AquaSentinel: An Intelligent Water Quality Monitoring System Based on IoT and Machine Learning

AquaSentinel is an end-to-end intelligent water quality monitoring solution that integrates ESP32 microcontrollers, LoRa wireless communication, Firebase cloud platform, and machine learning algorithms to achieve real-time monitoring, intelligent analysis, and visual display. It aims to address issues such as poor real-time performance, limited coverage, and insufficient data analysis in traditional water quality monitoring, and build a complete closed loop from data collection to intelligent decision-making.

Nowadays, water pollution is a serious issue. Fields such as industrial wastewater, agricultural non-point source pollution, and urban water supply safety require efficient and reliable monitoring methods. Traditional water quality monitoring has pain points like poor real-time performance, limited coverage, and insufficient data analysis capabilities. AquaSentinel is designed to address these needs, enabling real-time monitoring of multiple key indicators and intelligent assessment of water quality status.

Perception Layer

Deploy pH, turbidity, temperature, TDS, conductivity, methane, ammonia, and ultrasonic sensors, integrated with GPS positioning and mobile status detection.

Transmission Layer

Adopt LoRa technology for long-distance low-power communication. Data flow: Sensor → ESP32 → LoRa transmission → LoRa reception → Python gateway → Firebase database.

Platform Layer

Use Firebase real-time database, which provides real-time synchronization, offline support, security rules, and easy integration capabilities.

Application Layer

Responsive web dashboard with real-time charts, alarm system, and report generation functions.

Machine Learning Module

Build models based on Scikit-learn to implement water quality classification, trend prediction, and reuse recommendations. The model is deployed at the gateway layer to achieve edge computing.

1. Urban Water Supply Monitoring: Deployed at key nodes of water supply networks to monitor indicators of factory water and pipe network water in real time, triggering alarms when anomalies occur.
2. Industrial Wastewater Supervision: Installed at factory sewage outlets to monitor whether emissions meet standards, with data uploaded to the environmental protection department's platform.
3. Agricultural Irrigation Management: Monitor the water quality of irrigation water sources and recommend whether it is suitable for irrigation.
4. Aquaculture Optimization: Monitor parameters such as water temperature and pH to provide scientific data for farmers.

• End-to-end Integrity: Covers the entire chain from hardware to software; full-stack thinking supports IoT projects.
• Modular Design: Each module is loosely coupled, making independent upgrades and maintenance easier.
• Edge Intelligence: Deploy models at the gateway layer to achieve local inference, reducing cloud dependency and improving response speed.
• User Experience Priority: Responsive interface and report generation functions focus on end-user needs.

AquaSentinel realizes the transformation from traditional water quality monitoring to intelligent management. Through the integration of multiple technologies, it achieves the upgrade from "seeing" to "understanding" and from "post-processing" to "pre-warning". In the future, it will play an important role in smart cities, ecological protection, precision agriculture, and other fields, and it is also an excellent learning example for IoT full-stack development.