期刊
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
卷 15, 期 3, 页码 542-548出版社
IEEE COMPUTER SOC
DOI: 10.1109/TDSC.2016.2593446
关键词
Arrangement graph networks; comparison model; g-good-neighbor conditional diagnosability; PMC model; system-level diagnosis
类别
资金
- National Natural Science Foundation of China [61072080, U1405255, 61572010]
- Natural Science Foundation of Fujian Province [2013J01221, 2013J01222]
- Fujian Normal University Innovative Research Team [IRTL1207]
A networks 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 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.
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