An IoT-Enabled Smart Water Quality Monitoring System Using Low-Cost Sensors and Cloud Analytics

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 1, Issue 5, pp. 07-12, November 2025.

https://doi.org/10.58482/ijersem.v1i5.2

This work is licensed under a Creative Commons Attribution 4.0 International License.

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An IoT-Enabled Smart Water Quality Monitoring System Using Low-Cost Sensors and Cloud Analytics

G Ravi Kumar

C. Sushama

Department of Computer Science and Engineering, Mohan Babu University (erstwhile Sree Vidyanikethan Engineering College), Tirupathi, AP, India.

Abstract: The increasing demand for reliable and continuous water quality assessment has led to the rapid adoption of Internet of Things (IoT)-based monitoring systems in environmental management. This study presents the design and development of a low-cost, IoT-enabled smart water-quality monitoring system capable of measuring key parameters, including pH, temperature, dissolved oxygen (DO), turbidity, and electrical conductivity, in real time. The proposed framework integrates affordable sensors, a microcontroller-based acquisition unit, and a cloud analytics platform for data visualization, threshold detection, and remote accessibility. Prior studies have demonstrated the potential of IoT solutions in enhancing energy efficiency, autonomous sensing, predictive optimization, and large-scale environmental monitoring. Building on these advancements, the present system employs optimized data-transmission cycles, efficient calibration routines, and MQTT/HTTP protocols to achieve reduced power consumption and improved reliability for long-term field deployment. The results highlight the system’s capability to provide continuous water quality assessment, support predictive modelling, and enable timely decision-making for water resource management. This approach offers a scalable solution suitable for applications in lakes, rivers, aquaculture systems, and smart-city infrastructure.

Keywords: IoT, water quality monitoring, cloud analytics, low-cost sensors, turbidity, pH, dissolved oxygen, MQTT, smart environmental monitoring.

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This article is part of Volume 1, Issue 5 (November 2025)