Applied multivariate statistical analysis
Applied multivariate statistical analysis
Maximum-entropy remote sampling
Discrete Applied Mathematics
Local search heuristic for k-median and facility location problems
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Approximation algorithms
A new greedy approach for facility location problems
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Utility-based decision-making in wireless sensor networks
MobiHoc '00 Proceedings of the 1st ACM international symposium on Mobile ad hoc networking & computing
A constant-factor approximation algorithm for the k-median problem
Journal of Computer and System Sciences - STOC 1999
Primal-Dual Approximation Algorithms for Metric Facility Location and k-Median Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
The impact of spatial correlation on routing with compression in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Set k-cover algorithms for energy efficient monitoring in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Approximately uniform random sampling in sensor networks
DMSN '04 Proceeedings of the 1st international workshop on Data management for sensor networks: in conjunction with VLDB 2004
Efficient gathering of correlated data in sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Improved Combinatorial Algorithms for Facility Location Problems
SIAM Journal on Computing
Near-optimal sensor placements in Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Utility based sensor selection
Proceedings of the 5th international conference on Information processing in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Algorithms for subset selection in linear regression
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
Cluster-Swap: A Distributed K-median Algorithm for Sensor Networks
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Modeling of extreme data in wireless sensor networks
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Energy efficient min-max spatial monitoring with wireless sensor networks
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
In-network data estimation for sensor-driven scientific applications
HiPC'08 Proceedings of the 15th international conference on High performance computing
Estimating the average of a Lipschitz-continuous function from one sample
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
In-situ soil moisture sensing: Optimal sensor placement and field estimation
ACM Transactions on Sensor Networks (TOSN)
On assessing the accuracy of positioning systems in indoor environments
EWSN'13 Proceedings of the 10th European conference on Wireless Sensor Networks
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We study the problem of choosing the "best'' subset of ksensors to sample from among a sensor deployment of n k sensors, in order to predict aggregate functions over all the sensor values. The sensor data being measured are assumed to be spatially correlated, in the sense that the values at two sensors can differ by at most a monotonically increasing, concave function of their distance. The goal is then to select a subset of sensors so as to minimize the prediction error, assuming that the actual values at unsampled sensors are worst-case subject to the constraints imposed by their distances from sampled sensors.Even selecting sensors for the optimal prediction of the mean, maximum or minimum is NP-hard; we present approximation algorithms to select near-optimal subsets of k sensors that minimize the worst-case prediction error. In general, we show that for any aggregate function satisfying certain concavity, symmetry and monotonicity conditions, the sensor selection problem can be modeled as a k-median clustering problem, and solved using efficient approximation algorithms designed for k-median clustering.Our theoretical results are complemented by experiments on tworeal-world sensor data sets; our experiments confirm that ouralgorithms lead to prediction errors that are usually less thanthe (normalized) standard deviation of the test data, using only around 10% of the sensors.