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 .

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.

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

Open Access • Peer Reviewed Article

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

2025-11-30