Title
Real-time information processing of environmental sensor network data using bayesian gaussian processes
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.
Year
DOI
Venue
2012
10.1145/2379799.2379800
TOSN
Keywords
Field
DocType
sensor network operator,generic approach,sensor network,real-time information processing,real time,effective inference,efficient multi-output gaussian process,weather sensor,inference performance,new data sequentially,conventional independent gaussian process,environmental sensor network data,bayesian gaussian process,information processing,gaussian processes
Data mining,Monte Carlo method,Hyperparameter,Adaptive sampling,Computer science,Inference,Kalman filter,Gaussian process,Wireless sensor network,Bayesian probability
Journal
Volume
Issue
ISSN
9
1
1550-4859
Citations 
PageRank 
References 
29
1.10
7
Authors
4
Name
Order
Citations
PageRank
Michael Osborne125033.49
stephen j roberts21244174.70
alex rogers32500183.76
Nicholas R. Jennings4193481564.35