Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Near-optimal sensor placements: maximizing information while minimizing communication cost
Proceedings of the 5th international conference on Information processing in sensor networks
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Gaussian Process Models for Censored Sensor Readings
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Decentralized control of adaptive sampling in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Sequential Bayesian prediction in the presence of changepoints
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A utility-based adaptive sensing and multihop communication protocol for wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
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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.