Conditional diagnosability of hypercubes under the comparison diagnosis model

Processor fault diagnosis plays an important role in multiprocessor systems for reliable computing, and the diagnosability of many well-known networks has been explored. Lai et al. proposed a novel measure of diagnosability, called conditional diagnosability, by adding an additional condition that any faulty set cannot contain all the neighbors of any vertex in a system. We make a contribution to the evaluation of diagnosability for hypercube networks under the comparison model and prove that the conditional diagnosability of n-dimensional Hypercube Qn is 3(n − 2) + 1 for n ⩾ 5. The conditional diagnosability of Qn is about three times larger than the classical diagnosability of Qn.