Gossip Algorithms for Distributed Signal Processing Gossiping allows sensors in a network to ignore routing and just exchange data with their nearest neighbors; this paper explores the effects of gossiping on convergence rates, quantization, and channel coding, and surveys recent results.
Alexandros G. Dimakis, Soummya Kar, Fernando Silva de Moura, Michael Rabbat, Anna Scaglione · 2010
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 numberoftransmittedmessagesandthustheamountofenergy 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.