### Abstract

A network's diagnosability is the maximum number of faulty vertices the network can discriminate solely by performing mutual tests among the vertices. It is an important measure of a network's robustness. The original diagnosability without any condition is often rather low because it is bounded by the network's minimum degree. Several conditional diagnosability have been proposed in the past to increase the allowed faulty vertices, and hence enhancing the diagnosability of the network. The g-good-neighbor conditional diagnosability is the maximum number of faulty vertices a network can guarantee to identify, under the condition that every fault-free vertex has at least g fault-free neighbors (i.e., good neighbors). In this paper, we establish the g-good-neighbor conditional diagnosability for the (n,k)-arrangement graph network A_{{n,k}}. We will show that, under both the PMC model and the comparison model, the A_{{n,k}} 's g-good-neighbor conditional diagnosability is [(g+1)k-g](n-k) , which can be several times higher than the A_{{n,k}} 's original diagnosability.

Original language | English |
---|---|

Pages (from-to) | 542-548 |

Number of pages | 7 |

Journal | IEEE Transactions on Dependable and Secure Computing |

Volume | 15 |

Issue number | 3 |

DOIs | |

State | Published - 1 May 2018 |

### Keywords

- -good-neighbor conditional diagnosability
- Arrangement graph networks
- PMC model
- comparison model
- system-level diagnosis

### Cite this

*IEEE Transactions on Dependable and Secure Computing*,

*15*(3), 542-548. https://doi.org/10.1109/TDSC.2016.2593446

}

*IEEE Transactions on Dependable and Secure Computing*, vol. 15, no. 3, pp. 542-548. https://doi.org/10.1109/TDSC.2016.2593446

**The g-Good-Neighbor conditional diagnosability of arrangement graphs.** / Lin, Limei; Xu, Li; Wang, Dajin; Zhou, Shuming.

Research output: Contribution to journal › Article

TY - JOUR

T1 - The g-Good-Neighbor conditional diagnosability of arrangement graphs

AU - Lin, Limei

AU - Xu, Li

AU - Wang, Dajin

AU - Zhou, Shuming

PY - 2018/5/1

Y1 - 2018/5/1

N2 - A network's diagnosability is the maximum number of faulty vertices the network can discriminate solely by performing mutual tests among the vertices. It is an important measure of a network's robustness. The original diagnosability without any condition is often rather low because it is bounded by the network's minimum degree. Several conditional diagnosability have been proposed in the past to increase the allowed faulty vertices, and hence enhancing the diagnosability of the network. The g-good-neighbor conditional diagnosability is the maximum number of faulty vertices a network can guarantee to identify, under the condition that every fault-free vertex has at least g fault-free neighbors (i.e., good neighbors). In this paper, we establish the g-good-neighbor conditional diagnosability for the (n,k)-arrangement graph network A{n,k}. We will show that, under both the PMC model and the comparison model, the A{n,k} 's g-good-neighbor conditional diagnosability is [(g+1)k-g](n-k) , which can be several times higher than the A{n,k} 's original diagnosability.

AB - A network's diagnosability is the maximum number of faulty vertices the network can discriminate solely by performing mutual tests among the vertices. It is an important measure of a network's robustness. The original diagnosability without any condition is often rather low because it is bounded by the network's minimum degree. Several conditional diagnosability have been proposed in the past to increase the allowed faulty vertices, and hence enhancing the diagnosability of the network. The g-good-neighbor conditional diagnosability is the maximum number of faulty vertices a network can guarantee to identify, under the condition that every fault-free vertex has at least g fault-free neighbors (i.e., good neighbors). In this paper, we establish the g-good-neighbor conditional diagnosability for the (n,k)-arrangement graph network A{n,k}. We will show that, under both the PMC model and the comparison model, the A{n,k} 's g-good-neighbor conditional diagnosability is [(g+1)k-g](n-k) , which can be several times higher than the A{n,k} 's original diagnosability.

KW - -good-neighbor conditional diagnosability

KW - Arrangement graph networks

KW - PMC model

KW - comparison model

KW - system-level diagnosis

UR - http://www.scopus.com/inward/record.url?scp=85047191562&partnerID=8YFLogxK

U2 - 10.1109/TDSC.2016.2593446

DO - 10.1109/TDSC.2016.2593446

M3 - Article

AN - SCOPUS:85047191562

VL - 15

SP - 542

EP - 548

JO - IEEE Transactions on Dependable and Secure Computing

JF - IEEE Transactions on Dependable and Secure Computing

SN - 1545-5971

IS - 3

ER -