Online Distributed Sensor Selection

Online Distributed Sensor Selection (2010)

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by Daniel Golovin , Matthew Faulkner , Andreas Krause

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Datum	Value	Source
TITLE	Online Distributed Sensor Selection	user correction
AUTHOR NAME	Daniel Golovin	user correction
AUTHOR AFFIL	Caltech	user correction
AUTHOR NAME	Matthew Faulkner	user correction
AUTHOR AFFIL	Caltech	user correction
AUTHOR NAME	Andreas Krause	user correction
AUTHOR AFFIL	Caltech	user correction
ABSTRACT	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 observationdependent sensor selection. We empirically demonstrate the effectiveness of our algorithm on several real-world sensing tasks.	SVM HeaderParse 0.2
YEAR	2010	user correction
CITATIONS	32 found	ParsCit 1.0