Design and Implementation of a Low-Cost IoT Model for Real-Time Water Quality Monitoring in Resource-Constrained Environments
TL;DR
The model integrates low-cost sensors to measure pH, turbidity, temperature, and total dissolved solids (TDS), along with an embedded microcontroller for real-time data acquisition and processing, and demonstrates reliable real-time monitoring with minimal measurement deviations.
Abstract
Ensuring access to safe drinking water remains a major challenge in developing regions, where traditional water quality monitoring methods are often manual, time-consuming, and unable to provide real-time insights. These limitations delay detection of contamination and hinder timely intervention. The proposed model integrates low-cost sensors to measure pH, turbidity, temperature, and total dissolved solids (TDS), along with an embedded microcontroller for real-time data acquisition and processing. A four-layer architecture consisting of sensing, edge processing, GSM-based communication, and cloud visualisation was implemented. A mixed-methods approach was used, combining quantitative performance evaluation with qualitative stakeholder feedback. The model demonstrated reliable real-time monitoring with minimal measurement deviations (±0.1 pH, ±0.3 NTU, ±10 ppm TDS, ±0.2°C). Stable data transmission was maintained despite intermittent connectivity. Stakeholders reported improved usability, faster decision-making, and high confidence in system outputs. The results highlight the feasibility of deploying low-cost IoT devices for continuous water quality monitoring in resource-constrained environments.