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
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
Decentralised coordination of mobile sensors using the max-sum algorithm
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Energy-efficient data gathering in wireless sensor networks with asynchronous sampling
ACM Transactions on Sensor Networks (TOSN)
Bayesian optimization for sensor set selection
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 3 - Volume 3
Bounded approximate decentralised coordination via the max-sum algorithm
Artificial Intelligence
Adaptive compression for 3D laser data
International Journal of Robotics Research
Computationally Efficient Convolved Multiple Output Gaussian Processes
The Journal of Machine Learning Research
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Autonomous Agents and Multi-Agent Systems
Kernels for Vector-Valued Functions: A Review
Foundations and Trends® in Machine Learning
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In this paper, we describe a novel, computationally efficient algorithm that facilitates the autonomous acquisition of readings from sensor networks (deciding when and which sensor to acquire readings from at any time), and which can, with minimal domain knowledge, perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensor readings, and predicting how the monitored environmental variables will evolve into the future. Our motivating scenario is the need to provide situational awareness support to first responders at the scene of a large scale incident, and to this end, we describe a novel iterative formulation of a multi-output Gaussian process that can build and exploit a probabilistic model of the environmental variables being measured (including the correlations and delays that exist between them). We validate our approach using data collected from a network of weather sensors located on the south coast of England.