Evidence map›Paper›PMID 39797076›Full record

ArticleSensors (Basel, Switzerland)2025

An Innovative Linear Wireless Sensor Network Reliability Evaluation Algorithm.

Tao Ma, Huidong Guo, Xin Li

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

3 authors.

Tao MaCollege of Information Science and Engineering, Shenyang University of Technology, Shenyang 110167, China.
Huidong GuoCollege of Information Science and Engineering, Shenyang University of Technology, Shenyang 110167, China.
Xin LiCollege of Information Science and Engineering, Shenyang University of Technology, Shenyang 110167, China.

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

In recent years, wireless sensor networks (WSNs) have become a crucial technology for infrastructure monitoring. To ensure the reliability of monitoring services, evaluating the network's reliability is particularly important. Sensor nodes are distributed linearly when monitoring linear structures, such as railway bridges, forming what is known as a Linear Wireless Sensor Network (LWSN). Although existing evaluation methods, such as enumeration and Binary Decision Diagram (BDD)-based methods, can be used to assess the reliability of various types of networks, their efficiency is relatively low. Therefore, we classified network states based on the number of failed nodes located at the network's ends and analyzed the arrangement characteristics of nodes under different network states. This paper proposed a new reliability assessment method for LWSNs. This method is based on the combinatorial patterns of nodes and uses the concept of integer partitions to calculate the total number of states at different performance levels, applying probability formulas to assess network reliability. Compared to Multi-Valued Decision Diagram (MDD)-based evaluation algorithms, this method is suitable for large-scale LWSNs and offers lower time complexity.

Indexed as

failure probabilityLWSNsnumber splittingperformance levelreliability evaluation

Identifiers

PMID39797076
PMCPMC11723447

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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.