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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2025 / Cilt: 18

IoT-Based drinking water quality monitoring system

IoT tabanlı içilebilir su kalitesi izleme sistemi

Ali Kavurur(MEHMET AKIF ERSOY UNIVERSITY)0000-0003-1251-9574
Pages
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Publication Date
2026-10-14
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This approach offers an energy-efficient and sustainable IoT architecture capable of performing real-time, low-cost water quality assessments while minimizing hardware resource usage.

📌 3 Key Takeaways

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Objective & Hypothesis:To enable on-site and continuous monitoring of water resources, a low-cost, real-time Internet of Things (IoT) water quality monitoring system based on the Wemos D1 Mini ESP32 microcontroller has been developed to simultaneously track pH, turbidity, total dissolved solids (TDS), and temperature parameters.
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Key Findings & Data:The primary objective of the system is to provide instant and efficient assessment by running the mathematical Water Quality Index (WQI) directly on the hardware, rather than relying on complex Artificial Intelligence (AI) algorithms that require high processing power, extensive memory, and consume significant energy.
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Conclusion & Impact:This approach offers an energy-efficient and sustainable IoT architecture capable of performing real-time, low-cost water quality assessments while minimizing hardware resource usage.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Wemos D1 mini ESP32 · Nesnelerin interneti IoT · Su kalitesi izleme

Abstract

To enable on-site and continuous monitoring of water resources, a low-cost, real-time Internet of Things (IoT) water quality monitoring system based on the Wemos D1 Mini ESP32 microcontroller has been developed to simultaneously track pH, turbidity, total dissolved solids (TDS), and temperature parameters. The primary objective of the system is to provide instant and efficient assessment by running the mathematical Water Quality Index (WQI) directly on the hardware, rather than relying on complex Artificial Intelligence (AI) algorithms that require high processing power, extensive memory, and consume significant energy. Analog and digital data collected from sensors are processed within the Wemos D1 microcontroller and instantly converted into a WQI score, with the water’s potability status displayed in real time on a local OLED screen. In addition to real-time monitoring, the system transmits the data to the cloud-based ThingSpeak platform for future predictive analytics, large-scale data mining, and machine learning studies. This approach offers an energy-efficient and sustainable IoT architecture capable of performing real-time, low-cost water quality assessments while minimizing hardware resource usage.

Özet

Su kaynaklarının yerinde ve sürekli izlenmesi amacıyla, pH, bulanıklık, toplam çözünmüş katı madde (Total Dissolved Solids:TDS) ve sıcaklık parametrelerini eş zamanlı olarak takip eden, Wemos D1 Mini ESP32 mikrodenetleyicisi tabanlı düşük maliyetli ve gerçek zamanlı bir Nesnelerin İnternetini (Internet of Things :IoT) kullanan su kalitesi izleme sistemi geliştirilmiştir. Sistemin temel amacı; yüksek işlem gücü, yoğun bellek ve enerji tüketen karmaşık Yapay Zeka (YZ) algoritmaları yerine, matematiksel Su Kalitesi İndeksini (Water Quality Index: WQI) doğrudan donanım üzerinde çalıştırarak anlık ve verimli bir değerlendirme ortaya koymaktır. Sensörlerden alınan analog ve dijital veriler, Wemos D1 mikrodenetleyicisi bünyesinde işlenerek anlık olarak WQI skoruna dönüştürülmekte ve suyun içilebilirlik durumu yerel bir OLED ekran üzerinde gecikmesiz olarak gösterilmektedir. Sistem, anlık takibin yanı sıra verileri gelecekteki tahmine dayalı analizler, geniş ölçekli veri madenciliği ve makine öğrenmesi çalışmaları için bulut tabanlı ThingSpeak platformuna aktarmaktadır. Geliştirilen bu yaklaşım, donanım kaynaklarını asgari düzeyde tutarak gerçek zamanlı ve düşük maliyetli su kalitesi değerlendirmesi yapabilen, enerji verimli ve sürdürülebilir bir IoT mimarisi sunmaktadır.

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Cite This Article

Kavurur, A., *, A. K. (2026). IoT-Based drinking water quality monitoring system. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 18. https://doi.org/10.28948/ngumuh.1957047

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