Authors: Daniele Toscani, Francesco Archetti, Marco Frigerio, Enza Messina
Addresses: Consorzio Milano Ricerche, Milan, Italy. ' Consorzio Milano Ricerche, Milan, Italy; Department of Computer Science, Systems and Communication (DISCO), University of Milano Bicocca, Milan, Italy. ' Consorzio Milano Ricerche, Milan, Italy. ' Department of Computer Science, Systems and Communication (DISCO), University of Milano Bicocca, Milan, Italy
Abstract: This paper presents a framework for managing data from sensor of poor quality, with the objective to reduce at the same time the communication load and hence energy consumption. Each node in a wireless sensor network maintains a simple local model of the data it is collecting and sends its parameters to a central location (sink), where it is executed the global monitoring. Local models are used to simulate sensor|s readings, minimising the need of communication with sensors and hence the consumption of their battery; they are updated locally, when sensor readings differ excessively from simulated data. At the sink the global model (a Bayesian Network) is learnt on the simulated data. It is used to identify and replace anomalous readings (outliers) that a sensor should have produced and to detect anomalies missed by any single node (when communication with a sensor is interrupted).
Keywords: data aggregation; data modelling; time series forecasting; WSNs; wireless sensor networks; network monitoring; communication optimisation; anomaly detection; energy saving; wireless networks; inference; knowledge; data management; energy consumption.
International Journal of Sensor Networks, 2010 Vol.8 No.3/4, pp.209 - 221
Available online: 27 Oct 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article