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.

翻译：全球水资源危机亟需经济实惠、精确且实时的水质监测解决方案。依赖人工采样或昂贵商业系统的传统方法难以应对资源受限环境下的可及性挑战。本文提出HydroSense，一种创新的物联网框架，它将pH值、溶解氧（DO）、温度、总溶解固体（TDS）、估算氮含量及水位六个关键水质参数集成到一个统一的监测系统中。HydroSense采用新颖的双微控制器架构：利用Arduino Uno进行采用五点校准算法的精密模拟测量，并利用ESP32实现无线连接、边缘处理及云端集成。该系统实现了先进的信号处理技术，包括用于TDS测量的中值滤波、温度补偿算法以及鲁棒的错误处理机制。为期90天的实验验证展示了卓越的性能指标：在0至14范围内pH值精度达±0.08单位，DO测量稳定性在±0.2 mg/L以内，在0至1000 ppm范围内TDS精度达±1.9%，云端数据传输可靠性达99.8%。在总实现成本为32,983孟加拉塔卡（约合300美元）的条件下，HydroSense相比商业系统实现了85%的成本降低，同时通过Firebase实时数据库提供了增强的连接性。本研究为可及的环境监测建立了新范式，证明通过智能系统架构与高性价比的组件选择，可以实现专业级的水质评估。