HydroSense: A Dual-Microcontroller IoT Framework for Real-Time Multi-Parameter Water Quality Monitoring with Edge Processing and Cloud Analytics

TL;DR

HydroSense addresses the need for real-time, affordable multi-parameter water quality monitoring in resource-constrained settings by combining six core parameters within a dual-microcontroller IoT framework. The architecture separates precision sensing (Arduino) from wireless connectivity and edge/cloud analytics (ESP32), enabling robust edge processing and cloud analytics at low cost. Key contributions include a 5-point pH calibration, temperature-compensated median-filtered TDS, galvanic DO sensing, Firebase cloud integration, and a total system cost of 32,983 BDT with high reliability over a 90-day validation, demonstrating an 85% cost reduction compared with commercial systems. The results show professional-grade accuracy across pH, DO, and TDS, together with strong cloud connectivity, making HydroSense suitable for education, aquaculture, and community monitoring while enabling scalable environmental sensing at scale.

Abstract

The global water crisis necessitates affordable, accurate, and real-time water quality monitoring solutions. Traditional approaches relying on manual sampling or expensive commercial systems fail to address accessibility challenges in resource-constrained environments. This paper presents HydroSense, an innovative Internet of Things framework that integrates six critical water quality parameters including pH, dissolved oxygen (DO), temperature, total dissolved solids (TDS), estimated nitrogen, and water level into a unified monitoring system. HydroSense employs a novel dual-microcontroller architecture, utilizing Arduino Uno for precision analog measurements with five-point calibration algorithms and ESP32 for wireless connectivity, edge processing, and cloud integration. The system implements advanced signal processing techniques including median filtering for TDS measurement, temperature compensation algorithms, and robust error handling. Experimental validation over 90 days demonstrates exceptional performance metrics: pH accuracy of plus or minus 0.08 units across the 0 to 14 range, DO measurement stability within plus or minus 0.2 mg/L, TDS accuracy of plus or minus 1.9 percent across 0 to 1000 ppm, and 99.8 percent cloud data transmission reliability. With a total implementation cost of 32,983 BDT (approximately 300 USD), HydroSense achieves an 85 percent cost reduction compared to commercial systems while providing enhanced connectivity through the Firebase real-time database. This research establishes a new paradigm for accessible environmental monitoring, demonstrating that professional-grade water quality assessment can be achieved through intelligent system architecture and cost-effective component selection.

Figures (7)

• Figure 1: HydroSense Three-Layer Distributed Architecture
• Figure 2: HydroSense System Sequence Diagram
• Figure 3: HydroSense Hardware Architecture with Distributed Processing
• Figure 4: pH Measurement Error Across Calibration Points
• Figure 5: DO Measurement Correlation with Reference Values