Distributed Sparse Signal Recovery For Sensor Networks

TL;DR

This paper introduces a distributed sparse signal recovery algorithm for sensor networks that significantly reduces communication costs by leveraging solutions to the distributed top-K problem, outperforming existing methods.

Contribution

The paper presents a novel distributed IHT-based algorithm that minimizes communication and computational costs in sensor network signal recovery.

Findings

Requires up to three orders of magnitude less bandwidth than previous methods.

Effectively leverages distributed top-K problem solutions.

Achieves accurate sparse signal recovery with low communication overhead.

Abstract

We propose a distributed algorithm for sparse signal recovery in sensor networks based on Iterative Hard Thresholding (IHT). Every agent has a set of measurements of a signal x, and the objective is for the agents to recover x from their collective measurements at a minimal communication cost and with low computational complexity. A naive distributed implementation of IHT would require global communication of every agent's full state in each iteration. We find that we can dramatically reduce this communication cost by leveraging solutions to the distributed top-K problem in the database literature. Evaluations show that our algorithm requires up to three orders of magnitude less total bandwidth than the best-known distributed basis pursuit method.