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).

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Description/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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1145/2379799.2379800
Subjects:
Organisations: Agents, Interactions & Complexity
ePrint ID: 272749
Date :
Date Event
November 2012Published
Date Deposited: 06 Sep 2011 13:33
Last Modified: 17 Apr 2017 17:39
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/272749

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