AI Game Programming Wisdom
Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
Distributed optimization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
A kernel-based learning approach to ad hoc sensor network localization
ACM Transactions on Sensor Networks (TOSN)
Scalable, synthetic, sensor network data generation
Scalable, synthetic, sensor network data generation
Spatial correlation-based collaborative medium access control in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Modeling spatially correlated data in sensor networks
ACM Transactions on Sensor Networks (TOSN)
A spatial sampling scheme based on innovations diffusion in sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Wireless sensor network localization techniques
Computer Networks: The International Journal of Computer and Telecommunications Networking
Online prediction of time series data with kernels
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
Nonparametric decentralized detection using kernel methods
IEEE Transactions on Signal Processing
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
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Wireless sensor networks rely on sensor devices deployed in an environment to support sensing and monitoring, including temperature, humidity, motion, and acoustic. Here, we propose a new approach to model physical phenomena and track their evolution by taking advantage of the recent developments of pattern recognition for nonlinear functional learning. These methods are, however, not suitable for distributed learning in sensor networks as the order of models scales linearly with the number of deployed sensors and measurements. In order to circumvent this drawback, we propose to design reduced order models by using an easy to compute sparsification criterion. We also propose a kernel-based least-mean-square algorithm for updating the model parameters using data collected by each sensor. The relevance of our approach is illustrated by two applications that consist of estimating a temperature distribution and tracking its evolution over time.