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The prize collecting Steiner tree problem: theory and practice
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Approximation algorithms
Wireless Communications: Principles and Practice
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Connected sensor cover: self-organization of sensor networks for efficient query execution
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Wireless Sensor Networks: An Information Processing Approach
Wireless Sensor Networks: An Information Processing Approach
Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
Saving an epsilon: a 2-approximation for the k-MST problem in graphs
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FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Near-optimal sensor placements: maximizing information while minimizing communication cost
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Utility based sensor selection
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Statistical model of lossy links in wireless sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
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IEEE Transactions on Computers
An analysis of unreliability and asymmetry in low-power wireless links
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
Improved algorithms for orienteering and related problems
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Algorithms for subset selection in linear regression
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
The Journal of Machine Learning Research
Relay sensor placement in wireless sensor networks
Wireless Networks
Gaussian Process Models for Censored Sensor Readings
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Nonmyopic informative path planning in spatio-temporal models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Near-optimal observation selection using submodular functions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Efficient planning of informative paths for multiple robots
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Optimal value of information in graphical models
Journal of Artificial Intelligence Research
A better approximation algorithm for the budget prize collecting tree problem
Operations Research Letters
Collaborative path planning for event search and exploration in mixed sensor networks
International Journal of Robotics Research
Collecting data in ad-hoc networks with reduced uncertainty
Ad Hoc Networks
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When monitoring spatial phenomena with wireless sensor networks, selecting the best sensor placements is a fundamental task. Not only should the sensors be informative, but they should also be able to communicate efficiently. In this article, we present a data-driven approach that addresses the three central aspects of this problem: measuring the predictive quality of a set of sensor locations (regardless of whether sensors were ever placed at these locations), predicting the communication cost involved with these placements, and designing an algorithm with provable quality guarantees that optimizes the NP-hard trade-off. Specifically, we use data from a pilot deployment to build nonparametric probabilistic models called Gaussian Processes (GPs) both for the spatial phenomena of interest and for the spatial variability of link qualities, which allows us to estimate predictive power and communication cost of unsensed locations. Surprisingly, uncertainty in the representation of link qualities plays an important role in estimating communication costs. Using these models, we present a novel, polynomial-time, data-driven algorithm, PSPIEL, which selects Sensor Placements at Informative and communication-Efficient Locations. Our approach exploits two important properties of this problem: submodularity, formalizing the intuition that adding a node to a small deployment can help more than adding a node to a large deployment; and locality, under which nodes that are far from each other provide almost independent information. Exploiting these properties, we prove strong approximation guarantees for our PSPIEL approach. In addition, we show how our placements can be made robust against changes in the environment, and how PSPIEL can be used to plan informative paths for information gathering using mobile robots. We also provide extensive experimental validation of this practical approach on several real-world placement problems, and built a complete system implementation on 46 Tmote Sky motes, demonstrating significant advantages over existing methods.