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

Evangelos Syrmos (), Vasileios Sidiropoulos, Dimitrios Bechtsis, Fotis Stergiopoulos, Eirini Aivazidou, Dimitris Vrakas, Prodromos Vezinias and Ioannis Vlahavas
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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

Sustainability, 2023, vol. 15, issue 3, 1-20

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.

Keywords: smart cities; IoT; LoRaWAN; LoRa; smart meters; water quality monitoring; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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