Cited by 62Mobile Crowdsensing and CrowdsourcingOpportunistic and Delay-Tolerant NetworksVehicular Ad Hoc Networks (VANETs)

An intelligent big data collection technology based on micro mobile data centers for crowdsensing vehicular sensor network

Ren, Yingying, Wang, Tian, Zhang, Shaobo, Zhang, Jinhuan·2023·Personal and Ubiquitous Computing

Integrity signals

Registry

Crossref · 10.1007/s00779-020-01440-0

Cites

39 of 39 references verified0 citation errors · 0 not checkable

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Authors
Ren, Y., Wang, T., Zhang, S., & Zhang, J.
Affiliations
  • Central South University (CN)
  • Huaqiao University (CN)
  • Hunan University of Science and Technology (CN)
Corresponding author
Zhang, Jinhuan
Venue
Personal and Ubiquitous Computing
Published
2020-08-18
Volume
27
Issue
3
Pages
563-579
ISSN
  • 1617-4909 (ISSN-L)
  • 1617-4917
OpenAlex
W3063715646
Cited by
62
References
39
Citations by year
2026: 4 · 2025: 7 · 2024: 8 · 2023: 5 · 2022: 7 · 2021: 20 · 2020: 11
FWCI
9.43
Type
journal article
Language
en
Open access
closed
Locations
Personal and Ubiquitous Computing (publishedVersion)
Funders
  • National Natural Science Foundation of China (61902432, 61772554, 61602398, 61672447)
  • Hunan Provincial National Natural Science Foundation of China (2017JJ3316, No.2019JJ50592)
  • Independent Exploration and Innovation Project for Graduate Students of Central South University (2019zzts589)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
  • Physical Sciences › Computer Science › Computer Networks and Communications › Opportunistic and Delay-Tolerant Networks
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
Keywords
vehicular sensor networks, mobile crowdsensing, data collection, data redundancy, simulated annealing, data center selection, quality of service, Internet of Things

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.

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