Water quality monitoring for rural areas using a low-cost IoT-based sensor system
DOI:
https://doi.org/10.55873/apd27r75Keywords:
environmental monitoring, embedded systems, unsupervised learning, physicochemical sensors, rural areasAbstract
Limited access to continuous water quality monitoring technologies in rural areas hinders efficient water resource management and exposes the population to health risks. This study presents the design and implementation of a monitoring system based on low-cost sensors integrated into an Internet of Things (IoT) architecture, capable of real-time data transmission. The research was conducted in the rural community of Chuina, located in the Morales district of the San Martín region, Peru. An embedded system based on Arduino Mega was implemented, integrating sensors for pH, turbidity, temperature, total dissolved solids, and electrical conductivity, alongside communication and storage modules. A total of 2,880 automatic readings were collected over a 60-day monitoring period. The results showed that physicochemical parameters generally remained within regulatory limits; patterns were identified using descriptive statistics and K-Means unsupervised learning models. The study concludes that the system operates robustly and reliably, offering a replicable alternative for rural areas with limited infrastructure and serving as a useful tool for strengthening environmental monitoring.
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Copyright (c) 2026 Andrea Alexandra Garcia-Gonzales, Cristian García-Estrella
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