The G-Good-Neighbor Conditional Diagnosability of Arrangement Graphs
Document Type
Article
Publication Date
5-1-2018
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.
DOI
10.1109/TDSC.2016.2593446
Montclair State University Digital Commons Citation
Lin, Limei; Xu, Li; Wang, Dajin; and Zhou, Shuming, "The G-Good-Neighbor Conditional Diagnosability of Arrangement Graphs" (2018). Department of Computer Science Faculty Scholarship and Creative Works. 577.
https://digitalcommons.montclair.edu/compusci-facpubs/577