The Diagnosability of Wheel Networks with Missing Edges under the Comparison Model
Wei Feng, Shiying Wang · Mathematics · 2020
The diagnosability is an essential subject for the reliability of a multiple CPU system. As a celebrated topology structure of interconnection networks, an n-dimensional wheel network CWn has numerous great features. In this paper, we discuss the diagnosability of CWn with missing edges under the comparison model. Both the local diagnosability and the strong local diagnosability feature are studied; this feature depicts the equivalence of the local diagnosability of a node and its degree. We demonstrate that CWn(n≥6) possesses this feature, containing the strong feature even with up to 2n−4 missing edges in it, and the outcome is ideal regarding the amount of missing edges.