Evidence map›Paper›PMID 35890858›Full record

ArticleSensors (Basel, Switzerland)2022

Fruchterman-Reingold Hexagon Empowered Node Deployment in Wireless Sensor Network Application.

Jiahao Li, Yuhao Tao, Kai Yuan, Rongxin Tang, Zhiming Hu, Weichao Yan, Shiyun Liu

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

7 authors.

Jiahao LiSchool of Mathematics and Computer Science, Nanchang University, Nanchang 330031, China.
Yuhao TaoSchool of Mathematics and Computer Science, Nanchang University, Nanchang 330031, China.
Kai YuanInstitute of Space Science and Technology, Nanchang University, Nanchang 330031, China.
Rongxin TangInstitute of Space Science and Technology, Nanchang University, Nanchang 330031, China.ORCID 0000-0003-0154-3456
Zhiming HuDepartment of Physics, Nanchang University, Nanchang 330031, China.
Weichao YanInstitute of Space Science and Technology, Nanchang University, Nanchang 330031, China.
Shiyun LiuUsun Miscroelectronics, Nanchang 330072, China.

Funding

National Natural Science Foundation of China 41974195National Natural Science Foundation of China 61861031
6 · The paper itself

Abstract

Internet of Things (IoT) and Big Data technologies are becoming increasingly significant parts of national defense and the military, as well as in the civilian usage. The proper deployment of large-scale wireless sensor network (WSN) provides the foundation for these advanced technologies. Based on the Fruchterman-Reingold graph layout, we propose the Fruchterman-Reingold Hexagon (FR-HEX) algorithm for the deployment of WSNs. By allocating edges of hexagonal topology to sensor nodes, the network forms hexagonal network topology. A comprehensive evaluation of 50 simulations is conducted, which utilizes three evaluation metrics: average moving distance, pair correlation diversion (PCD), and system coverage rate. The FR-HEX algorithm performs consistently, the WSN topologies are properly regulated, the PCD values are below 0.05, and the WSN system coverage rate reaches 94%. Simulations involving obstacles and failed nodes are carried out to explore the practical applicability of the FR-HEX algorithm. In general, the FR-HEX algorithm can take full advantage of sensors' hardware capabilities in the deployment. It may be a viable option for some IoT and Big Data applications in the near future.

Indexed as

Fruchterman–Reingold algorithmnode deploymentunmanned aerial vehiclewireless sensor network

Identifiers

PMID35890858
PMCPMC9319457

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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.