IoT WSN-Based Water Quality Monitoring System with AI Sensors¶
Water Quality & Sensors • Est. CapEx: $3,000 USD • Lifespan: 7–15 Years
Technical Overview¶
A smart framework integrating IoT, wireless sensor networks, and AI machine learning models for real-time water quality monitoring. It uses physical sensors for standard parameters and AI models to predict indicators like E. coli without dedicated electronic sensors, targeting water treatment plants and municipal systems.
Key Specifications & Engineering Parameters¶
| Parameter | Specification |
|---|---|
| Category | Water Quality & Sensors |
| Capital Cost (CapEx) | $3,000 USD |
| Power / Energy | Solar PV / Thermal |
| Target Scale | Village / Community (200–1,000+ people) |
| Standard Lifespan | 7–15 Years |
| Technology Maturity | R&D |
| Primary Mechanism | Sensor nodes measure parameters (pH, conductivity, turbidity, etc.) and transmit data wirelessly to a central platform. AI algorithms, such as AdaBoost, are trained on historical sensor data to predict non-sensor indicators (e.g., E. coli) by correlating them with physically measured variables, enabling proactive contamination alerts. |
Cross-Reference & Interactive Tools¶
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