Evidence map›Paper›PMID 35684773›Full record

ArticleSensors (Basel, Switzerland)2022

Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories.

Yue Li, Zhenyu Yin, Yue Ma, Fulong Xu, Haoyu Yu, Guangjie Han, Yuanguo Bi

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Yue LiSchool of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China.ORCID 0000-0002-0222-9859
Zhenyu YinSchool of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China.ORCID 0000-0001-5403-3922
Yue MaSchool of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
Fulong XuSchool of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China.ORCID 0000-0001-8690-4649
Haoyu YuSchool of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
Guangjie HanCollege of Internet of Things Engineering, Hohai University, Changzhou 213022, China.ORCID 0000-0002-6921-7369
Yuanguo BiSchool of Computer Science and Engineering, Northeastern University, Shenyang 110167, China.

Funding

National Key R&D Program of China 2017YFE0125300
6 · The paper itself

Abstract

Over recent years, traditional manufacturing factories have been accelerating their transformation and upgrade toward smart factories, which are an important concept within Industry 4.0. As a key communication technology in the industrial internet architecture, time-sensitive networks (TSNs) can break through communication barriers between subsystems within smart factories and form a common network for various network flows. Traditional routing algorithms are not applicable for this novel type of network, as they cause unnecessary congestion and latency. Therefore, this study examined the classification of TSN flows in smart factories, converted the routing problem into two graphical problems, and proposed two heuristic optimization algorithms, namely GATTRP and AACO, to find the optimal solution. The experiments showed that the algorithms proposed in this paper could provide a more reasonable routing arrangement for various TSN flows with different time sensitivities. The algorithms could effectively reduce the overall delay by up to 74% and 41%, respectively, with promising operating performances.

Indexed as

heuristic algorithmindustrial internetroutingsmart factorytime-sensitive network

Identifiers

PMID35684773
PMCPMC9185460

What OpenQuestion holds

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Registered trials

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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.