<![CDATA[Water-quality deterioration requires timely and reliable monitoring, yet conventional laboratory-based approaches often face limitations in cost, sampling frequency, spatial coverage, and response time, particularly in resource-constrained environments. This study aimed to design, calibrate, and validate a low-cost Internet of Things sensor instrumentation system for continuous real-time water-quality monitoring. An experimental engineering design integrated multi-parameter sensing, embedded processing, wireless communication, laboratory calibration, reference-based comparison, field validation, data-transmission assessment, and threshold-based alert evaluation. The results demonstrated strong calibration performance across monitored parameters, although field accuracy varied according to sensor type and environmental conditions. Temperature and pH exhibited comparatively stable performance, whereas turbidity and dissolved oxygen showed greater field-related measurement variability. The system maintained high data completeness and communication reliability while successfully capturing short-duration water-quality changes that periodic sampling could potentially miss. Field validation confirmed that strong laboratory calibration alone did not guarantee equivalent performance under natural environmental conditions. The study concludes that low-cost IoT instrumentation provides a promising fit-for-purpose platform for continuous surveillance, temporal pattern detection, and early warning when supported by rigorous calibration and field validation. The proposed end-to-end framework strengthens the development of scalable, reliable, and context-sensitive water-quality monitoring systems for environmental management.]]>
Copyrights © 2026
