A Survey for Conditional Diagnosability of Alternating Group Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Fault diagnosis of processors has played an essential role when evaluating the reliability of multiprocessor systems. In many novel multiprocessor systems, their diagnosability has been extensively explored. Conditional diagnosability is a useful measure for evaluating diagnosability by adding a further condition that all neighbors of every node in the system do not fail at the same time. In this paper, we study the conditional diagnosability of n-dimensional alternating group networks under the PMC model, and obtain the results, and. In addition, for the isomorphism property between with, namely star graphs, the above results can be extended to, and we have and for. It is worth noting that the conditional diagnosability is about six times the degree of and, which is very different from general networks with a multiple of four.

Original languageEnglish
Title of host publicationComputing and Combinatorics - 26th International Conference, COCOON 2020, Proceedings
EditorsDonghyun Kim, R.N. Uma, Zhipeng Cai, Dong Hoon Lee
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030581497
Publication statusPublished - 2020
Event26th International Conference on Computing and Combinatorics, COCOON 2020 - Atlanta, United States
Duration: 2020 Aug 292020 Aug 31

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12273 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th International Conference on Computing and Combinatorics, COCOON 2020
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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