Online distributed sensor selection

11 years 8 months ago
Online distributed sensor selection
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 ext...
Daniel Golovin, Matthew Faulkner, Andreas Krause
Added 18 May 2010
Updated 18 May 2010
Type Conference
Year 2010
Where IPSN
Authors Daniel Golovin, Matthew Faulkner, Andreas Krause
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