Evidence map›Paper›PMID 36146341›Full record

ArticleSensors (Basel, Switzerland)2022

Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues.

Sedeng Danba, Jingjing Bao, Guorong Han, Siri Guleng, Celimuge Wu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Sedeng DanbaSchool of Computer Science and Information Engineering, Hohhot Minzu College, Hohhot 010051, China.
Jingjing BaoGraduate School of Informatics and Engineering, University of Electro-Communications, Tokyo 182-8585, Japan.
Guorong HanB&S Tech Co., Ltd., Arakawa-ku, Tokyo 116-0014, Japan.
Siri GulengSchool of Computer Science and Information Engineering, Hohhot Minzu College, Hohhot 010051, China.
Celimuge WuGraduate School of Informatics and Engineering, University of Electro-Communications, Tokyo 182-8585, Japan.ORCID 0000-0001-6853-5878

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Internet of Vehicles (IoV) technology has been attracting great interest from both academia and industry due to its huge potential impact on improving driving experiences and enabling better transportation systems. While a large number of interesting IoV applications are expected, it is more challenging to design an efficient IoV system compared with conventional Internet of Things (IoT) applications due to the mobility of vehicles and complex road conditions. We discuss existing studies about enabling collaborative intelligence in IoV systems by focusing on collaborative communications, collaborative computing, and collaborative machine learning approaches. Based on comparison and discussion about the advantages and disadvantages of recent studies, we point out open research issues and future research directions.

Indexed as

Automobile DrivingInternet of ThingsIntelligenceRemote Sensing TechnologyTechnologycollaborative intelligenceedge computinginternet of vehiclesmachine learningmulti-access vehicular environment

Identifiers

PMID36146341
PMCPMC9503948

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.