Elements of information theory
Elements of information theory
Declarative Data Cleaning: Language, Model, and Algorithms
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
Adaptive stream resource management using Kalman Filters
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Distributed Sensor Networks (Chapman & Hall/Crc Computer and Information Science)
Distributed Sensor Networks (Chapman & Hall/Crc Computer and Information Science)
Adaptive cleaning for RFID data streams
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A deferred cleansing method for RFID data analytics
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Online outlier detection in sensor data using non-parametric models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Declarative support for sensor data cleaning
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
PAQ: time series forecasting for approximate query answering in sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
IEEE Communications Magazine
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Wireless sensor network (WSN) data is often subjected to corruption and losses due to wireless medium of communication and presence of hardware inaccuracies in the nodes. For a WSN application to deduce an appropriate result it is necessary that the data received is clean, accurate, and lossless. WSN data cleaning systems exploit contextual associations existing in the received data to suppress data inconsistencies and anomalies. In this work we attempt to clean the data gathered from WSN by capturing the influence of changing dynamics of the environment on the contextual associations existing in the sensor nodes. Specifically, our work validates the extent of similarities among the sensed observations from contextually (spatio-temporally) associated nodes and considers the time of arrival of data at the sink to educate the cleaning process about the WSN's behavior. We term the data cleaning technique proposed in this work as time of arrival for data cleaning (TOAD). TOAD establishes belief on spatially related nodes to identify potential nodes that can contribute to data cleaning. By using information theory concepts and experiments on data sets from a real-time scenario we demonstrate and establish that validation of contextual associations among the sensor nodes significantly contributes to data cleaning. Copyright © 2010 John Wiley & Sons, Ltd.