Evidence map›Paper›PMID 36236287›Full record

ArticleSensors (Basel, Switzerland)2022

LoRaWAN Performance Analysis for a Water Monitoring and Leakage Detection System in a Housing Complex.

Atheer M Alghamdi, Enas F Khairullah, Mohammad M Al Mojamed

Open access · goldAbstract 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 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.8field-weighted citation impact, top 15% of its field
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

8 citing papers in PubMed, 23 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Atheer M AlghamdiInformation Technology Department, Faculty of Computers and Information Technology, King Abdulaziz University, Jeddah 23713, Saudi Arabia.ORCID 0000-0002-8763-8044
Enas F KhairullahInformation Technology Department, Faculty of Computers and Information Technology, King Abdulaziz University, Jeddah 23713, Saudi Arabia.ORCID 0000-0002-5969-9333
Mohammad M Al MojamedComputer Science Department, Computing College-Al-Qunfudah, UMM Al-QURA University, Al-Qunfudah 28814, Saudi Arabia.ORCID 0000-0002-4148-0958
King Abdulaziz University · SAUmm al-Qura University · SA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The automation of water leakage detection and monitoring systems has recently been made possible by the Internet of Things (IoT). However, the high cost is an obstacle when applying a network over a large area. The Low-Power Wide-Area Network (LPWAN) was created specifically to address long-range IoT applications. The Long-Range Wide-Area Network (LoRaWAN) is one of the most common LPWANs. In this study, a method for monitoring and detecting water leakage in a housing complex was tested using LoRaWAN. Water leakage was detected using a low-pressure system model comprising a water meter, presser sensor, and smart valve within a LoRa node. This study investigates the use of LoRaWAN for water monitoring and leakage detection by implementing a comprehensive case study to identify LoRaWAN's feasibility, reliability, and scalability for water monitoring and leakage detection in simulated scenarios. The housing complex varied in size and number of nodes. The LoRaWAN was evaluated by the FloRa simulator package through the Objective Modular Network Testbed (OMNeT++) platform. The results indicated that it was an efficient means of water monitoring and leakage detection in housing complexes.

Indexed as

Delivery of Health CareHousingMonitoring, PhysiologicReproducibility of ResultsWaterWaterFloRahousing complexIoTleakage detectionLoRaWANLPWANwater monitoring

Identifiers

PMID36236287
PMCPMC9573328
OpenAlexW4296830544

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.