Evidence map›Paper›PMID 39459984›Full record

ArticleSensors (Basel, Switzerland)2024

Self-Organizing and Routing Approach for Condition Monitoring of Railway Tunnels Based on Linear Wireless Sensor Network.

Haibo Yang, Huidong Guo, Junying Jia, Zhengfeng Jia, Aiyang Ren

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Haibo YangCollege of Information Science and Engineering, Shenyang University of Technology, Shenyang 110167, China.ORCID 0000-0001-9760-3905
Huidong GuoCollege of Information Science and Engineering, Shenyang University of Technology, Shenyang 110167, China.
Junying JiaCollege of Information Science and Engineering, Shenyang University of Technology, Shenyang 110167, China.
Zhengfeng JiaCollege of Information Science and Engineering, Shenyang University of Technology, Shenyang 110167, China.
Aiyang RenCollege of Information Science and Engineering, Shenyang University of Technology, Shenyang 110167, China.ORCID 0009-0003-8882-584X

Funding

Natural Science Foundation Joint Fund of Liaoning Province China 2023-MSLH-248Natural Science Foundation of Liaoning Province, China 2022-MS- 438Science and Technology Projects in Liaoning Province 2022JH1/10800085Scientific Study Project for Institutes of Higher Learning, Ministry of Education, Liaoning Province LJKFZ20220184
6 · The paper itself

Abstract

Real-time status monitoring is crucial in ensuring the safety of railway tunnel traffic. The primary monitoring method currently involves deploying sensors to form a Wireless Sensor Network (WSN). Due to the linear characteristics of railway tunnels, the resulting sensor networks usually have a linear topology known as a thick Linear Wireless Sensor Network (LWSN). In practice, sensors are deployed randomly within the area, and to balance the energy consumption among nodes and extend the network's lifespan, this paper proposes a self-organizing network and routing method based on thick LWSNs. This method can discover the topology, form the network from randomly deployed sensor nodes, establish adjacency relationships, and automatically form clusters using a timing mechanism. In the routing, considering the cluster heads' load, residual energy, and the distance to the sink node, the optimal next-hop cluster head is selected to minimize energy disparity among nodes. Simulation experiments demonstrate that this method has significant advantages in balancing network energy and extending network lifespan for LWSNs.

Indexed as

energy consumptionnetwork clusteringnetwork self-organizingrouting protocolthick LWSN

Identifiers

PMID39459984
PMCPMC11511145

What OpenQuestion holds

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LicenceCC BY
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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.