparison diagnosis models can be modeled as directed graphs with bidirectional edges, so it is difficult to apply them in classical digraphs. In this paper, we introduce a (p + q)-directed local diagnosis structure D(u, p, q) and present a local diagnosis algorithm for digraphs. This algorithm identifies faults or fault-free status of each processor under the comparison model. Our results indicate that any vertex in a digraph with the structure D (u, p,q) is locally (p + q)-diagnosable, with a time complexity of O(p + q). We apply the (p + q)-directed local diagnosis structure to unidirectional hypercubes, determining the local diagnosability for any vertex. Simulation results show that our algorithm maintains high performance even with a 40% fault probability for each vertex, achieving ACC ≥ 0.836, PPV ≥ 0.827, and NPV ≥ 0.816. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Local diagnosability is an effective approach to assess system diagnosability by evaluating each processor individually. According to the basic definition of diagnosis, the underlying topology of existing local comparison diagnosis models can be modeled as directed graphs with bidirectional edges, so it is difficult to apply them in classical digraphs. In this paper, we introduce a (p + q)-directed local diagnosis structure D(u, p, q) and present a local diagnosis algorithm for digraphs. This algorithm identifies faults or fault-free status of each processor under the comparison model. Our results indicate that any vertex in a digraph with the structure D (u, p,q) is locally (p + q)-diagnosable, with a time complexity of O(p + q). We apply the (p + q)-directed local diagnosis structure to unidirectional hypercubes, determining the local diagnosability for any vertex. Simulation results show that our algorithm maintains high performance even with a 40% fault probability for each vertex, achieving ACC ≥ 0.836, PPV ≥ 0.827, and NPV ≥ 0.816. [ABSTRACT FROM AUTHOR]
ISSN:	10162364
DOI:	10.6688/JISE.202605_42(3).0007