Abslrcrct-On-line fault detection in sensor networks is of paramount importance due to the convergence of a variety of challenging technological, application, conceptual, and safety related factors. We introduce a taxonomy for classifcation of faults in sensor networks and the Erst on-line model-based testing technique. The approach is generic in the sense that it can be applied on an arbitrary system of heterogeneous sensors with an arbitrary type of fault model, while it provides a mxihle tradeoff between accuracy and latency. The key idea is to formulate on-line testing as a set of instances of a non-linear function minimization and consequently apply nonparametric statistical methods to identify the sensors that have the highest probability to be faulty. The optimization is conducted using the Powell nonlinear function minimization method. The effectiveness of the approach is evaluated in the presence of random noise using a system of light sensors.
In Ref. REF , the taxonomy for classification of fault in sensor networks and the first on-line model-based testing technique was introduced.
On-line fault detection of sensor measurements
Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498)
Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498)
Engineering