Evidence map›Paper›PMID 31337069›Full record

ArticleSensors (Basel, Switzerland)2019

Measurement and Characterization of Electromagnetic Noise in Edge Computing Networks for the Industrial Internet of Things.

Huiting Li, Liu Liu, Yiqian Li, Ze Yuan, Kun Zhang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. 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.

Huiting LiSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Liu LiuSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China. liuliu@bjtu.edu.cn.
Yiqian LiSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Ze YuanSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Kun ZhangSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.

Funding

Beijing Municipal Natural Science Foundation 4174102Natural Science Foundation of Beijing Municipality NO. L172030the Key Laboratory of Universal Wireless Communications (BUPT), Ministry of Education, P.R.China KFKT-2018105
6 · The paper itself

Abstract

Edge computing and the Internet of Things (IOT) provide the technological basis for the development of intelligent manufacturing nowadays. In order to support the intelligent interconnection and application of all kinds of equipment in the industrial field, edge computing should be equipped close to or embedded in all kinds of equipment nodes in the industrial wireless network. Therefore, it is meaningful to investigate the wireless network design of the Industrial Internet of Things. Low power wireless sensor devices are widely used in the Industrial Internet of Things (IIoT), which are sensitive to electromagnetic noise. The electromagnetic noises in industrial scenarios are significantly different from the conventional assumed white noise. In this paper, the measurement results of electromagnetic noises at three different test positions are given in an automobile factory. The spectrum occupancy of the factory wireless environment in the 300 MHz-3 GHz band was obtained by frequency domain measurement. In the time domain measurement, four statistical parameters of the three bands of 315 MHz, 433 MHz, and 916 MHz were measured, and the electromagnetic noise distributions in different plant areas and different frequency bands were analyzed. According to the measurement results, the time-varying characteristics of electromagnetic noise can be characterized by continuous hidden Markov models (CHMM). These results are informative to the design and optimization for the edge computing networks for IIoT.

Indexed as

continuous hidden Markov modeledge computingelectromagnetic noiseIndustrial Internet of Thingsmeasurement

Identifiers

PMID31337069
PMCPMC6679246

What OpenQuestion holds

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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.