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An Intelligent Modular Water Monitoring IoT System for Real-Time Quantitative and Qualitative Measurements

Author

Listed:
  • Evangelos Syrmos

    (Department of Industrial Engineering and Management, International Hellenic University, 57001 Thessaloniki, Greece)

  • Vasileios Sidiropoulos

    (Department of Industrial Engineering and Management, International Hellenic University, 57001 Thessaloniki, Greece)

  • Dimitrios Bechtsis

    (Department of Industrial Engineering and Management, International Hellenic University, 57001 Thessaloniki, Greece)

  • Fotis Stergiopoulos

    (Department of Industrial Engineering and Management, International Hellenic University, 57001 Thessaloniki, Greece)

  • Eirini Aivazidou

    (Department of Industrial Engineering and Management, International Hellenic University, 57001 Thessaloniki, Greece)

  • Dimitris Vrakas

    (School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Prodromos Vezinias

    (Link Technologies SA, 57001 Thessaloniki, Greece)

  • Ioannis Vlahavas

    (School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

Abstract

This study proposes a modular water monitoring IoT system that enables quantitative and qualitative measuring of water in terms of an upgraded version of the water infrastructure to sustain operational reliability. The proposed method could be used in urban and rural areas for consumption and quality monitoring, or eventually scaled up to a contemporary water infrastructure enabling water providers and/or decision makers (i.e., governmental authorities, global water organization, etc.) to supervise and drive optimal decisions in challenging times. The inherent resilience and agility that the proposed system presents, along with the maturity of IoT communications and infrastructure, can lay the foundation for a robust smart water metering solution. Introducing a modular system can also allow for optimal consumer profiling while alleviating the upfront adoption cost by providers, environmental stewardship and an optimal response to emergencies. The provided system addresses the urbanization and technological gap in the smart water metering domain by presenting a modular IoT architecture with consumption and quality meters, along with machine learning capabilities to facilitate smart billing and user profiling.

Suggested Citation

  • Evangelos Syrmos & Vasileios Sidiropoulos & Dimitrios Bechtsis & Fotis Stergiopoulos & Eirini Aivazidou & Dimitris Vrakas & Prodromos Vezinias & Ioannis Vlahavas, 2023. "An Intelligent Modular Water Monitoring IoT System for Real-Time Quantitative and Qualitative Measurements," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2127-:d:1044596
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    References listed on IDEAS

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    1. Ke Zhang & Zhi Hu & Yufei Zhan & Xiaofen Wang & Keyi Guo, 2020. "A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine," Energies, MDPI, vol. 13(18), pages 1-19, September.
    2. Eirini Aivazidou & Georgios Banias & Maria Lampridi & Giorgos Vasileiadis & Athanasios Anagnostis & Elpiniki Papageorgiou & Dionysis Bochtis, 2021. "Smart Technologies for Sustainable Water Management: An Urban Analysis," Sustainability, MDPI, vol. 13(24), pages 1-14, December.
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    4. Pavel Masek & Martin Stusek & Ekaterina Svertoka & Jan Pospisil & Radim Burget & Elena Simona Lohan & Ion Marghescu & Jiri Hosek & Aleksandr Ometov, 2021. "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor," Data, MDPI, vol. 6(6), pages 1-20, June.
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    Cited by:

    1. Eirini Aivazidou & Naoum Tsolakis, 2023. "Water Management and Environmental Engineering: Current Practices and Opportunities," Sustainability, MDPI, vol. 15(15), pages 1-3, August.

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