IoT and ML-Based Magnetic Field Monitoring and Anomaly Detection System

This project develops a smart magnetic field monitoring system using a digital Hall sensor. Designed for applications in industrial machinery, security systems, and scientific research, the system integrates with an IoT platform to enable real-time monitoring, data logging, and cloud-based analytics.

Machine learning algorithms analyze magnetic field data to detect anomalies, predict trends, and provide actionable insights. The system generates instant alerts when irregular magnetic activity is detected, ensuring timely responses. A mobile app offers live monitoring, historical data visualization, and personalized notifications, making it a versatile tool for magnetic field analysis and safety management.

Key Features:

• Real-time magnetic field detection and monitoring using IoT technology.
• ML-powered anomaly detection and predictive analytics for trend forecasting.
• Cloud-based storage for long-term data analysis and reporting.
• Mobile app for live updates, alerts, and historical trend visualization.

This project includes source code, complete hardware setup, detailed analysis, and a 200-page project report in soft copy, making it an excellent choice for students exploring IoT and ML applications in industrial and scientific environments.