Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 164

The DOI matches its OpenAlex record; 1 of the 1 references it cites were found in a registry or an index.

Ubiquitous access to distributed data in large-scale sensor networks through decentralized erasure codes

Alexandros G. Dimakis, Vinod M. Prabhakaran, Kannan Ramchandran·2005
DOI 10.5555/1147685.1147708

Abstract

Consider a large-scale wireless sensor network of n nodes, where a fraction k out of n generate data packets of global interest. Assuming that the individual nodes have limited storage and computational capabilities, we address the problem of how to enable ubiquitous access to the distributed data packets. Specifically, we assume that each node can store at most one data packet, and study the problem of diffusing the data so that by querying any k nodes, it is possible to retrieve all the k data packets of interest (with high probability). We introduce a class of erasure codes and show how to solve this problem efficiently in a completely distributed and robust way. Specifically we show that we can efficiently diffuse the data by “prerouting” only O(ln n) packets per data node to randomly selected storage nodes. By using the proposed scheme, the distributed data becomes available “at the fingertips” of a potential data collector located anywhere in the network.

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

OpenAlex · 10.5555/1147685.1147708

Cites

1 of 1 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 1 references

Cited by

See all 164

Metadata

Authors
Dimakis, A. G., Prabhakaran, V. M., & Ramchandran, K.
Published
2005-04-24
Pages
111-117
DOI
10.5555/1147685.1147708
OpenAlex
W2097401531
Cited by
164
References
1
FWCI
16.44
Type
journal article
Language
en
Open access
closed

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: Ubiquitous access to distributed data in large-scale sensor networks through decentralized erasure codes. https://app.cochat.ai/papers/10.5555/1147685.1147708
The paper
Dimakis, A. G., Prabhakaran, V. M., & Ramchandran, K. (2005). Ubiquitous access to distributed data in large-scale sensor networks through decentralized erasure codes. https://doi.org/10.5555/1147685.1147708

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.