Approximation schemes for covering and packing problems in image processing and VLSI
Journal of the ACM (JACM)
ACM Computing Surveys (CSUR)
A randomized art-gallery algorithm for sensor placement
SCG '01 Proceedings of the seventeenth annual symposium on Computational geometry
Utility-based decision-making in wireless sensor networks
MobiHoc '00 Proceedings of the 1st ACM international symposium on Mobile ad hoc networking & computing
Near-optimal sensor placements in Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Near-optimal sensor placements: maximizing information while minimizing communication cost
Proceedings of the 5th international conference on Information processing in sensor networks
Utility based sensor selection
Proceedings of the 5th international conference on Information processing in sensor networks
Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach
Proceedings of the 24th international conference on Machine learning
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
NP-hardness of Euclidean sum-of-squares clustering
Machine Learning
Maximum mutual information principle for dynamic sensor query problems
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
IEEE Communications Magazine
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We study the problem of optimal sensor placement in the context of soil moisture sensing. We show that the soil moisture data possesses some unique features that can be used together with the commonly used Gaussian assumption to construct more scalable, robust, and better performing placement algorithms. Specifically, there exists a coarse-grained monotonic ordering of locations in their soil moisture level over time, both in terms of its first and second moments, a feature much more stable than the soil moisture process itself at these locations. This motivates a clustered sensor placement scheme, where locations are classified into clusters based on the ordering of the mean, with the number of sensors placed in each cluster determined by the ordering of the variances. We show that under idealized conditions the greedy mutual information maximization algorithm applied globally is equivalent to that applied cluster by cluster, but the latter has the advantage of being more scalable. Extensive numerical experiments are performed on a set of three-dimensional soil moisture data generated by a state-of-the-art soil moisture simulator. Our results show that our clustering approach outperforms applying the same algorithms globally, and is very robust to lack of training and errors in training data.