IoT and ML-Based Air Pressure Monitoring System for Environmental and Industrial Applications

This project develops a smart air pressure monitoring system designed for environmental and industrial applications. Using a high-precision air pressure sensor, the system measures barometric pressure in real time and integrates with an IoT platform for seamless data transmission, remote monitoring, and cloud storage. Capable of measuring a wide range of pressures (from 15 to 700 kPa), the system transmits data to an IoT dashboard, allowing users to observe and track air pressure variations remotely.

Machine learning algorithms analyze pressure data to detect anomalies, forecast trends, and provide actionable insights for weather predictions, industrial process optimization, or safety management. The system generates alerts for abnormal pressure changes, enabling timely responses. A mobile app offers real-time monitoring, historical trend analysis, and customized notifications to enhance decision-making in various scenarios.

Key Features:

• Accurate air pressure monitoring with IoT-enabled real-time data access.
• ML-powered anomaly detection and pressure trend predictions.
• Cloud-based storage for long-term data analysis and reporting.
• Mobile app for live data visualization, alerts, and historical trend analysis.

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 environmental and industrial monitoring.