A Complete Fault Tolerant Method for Extra Fault Diagnosability of Alternating Group Graphs

Limei Lin, Yanze Huang, Li Xu, Sun Yuan Hsieh

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


A network's diagnosability is the maximum number of faulty vertices that the network can discriminate solely by performing mutual tests among vertices. The original diagnosability without any condition is often rather low because it is bounded by the network's minimum degree. The h-extra fault diagnosability is an important and widely accepted diagnostic strategy as a new measure of diagnosability, which guarantees that the scale of every component is at least h+1 in the remaining system. Moreover, it increases the allowed faulty vertices, hence enhancing the diagnosability of the network. There have been lots of state-of-the-art literatures concerning the h-extra fault diagnosability. Although there are some methods to theoretically prove the extra fault diagnosability of some other well-known networks under MM∗ model, these methods have some serious flaws when there exists a 4-cycle in these networks. In this article, we investigate the reason that caused the flawed results in some references, and we derive a different, broadly applicable, and complete fault tolerant method to establish the extra fault diagnosability in an n-dimensional alternating group graph AG_n under MM∗ model. The complete fault tolerant method adopts combinatorial properties and linearly many fault analysis to conquer the core of our proofs. Moreover, we compare the extra fault diagnosability of AG_n with various types of fault diagnosability, including the diagnosability, strong diagnosability, conditional diagnosability, t/k-diagnosability, and pessimistic diagnosability. It can be seen that the extra fault diagnosability is greater than all the other types of fault diagnosability.

Original languageEnglish
Article number9199414
Pages (from-to)957-969
Number of pages13
JournalIEEE Transactions on Reliability
Issue number3
Publication statusPublished - 2021 Sept

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Electrical and Electronic Engineering


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