Gossip Algorithms for Distributed Signal Processing

TL;DR

Gossip algorithms enable robust, energy-efficient distributed signal processing in sensor networks by allowing nodes to exchange information without centralized routing, with recent advances improving their speed and reliability.

Contribution

This paper provides an overview of recent developments in gossip algorithms, including convergence analysis, wireless communication issues, and applications in distributed estimation and localization.

Findings

Convergence rates depend on message count and energy consumption.

Wireless link issues like quantization and noise affect performance.

Gossip algorithms effectively perform distributed estimation and source localization.

Abstract

Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This article presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.