Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: A key problem in sensor networks is to decide which sensors<lb>to query when, in order to obtain the most useful information<lb>(e.g., for performing accurate prediction), subject to con-<lb>straints (e.g., on power and bandwidth). In many applications<lb>the utility function is not known a priori, must be learned<lb>from data, and can even change over time. Furthermore for<lb>large sensor networks solving a centralized optimization prob-<lb>lem to select sensors is not feasible, and thus we seek a fully<lb>distributed solution. In this paper, we present Distributed<lb>Online Greedy (DOG), an efficient, distributed algorithm for<lb>repeatedly selecting sensors online, only receiving feedback<lb>about the utility of the selected sensors. We prove very strong theoretical no-regret guarantees that apply whenever the (un-<lb>known) utility function satisfies a natural diminishing returns<lb>property called submodularity. Our algorithm has extremely<lb>low communication requirements, and scales well to large sensor deployments. We extend DOG to allow observation-<lb>dependent sensor selection. We empirically demonstrate the<lb>effectiveness of our algorithm on several real-world sensing<lb>tasks.

Online Distributed Sensor Selection