Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 316

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Multi-UAV-Aided Networks: Aerial-Ground Cooperative Vehicular Networking Architecture

Zhou, Yi, Cheng, Nan, Lu, Ning, Shen, Xuemin Sherman·2015·IEEE Vehicular Technology Magazine

Abstract

In this article, an aerial-ground cooperative vehicular networking architecture is proposed. Multiple unmanned aerial vehicles (UAVs), forming an aerial subnetwork, aid the ground vehicular subnetwork through air-to-air (A2A) and air-to-ground (A2G) communications. UAVs can be dispatched to areas of interest to collect information, and transmit it to ground vehicles. Moreover, UAVs can act as intermediate relays due to their flexible mobility when network partitions happen in the ground vehicular subnetwork. With the assistance of UAVs, the twolayer cooperative networking can facilitate applications such as disaster rescue and polluted area investigation. Potential research issues and challenges in multi-UAV-aided vehicular networks are presented and discussed, which can shed light on extending the applications of vehicular networks in an extreme environment.

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/mvt.2015.2481560

Cites

8 of 8 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 8 references

Cited by

See all 316

Metadata

Authors
Zhou, Y., Cheng, N., Lu, N., & Shen, X. S.
Venue
IEEE Vehicular Technology Magazine
Published
2015-12
Volume
10
Issue
4
Pages
36-44
ISSN
1556-6072 (ISSN-L)
OpenAlex
W2183455461
Cited by
316
References
8
FWCI
32.19
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: Multi-UAV-Aided Networks: Aerial-Ground Cooperative Vehicular Networking Architecture. https://app.cochat.ai/papers/10.1109/mvt.2015.2481560
The paper
Zhou, Y., Cheng, N., Lu, N., & Shen, X. S. (2015). Multi-UAV-Aided Networks: Aerial-Ground Cooperative Vehicular Networking Architecture. IEEE Vehicular Technology Magazine. https://doi.org/10.1109/mvt.2015.2481560

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.