Evidence map›Paper›PMID 39460242›Full record

ArticleSensors (Basel, Switzerland)2024

Mobile Application Development for Prepaid Water Meter Based on LC Sensor.

Ario Kusuma Purboyo, Hanif Fakhrurroja, Dita Pramesti, Achmad Rozan Chaidir

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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

4 authors.

Ario Kusuma PurboyoSchool of Industrial Engineering, Telkom University, Bandung 40257, Indonesia.
Hanif FakhrurrojaSchool of Industrial Engineering, Telkom University, Bandung 40257, Indonesia.ORCID 0000-0002-2483-251X
Dita PramestiSchool of Industrial Engineering, Telkom University, Bandung 40257, Indonesia.ORCID 0000-0002-8784-5377
Achmad Rozan ChaidirSchool of Industrial Engineering, Telkom University, Bandung 40257, Indonesia.ORCID 0009-0009-7389-4045

Funding

Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia 024/SAM4/PPM/2024Telkom University 15/E1/KS.00.00/2024
6 · The paper itself

Abstract

This study presents a novel low-cost and low-power prepaid water meter system that combines tokenization and LC sensors to monitor water consumption accurately with mobile application via Bluetooth Low Energy (BLE) connectivity compared to conventional meters. Water meters play a vital role in monitoring water usage in Indonesia. Postpaid billing methods that rely on manual data recording are a source of concern due to potential inaccuracies caused by human error. This study presents the development of a prepaid water meter system that integrates LC sensors, BLE connectivity, a tokenization mechanism, and a mobile application to address this issue. The system offers a cost-effective solution by utilizing BLE + Global System for Mobile (GSM) from the user's mobile phone. Using the design thinking methodology, the mobile application for the prepaid water meter achieved a usability testing score of 80. The load testing results for the back-end server, conducted with a sample size of 515 users, revealed a back-end latency of 1.973 milliseconds and an error rate of 8.74%. Furthermore, the LC sensors integrated into the PWM device showed an average error rate of 1.33%. The power consumption during each work cycle was measured at 129 mA and each battery is expected to last six years. Overall, with simple LC sensors, this system can precisely measure water usage.

Indexed as

Bluetooth low energyLC sensormobile applicationprepaid water metertoken

Identifiers

PMID39460242
PMCPMC11511330

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.