IKNOS Inference and knowledge in networks of sensors

This paper presents a framework for managing data from sensor of poor quality, withthe 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 collectingand 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 withsensors and hence the consumption of their battery; they are updated locally, when sensorreadings differ excessively from simulated data. At the sink the global model (a BayesianNetwork) 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). © 2010 Inderscience Enterprises Ltd.