The coverage problem in a wireless sensor network
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Integrated coverage and connectivity configuration in wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Near-optimal sensor placements: maximizing information while minimizing communication cost
Proceedings of the 5th international conference on Information processing in sensor networks
Spatial correlation-based collaborative medium access control in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
A distortion-aware scheduling approach for wireless sensor networks
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
Sensor Selection for Minimizing Worst-Case Prediction Error
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Algorithms for subset selection in linear regression
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Energy efficient min-max spatial monitoring with wireless sensor networks
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Efficient Sensing Topology Management for Spatial Monitoring with Sensor Networks
Journal of Signal Processing Systems
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An important class of sensor network applications aims at estimating the spatiotemporal behavior of a physical phenomenon, such as temperature variations over an area of interest. These networks thereby essentially act as a distributed sampling system. However, unlike in the event detection class of sensor networks, the notion of sensing range is largely meaningless in this case. As a result, existing techniques to exploit sensing redundancy for event detection, which rely on the existence of such sensing range, become unusable. Instead, this paper presents a new method to exploit redundancy for the sampling class of applications, which adaptively selects the smallest set of reporting sensors to act as sampling points. By projecting the sensor space onto an equivalent Hilbert space, this method ensures sufficiently accurate sampling and interpolation, without a priori knowledge of the statistical structure of the physical process. Results are presented using synthetic sensor data and show significant reductions in the number of active sensors.