IKNOS: inference and knowledge in networks of sensors. (28th October 2010)

• Record Type: Journal Article
• Authors: Toscani, Daniele; Archetti, Francesco; Frigerio, Marco; Messina, Enza
• Is Part Of: International journal of sensor networks. Volume 8:Number 3/4(2010)
• Page Start: 209
• Page End: 221
• Publication Date: 2010-10-28
• Subjects: 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; Sensor networks -- Periodicals; 681.2
• Languages: English
• ISSNs: 1748-1279

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).