Sensor networks are widely used in industrial and academic applications as the pervasive sensing module of anintelligent system. Sensor nodes may occasionally produce incorrect measurements due to battery depletion, dust on the sensor, manumissions and other causes. The aim of this paper is to develop a distributed Bayesian fault detection algorithm that classiﬁes measurements coming from the network as corrupted or not. The computational complexity is polynomial so the algorithm scales well with the size of the network. We tested the approach on a synthetic dataset and obtained signiﬁcant results in terms of correctly labeled measurements.
|Numero di pagine||6|
|Stato di pubblicazione||Published - 2012|
All Science Journal Classification (ASJC) codes