Performance Modeling of Stochastic Diagnosis Engines

Critical systems are complex, consisting of thousands of components, which can fail at any time. Diagnosing these systems within a certain time is highly desirable. Traditional diagnosis algorithms are mostly deterministic, able to find single faults extremely fast and double faults reasonably quick as well. However, these algorithms fail to...

Approximation Algorithms for Model-Based Diagnosis

Model-based diagnosis is an area of abductive inference that uses a system model, together with observations about system behavior, to isolate sets of faulty components (diagnoses) that explain the observed behavior, according to some minimality criterion. This thesis presents greedy approximation algorithms for three problems closely related to...

Evaluating model-based diagnosis for wireless sensor networks

Model-based diagnosis is a technique where a model of a system is combined with observations from that system, to generate diagnoses for failures of the system. This thesis looks at how model-based diagnosis can be applied to wireless sensor networks (WSNs). WSNs are ad-hoc wireless networks of small form-factor, embedded nodes with limited...