Principles of data mining
Declarative Data Cleaning: Language, Model, and Algorithms
Proceedings of the 27th International Conference on Very Large Data Bases
Potter's Wheel: An Interactive Data Cleaning System
Proceedings of the 27th International Conference on Very Large Data Bases
Cleaning and querying noisy sensors
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Approximate distributed Kalman filtering in sensor networks with quantifiable performance
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A collaborative approach to in-place sensor calibration
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Robust preprocessing for health care monitoring framework
Healthcom'09 Proceedings of the 11th international conference on e-Health networking, applications and services
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Sensor networks have become an important source of data with numerous applications in monitoring various real-life phenomena as well as industrial applications and traffic control. However, sensor data are subject to several sources of errors as the data captured from the physical world through these sensor devices tend to be incomplete, noisy, and unreliable, thus yielding imprecise or even incorrect and misleading answers which can be very significative if they result in immediate critical decisions or activation of actuators. Traditional data cleaning techniques cannot be applied in this context as they do not take into account the strong spatial and temporal correlations typically present in sensor data, so machine learning techniques could greatly be of aid. In this paper, we propose a neuro-fuzzy regression approach to clean sensor network data: the well known ANFIS model is employed for reducing the uncertainty associated with the data thus obtaining a more accurate estimate of sensor readings. The obtained cleaning results show good ANFIS performance compared to other common used model such as kernel methods, and we demonstrate its effectiveness if the cleaning model has to be implemented at sensor level rather than at base-station level.