Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)

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Evaluating Sensor-Derived Data Quality for IoT-based Temperature Monitoring

Authors
Aissa Bensattalah1, 2, *, Youcef Belhadji3
1Department of Science and Technology, University of Tiaret, Tiaret, 14000, Algeria
2Laboratoire des Méthodes de Conception des Systèmes (LMCS), 16000, Oued-Smar, Algiers, Algeria
3Department of Eelctrical Engineering, University of Tiaret, Tiaret, 14000, Algeria
*Corresponding author. Email: a_bensattalah@esi.dz
Corresponding Author
Aissa Bensattalah
Available Online 31 August 2024.
DOI
10.2991/978-94-6463-496-9_9How to use a DOI?
Keywords
IoT; statistical descriptive; data quality; monitoring
Abstract

IoT sensors undergo substantial fluctuations in their conditions, encompassing events of connectivity, disconnection, and alterations in environmental parameters. Within the scope of this paper, we introduce an experimental methodology to optimize the data quality of a temperature measurement and control system. To achieve the aim of the study, we employed a set of essential hardware components for data acquisition and processing. The integration comprised two types of temperature sensors of heterogeneous technologies: Dallas DS18B20, operating as a digital sensor, and LM35, used as an analog sensor. The measurement procedure encompasses two scenarios: simple tests involving individual sensor measurements and multiple tests entailing concurrent measurement using a group of three sensors of the same technology. The tests are made under ambient temperature and under heat source then cold environment (refrigerator). Applying a descriptive statistical approach, we computed the mean, variance, and standard deviation to assess the data quality of the system. This assessment aimed to gauge accuracy and completeness, identify variations, and comprehend implications. We also extract critical insights regarding the error and performance of both sensors within the examined operational conditions. The results show that DS18B20 present more accuracy and completeness than LM35.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
Series
Advances in Intelligent Systems Research
Publication Date
31 August 2024
ISBN
978-94-6463-496-9
ISSN
1951-6851
DOI
10.2991/978-94-6463-496-9_9How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Aissa Bensattalah
AU  - Youcef Belhadji
PY  - 2024
DA  - 2024/08/31
TI  - Evaluating Sensor-Derived Data Quality for IoT-based Temperature Monitoring
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 103
EP  - 116
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_9
DO  - 10.2991/978-94-6463-496-9_9
ID  - Bensattalah2024
ER  -