Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 741

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Gossip Algorithms for Distributed Signal Processing

Open access
Dimakis, Alexandros G., Kar, Soummya, Moura, José M. F., Rabbat, Michael G., Scaglione, Anna·2010·Proceedings of the IEEE

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/jproc.2010.2052531

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 6 references

Cited by

See all 741

Metadata

Authors
Dimakis, A. G., Kar, S., Moura, J. M. F., Rabbat, M. G., & Scaglione, A.
Venue
Proceedings of the IEEE
Published
2010-11
Volume
98
Issue
11
Pages
1847-1864
ISSN
  • 0018-9219 (ISSN-L)
  • 1558-2256
OpenAlex
W2120286056
Cited by
741
References
6
FWCI
67.62
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Gossip Algorithms for Distributed Signal Processing. https://app.cochat.ai/papers/10.1109/jproc.2010.2052531
The paper
Dimakis, A. G., Kar, S., Moura, J. M. F., Rabbat, M. G., & Scaglione, A. (2010). Gossip Algorithms for Distributed Signal Processing. Proceedings of the IEEE. https://doi.org/10.1109/jproc.2010.2052531

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.