Gossip Algorithms for Distributed Signal Processing

TLDR

Gossip algorithms enable in‑network processing in sensor networks without specialized routing, avoiding bottlenecks or single points of failure, and are robust to unreliable wireless conditions, while recent research has accelerated their development and theoretical analysis. This paper surveys recent advances in gossip algorithms for distributed signal processing. The authors analyze convergence rates tied to message counts and energy use, and examine wireless link challenges such as quantization and noise while demonstrating gossip’s application to distributed estimation, source localization, and compression. They show that gossip algorithms can effectively perform distributed estimation, source localization, and compression, but their performance is affected by quantization and noise over wireless links.

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 paper 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.