Toward the fault identification method for diagnosing strongly t-diagnosable systems under the PMC model
by Tzu-Liang Kung; Hsing-Chung Chen
International Journal of Communication Networks and Distributed Systems (IJCNDS), Vol. 15, No. 4, 2015

Abstract: System-level diagnosis is a crucial subject for maintaining the reliability of interconnected systems. Based on the classical notion of one-step diagnosability, strong and conditional diagnosabilities are proposed to reflect a systems' self-diagnostic capability under more realistic assumptions. Zhu et al. (2014) studied the strong networks, which are n-regular and (n - 1)-connected, and in which any two nodes share at most n - 3 common neighbours, and then they proved that a t-regular strong network is strongly t-diagnosable if and only if its conditional diagnosability is greater than t. In this paper, a fault identification algorithm is proposed to diagnose strongly t-diagnosable strong networks under the PMC model.

Online publication date: Mon, 12-Oct-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Communication Networks and Distributed Systems (IJCNDS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com