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
Exploratory Data Mining and Data Cleaning
Exploratory Data Mining and Data Cleaning
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
Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
Data cleaning using belief propagation
Proceedings of the 2nd international workshop on Information quality in information systems
A robust architecture for distributed inference in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
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 Weighted Moving Average-based Approach for Cleaning Sensor Data
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Machine learning in ecosystem informatics
DS'07 Proceedings of the 10th international conference on Discovery science
Declarative support for sensor data cleaning
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
IEEE Communications Magazine
Hi-index | 0.00 |
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. Sensor data is 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. Such errors may seriously impact the answer to any query posed to the sensors yielding imprecise or even incorrect and misleading answers for critical decisions or activation of actuators. Play, thus, a fundamental role data cleaning procedures to overcome these problems. The most recent applications in this research field conceive the use of machine learning techniques. Machine learning approaches have assumed a prominent role in data analysis especially for their ability to deal with very large amount of noisy and incomplete data. In this paper, we propose the application of the well known ANFIS model for reducing the uncertainty associated with the data thus obtaining a more accurate estimate of sensor readings. The obtained cleaning results demonstrate its effectiveness if the cleaning model has to be implemented at sensor level rather than at base-station level.