Collective Mining of Bayesian Networks from Distributed Heterogeneous Data
Knowledge and Information Systems
Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards
Computer Communications
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Learning Bayesian Network Structure from Distributed Homogeneous Data
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03
IEEE Journal on Selected Areas in Communications - Special issue on wireless and pervasive communications for healthcare
Environmental Modelling & Software
PAQ: time series forecasting for approximate query answering in sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
IKNOS: inference and knowledge in networks of sensors
International Journal of Sensor Networks
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The analysis of sensor data is a major activity in an emergency system; it is aimed at extracting useful information and at executing monitoring and anomaly detection. We focus on automatic data analysis through machine learning techniques, which require creating a model of the data that has to be kept up to date to match the evolving status of the environment. The update of a model improves its quality but introduces computation and communication overhead. In this paper we address the problem of identifying the optimal trade off between a low update rate and high quality of the model, we describe two update strategies and we draw considerations from their application on two sets of sensor data.