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Structural diagnosability of hypercubes under the PMC and MM* models.

Authors :
Li, Ping
Zhang, Shurong
Hu, Xiaomin
Yang, Weihua
Source :
Theoretical Computer Science. Nov2023, Vol. 980, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

The fault diagnosability has played an important role in the reliability of the interconnection network. In a network, the states of any two adjacent vertices can usually affect each other, and the neighbor of a faulty vertex is more likely to become faulty. These motivate our study of fault diagnosability from the perspective of some structures instead of basing on individual faulty vertices. Therefore, we introduce a novel measure of diagnosability, called structural diagnosability. Given a specific structure H , the H -structure diagnosability of a network G , denoted by t s (G ; H) , is the maximum number of pairwise disjoint subnetworks H 1 , H 2 , ... , H m in G , such that, for i = 1 , 2 , ... , m , H i is isomorphic to H and when all vertices in H i are faulty, these vertices can be diagnosed correctly. In this paper, we will establish t s (Q n ; H) for the n -dimensional hypercube Q n under the PMC model and MM* model, respectively, where H ∈ { K 1 , 1 , K 1 , 2 , K 1 , 3 , C 4 }. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
*MOTIVATION (Psychology)

Details

Language :
English
ISSN :
03043975
Volume :
980
Database :
Academic Search Index
Journal :
Theoretical Computer Science
Publication Type :
Academic Journal
Accession number :
173434778
Full Text :
https://doi.org/10.1016/j.tcs.2023.114231