In this paper, we propose a state-problem correlation model to locate system faults in smart home services. First we exploit the parent-children tree-like structure of a smart home system's objects and deduce instinctively a coarse object state to problem correlation model, called "Symptom-Problem Correlation Model", that corresponds a set of symptoms to a particular problem. Then the proposed algorithm is applied to verify and reduce the size of this model so that the model can accurately identify the defined faults using minimum number of observed objects' states. The simplified model can be utilized for automatic fault location. Moreover, the Home Gateway and the supporting management system can be designed or set-up to monitor only key states of the underlying components according to the trained model to reduce operational load, provision cost, and enhance the overall system performance.
Smart Home System Fault Diagnosis Causality Graph Symptom-Problem Correlation Model Feature Symptoms