IKNOS: inference and knowledge in networks of sensors
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).
data aggregation data modelling time series forecasting wsn wireless sensor networks monitoring communication optimisation anomaly detection energy saving