Evidence map›Paper›PMID 35336543›Full record

ArticleSensors (Basel, Switzerland)2022

Designing a Reliable and Low-Latency LoRaWAN Solution for Environmental Monitoring in Factories at Major Accident Risk.

Dinesh Tamang, Alessandro Pozzebon, Lorenzo Parri, Ada Fort, Andrea Abrardo

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Dinesh TamangDepartment of Information Engineering and Mathematical Science, University of Siena, 53100 Siena, Italy.ORCID 0000-0001-9510-7457
Alessandro PozzebonDepartment of Information Engineering, University of Padova, 35131 Padova, Italy.ORCID 0000-0003-3991-8858
Lorenzo ParriDepartment of Information Engineering and Mathematical Science, University of Siena, 53100 Siena, Italy.
Ada FortDepartment of Information Engineering and Mathematical Science, University of Siena, 53100 Siena, Italy.ORCID 0000-0003-0916-1516
Andrea AbrardoDepartment of Information Engineering and Mathematical Science, University of Siena, 53100 Siena, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this article, we propose a reliable and low-latency Long Range Wide Area Network (LoRaWAN) solution for environmental monitoring in factories at major accident risk (FMAR). In particular, a low power wearable device for sensing the toxic inflammable gases inside an industrial plant is designed with the purpose of avoiding peculiar risks and unwanted accidents to occur. Moreover, the detected data have to be urgently and reliably delivered to remote server to trigger preventive immediate actions so as to improve the machine operation. In these settings, LoRaWAN has been identified as the most proper communications technology to the needs owing to the availability of off the shelf devices and software. Hence, we assess the technological limits of LoRaWAN in terms of latency and reliability and we propose a fully LoRaWAN compliant solution to overcome these limits. The proposed solution envisages coordinated end device (ED) transmissions through the use of Downlink Control Packets (DCPs). Experimental results validate the proposed method in terms of service requirements for the considered FMAR scenario.

Indexed as

Environmental MonitoringWearable Electronic DevicesAccidentsReproducibility of ResultsSoftwaredownlinkIoTLoRaWANreliabilitysafety

Identifiers

PMID35336543
PMCPMC8948738

What OpenQuestion holds

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LicenceCC BY
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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.