Z-Number-Based Reliability-Aware Reasoning with Positive and Negative Evidence for Localizing Network Degradation Sources

Localizing the source of degradation in a communication network is difficult when symptoms come from sources of unequal reliability, are partly missing and are mixed with false alarms. We propose a reasoning model in which every piece of evidence is specified as a Z-number of strength and reliability. Observed symptoms support the candidates that can explain them through the topology and through temporal Z-rules evaluated automatically from time stamps; unexplained alarms and expected but unobserved symptoms act as negative evidence; all evidence is discounted by the reliability of its source. In 47 simulated configurations, the model identified the true source in 85.9% of incidents in the default setting, against 82.9% for Dempster–Shafer fusion with the same negative evidence and 73.4% for codebook correlation, and it was the most accurate method in most stress tests, but not with correlated false-alarm bursts or upstream symptoms. On an independent generator of telecom alarm storms on three real backbones, it was within four points of the evidential baseline and about 10–12 points more accurate than the codebook. A crisp version was equally accurate; we show why the benefit lies in the structure and linguistic specification of the evidence rather than in fuzzy arithmetic.