Evidence map›Paper›PMID 42539678›Full record

ArticleFundamental research2026

Hyper-converged space-air-ground integrated low-altitude aerial vehicular networks: Architecture, challenges, and technologies.

Haibo Zhou, Jianzhe Xue, Yi Yuan, Zeyu Sun, Yunting Xu, Jiacheng Chen, Xuemin Shen

Abstract read
In one paragraph

Article in Fundamental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Future mobile information networks.Fundamental research · 2026
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Haibo ZhouSchool of Electronic Science and Engineering, Nanjing University, 210023 Nanjing, Jiangsu, China.
Jianzhe XueSchool of Electronic Science and Engineering, Nanjing University, 210023 Nanjing, Jiangsu, China.
Yi YuanSchool of Electronic Science and Engineering, Nanjing University, 210023 Nanjing, Jiangsu, China.
Zeyu SunSchool of Electronic Science and Engineering, Nanjing University, 210023 Nanjing, Jiangsu, China.
Yunting XuSchool of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore.
Jiacheng ChenDepartment of Strategic and Advanced Interdisciplinary Research, Peng Cheng Laboratory, 518000 Shenzhen, Guangdong, China.
Xuemin ShenDepartment of Electrical and Computer Engineering, University of Waterloo, N2L3G1 Waterloo, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we introduce the space-air-ground integrated low-altitude aerial vehicular networks (SAG-LAAVN) that deliver ubiquitous intelligent connectivity for three-dimensional intelligent transportation systems (ITS). Built upon an innovative hyper-converged architecture that unifies Data, Operation, Information, and Communication Technologies (DOICT), SAG-LAAVN aims to address the stringent connectivity requirements imposed by the rapid evolution of low-altitude aerial vehicles, offering seamless coverage, enhanced resilience, and improved quality of experience across diverse scenarios. Three key scientific challenges in the SAG-LAAVN are discussed. Besides, we present three potential technologies: intelligent networking approach, multi-dimensional resource management, and virtual-physical dynamic collaboration. Implemented via the fully decoupled radio access network (FD-RAN), a case study is presented to demonstrate that SAG-LAAVN can provide ubiquitous, high-quality services that meet the evolving needs of the low-altitude economy.

Indexed as

6GDOICTLow-altitude networkSpace-air-groundUbiquitous connectivity

Identifiers

PMID42539678
PMCPMC13424412

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.