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Real-time information processing of environmental sensor network data using Bayesian Gaussian processes

Osborne, Michael A., Roberts, Stephen J., Rogers, Alex and Jennings, Nicholas R. (2012) Real-time information processing of environmental sensor network data using Bayesian Gaussian processes ACM Transactions on Sensor Networks, 9, (1), 1:1-1:32. (doi:10.1145/2379799.2379800).

Record type: Article

Abstract

In this article, we consider the problem faced by a sensor network operator who must infer, in real time, the value of some environmental parameter that is being monitored at discrete points in space and time by a sensor network. We describe a powerful and generic approach built upon an efficient multi-output Gaussian process that facilitates this information acquisition and processing. Our algorithm allows effective inference even with minimal domain knowledge, and we further introduce a formulation of Bayesian Monte Carlo to permit the principled management of the hyperparameters introduced by our flexible models. We demonstrate how our methods can be applied in cases where the data is delayed, intermittently missing, censored, and/or correlated. We validate our approach using data collected from three networks of weather sensors and show that it yields better inference performance than both conventional independent Gaussian processes and the Kalman filter. Finally, we show that our formalism efficiently reuses previous computations by following an online update procedure as new data sequentially arrives, and that this results in a four-fold increase in computational speed in the largest cases considered.

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More information

Published date: November 2012
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 272749
URI: http://eprints.soton.ac.uk/id/eprint/272749
PURE UUID: f231fd8d-6691-43e0-8950-2048ce3d0e39

Catalogue record

Date deposited: 06 Sep 2011 13:33
Last modified: 18 Jul 2017 06:20

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Contributors

Author: Michael A. Osborne
Author: Stephen J. Roberts
Author: Alex Rogers
Author: Nicholas R. Jennings

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