Distributed localization in wireless sensor networks: a quantitative comparison
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
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Proceedings of the 3rd international symposium on Information processing in sensor networks
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Proceedings of the 3rd international symposium on Information processing in sensor networks
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Proceedings of the 5th international conference on Information processing in sensor networks
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Proceedings of the 5th international conference on Information processing in sensor networks
Algorithms for simultaneous sparse approximation: part I: Greedy pursuit
Signal Processing - Sparse approximations in signal and image processing
Spatial correlation-based collaborative medium access control in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
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ACM Transactions on Sensor Networks (TOSN)
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STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Leveraging redundancy in sampling-interpolation applications for sensor networks
DCOSS'07 Proceedings of the 3rd IEEE international conference on Distributed computing in sensor systems
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MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
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DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
Sparse Bayesian learning for basis selection
IEEE Transactions on Signal Processing
An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem
IEEE Transactions on Signal Processing - Part II
Subset selection in noise based on diversity measure minimization
IEEE Transactions on Signal Processing
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We focus on exploiting redundancy for sensor networks in the context of spatial interpolation. The network acts as a distributed sampling system, where sensors periodically sample a physical phenomenon of interest, e.g. temperature. Samples are then used to construct a continuous spatial estimate of the phenomenon over time through interpolation. In this regime, the notion of sensing range typically utilized to characterize redundancy in event detection applications is meaningless and sensor selection schemes based on it become unsuitable. Instead, this paper presents pragmatic approaches for exploiting redundancy in such applications. Their underlying characteristic is that no a-priori assumptions need to be made on the statistical properties of the physical phenomenon. These are instead learned by the network after deployment. Our approaches are evaluated through real as well as synthetic sensor network data showing that significant reductions in the number of active sensors are indeed possible.