Designed and developed an intelligent water-level monitoring system integrating IoT sensors with machine learning. Implemented real-time leak detection and automated response mechanism to shut off water flow and prevent waste or damage. Built end-to-end pipeline including data collection (Arduino), backend processing (Python/PHP), and database management (SQL). Developed as a self-initiated project with no budget, demonstrating strong problem-solving and engineering skills. Successfully accepted and presented at an AI Conference in Malaysia.

Water-Level Monitoring System

An intelligent IoT water-level monitoring system with machine learning-powered leak detection and automated response mechanism.

Project Overview

This system integrates IoT sensors with machine learning to provide real-time water-level monitoring and automated leak detection. The project was successfully accepted and presented at an AI Conference in Malaysia.

Key Features

Real-time Monitoring: Continuous water-level tracking with IoT sensors
Leak Detection: ML-powered anomaly detection to identify leaks early
Automated Response: Automatic shut-off mechanism to prevent water waste and damage
End-to-End Pipeline: Complete system from data collection to database management
Self-Initiated: Developed independently with zero budget, showcasing strong problem-solving abilities

Technical Architecture

Data Collection: Arduino-based sensors for real-time water-level measurements
Backend Processing: Python and PHP for data processing and ML model inference
Database: SQL database for storing historical data and system logs
ML Models: Machine learning algorithms for pattern recognition and leak prediction

Achievements

Successfully presented at AI Conference in Malaysia
Research paper accepted and published
Demonstrated practical application of IoT and ML integration