A Novel Approach for Faulty Sensor Detection and Data Correction in Wireless Sensor Network

The main Wireless Sensor Networks purpose is represented by areas of interest monitoring. Even if the Wireless sensor network is properly initialized, errors can occur during its monitoring tasks. The present work describes an approach for detecting faulty sensors in Wireless Sensor Network and for correcting their corrupted data. The approach is based on the assumption that exist a spatio-temporal cross-correlations among sensors. Two sequential mathematical tools are used. The first stage is a probabilistic tools, namely Markov Random Field, for a two-fold sensor classification (working or damaged). The last stage is represented by the Locally Weighted Regression model, a learning techniques modelling each sensor on the basis of its neighbours. If the sensor is working, the approach actives a learning phase and the sensor model is trained, while if the sensor is damaged, a correction phase starts and the related corrupted data are replaced with the data produced by the learned model. The effectiveness of the proposed approach has been proved using real data obtained from the Intel Berkeley Research Laboratory, over which different classes of faults were artificially superimposed. The proposed architecture achieves satisfactory results, since it successfully corrects faulty data produced by sensors.