The weighted majority algorithm
Information and Computation
Approximation schemes for covering and packing problems in image processing and VLSI
Journal of the ACM (JACM)
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
A randomized art-gallery algorithm for sensor placement
SCG '01 Proceedings of the seventeenth annual symposium on Computational geometry
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Set k-cover algorithms for energy efficient monitoring in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Intelligent light control using sensor networks
Proceedings of the 3rd international conference on Embedded networked sensor systems
Deploying wireless sensors to achieve both coverage and connectivity
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
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
Regret minimization and the price of total anarchy
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Distributed selection: a missing piece of data aggregation
Communications of the ACM - Enterprise information integration: and other tools for merging data
The Journal of Machine Learning Research
Near-optimal observation selection using submodular functions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Submodularity and its applications in optimized information gathering
ACM Transactions on Intelligent Systems and Technology (TIST)
Online active inference and learning
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
On dynamic data-driven selection of sensor streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Utility-driven data acquisition in participatory sensing
Proceedings of the 16th International Conference on Extending Database Technology
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A key problem in sensor networks is to decide which sensors to query when, in order to obtain the most useful information (e.g., for performing accurate prediction), subject to constraints (e.g., on power and bandwidth). In many applications the utility function is not known a priori, must be learned from data, and can even change over time. Furthermore for large sensor networks solving a centralized optimization problem to select sensors is not feasible, and thus we seek a fully distributed solution. In this paper, we present Distributed Online Greedy (DOG), an efficient, distributed algorithm for repeatedly selecting sensors online, only receiving feedback about the utility of the selected sensors. We prove very strong theoretical no-regret guarantees that apply whenever the (unknown) utility function satisfies a natural diminishing returns property called submodularity. Our algorithm has extremely low communication requirements, and scales well to large sensor deployments. We extend DOG to allow observation-dependent sensor selection. We empirically demonstrate the effectiveness of our algorithm on several real-world sensing tasks.