Online Distributed Sensor Selection

TL;DR

This paper introduces Distributed Online Greedy (DOG), a scalable, low-communication distributed algorithm for sensor selection in networks with unknown, changing utility functions, with strong theoretical guarantees and real-world validation.

Contribution

The paper presents a novel distributed online algorithm for sensor selection with no-regret guarantees under submodular utility functions, scalable to large networks.

Findings

Strong theoretical no-regret guarantees for DOG.

Efficient low-communication protocol suitable for large networks.

Empirical validation on real-world sensing tasks.

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 observation-dependent sensor selection. We empirically demonstrate the effectiveness of our algorithm on several real-world sensing tasks.