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Conditional recursive match networks (CRMNs) are a new class of recursive networks, which contain not only the well-known BCube and hypercube, but also other future networks beyond BCube and the hypercube. Fault diagnosis is an essential subject for the reliability of networks. In 2007, Kung et al. proposed a diagnosis algorithm for wireless sensor networks under the PMC model. Under the precise diagnosis strategy, the g-extra conditional diagnosability can greatly enhance the diagnosis capability of networks. In this paper, we mainly design an O(N) g-extra diagnosis algorithm for CRMNs under the PMC model, named EX-Diagnosis \(_{X_{n}}\) , based on CRMNs’ g-extra conditional diagnosability, where N is the number of edges in the CRMN. The proposed diagnosis algorithm can be directly applied to diagnosing the state of processors/servers in any network that satisfies the definition of CRMNs, which encompasses the hypercube and BCube. Theoretical verification and simulation experiment results have demonstrated that the EX-Diagnosis \(_{X_{n}}\) algorithm can accurately diagnose the state of all processors/servers in the CRMN.

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An Extra Diagnosis Algorithm for Conditional Recursive Match Networks under the PMC Model

  • 会议论文

  • 发表日期：

    2025年

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  • Qianru Zhou,
  • Yan Wang,
  • Baolei Cheng,
  • Guijuan Wang,
  • Jianxi Fan

摘要

Conditional recursive match networks (CRMNs) are a new class of recursive networks, which contain not only the well-known BCube and hypercube, but also other future networks beyond BCube and the hypercube. Fault diagnosis is an essential subject for the reliability of networks. In 2007, Kung et al. proposed a diagnosis algorithm for wireless sensor networks under the PMC model. Under the precise diagnosis strategy, the g-extra conditional diagnosability can greatly enhance the diagnosis capability of networks. In this paper, we mainly design an O(N) g-extra diagnosis algorithm for CRMNs under the PMC model, named EX-Diagnosis \(_{X_{n}}\) , based on CRMNs’ g-extra conditional diagnosability, where N is the number of edges in the CRMN. The proposed diagnosis algorithm can be directly applied to diagnosing the state of processors/servers in any network that satisfies the definition of CRMNs, which encompasses the hypercube and BCube. Theoretical verification and simulation experiment results have demonstrated that the EX-Diagnosis \(_{X_{n}}\) algorithm can accurately diagnose the state of all processors/servers in the CRMN.