AI Fire Monitoring System 🛰️🔥

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An AI-powered IoT-based fire detection and monitoring system that provides real-time alerts, data analytics, and automated reporting to improve fire safety and emergency response efficiency.

🚀 Overview

The AI Fire Monitoring System integrates IoT sensors and machine learning algorithms to detect heat, gas, smoke, humidity, air quality and using satellite capture image for detect forest fire in real time. Once a potential fire is identified, the system instantly sends alerts to relevant authorities such as BOMBA JBPM officers, enabling faster response and reducing the risk of major damage.

🧠 Key Features

🔍 AI Detection: Uses computer vision and sensor data for accurate fire identification

🌐 IoT Integration: Connects multiple monitoring devices through a centralized network

📊 Real-Time Dashboard: Displays live status, fire locations, and historical data

📱 Automated Alerts: Sends notifications via mobile and web platforms

🧾 Reporting Module: Generates analytical reports for post-incident review

☁️ Cloud Storage: Stores sensor data and event logs securely

⚙️ Tech Stack

Frontend: Web (Django templates or SPA frontend) Backend: Python (Django) Database: PostgreSQL (manage with pgAdmin) AI & ML: (see ml_engine for model code)

IoT Hardware: ESP32, Flame Sensor, Smoke Sensor, DHT11 (Temperature)

📦 Installation

Prerequisites

Before installing the AI Fire Monitoring System, ensure you have the following installed:

• Python 3.8+ – Download Python
• Git – Download Git
• PostgreSQL – Download PostgreSQL
• pgAdmin (optional, for DB management) – pgAdmin
• Node.js & npm (optional, only if you run a separate SPA frontend)

Backend Setup (Django)

1. Clone the repository:

   git clone https://github.com/SNFASA/AI-Fire-Monitoring-System.git
   cd AI-Fire-Monitoring-System

2. Create a virtual environment and activate it:

   python -m venv .venv
   # On Windows (PowerShell): .venv\Scripts\Activate.ps1
   # On Windows (cmd): .venv\Scripts\activate
   # On macOS / Linux: source .venv/bin/activate

3. Install Python dependencies:

   pip install -r requirements.txt

4. Configure environment variables:

   • Create a .env file in the project root (the project reads env vars via settings)
   • Add PostgreSQL connection info and Django secret

   SECRET_KEY=your_secret_key
   POSTGRES_DB=your_db_name
   POSTGRES_USER=your_db_user
   POSTGRES_PASSWORD=your_db_password
   POSTGRES_HOST=localhost
   POSTGRES_PORT=5432
   # Or provide a single DATABASE_URL, e.g.:
   # DATABASE_URL=postgres://user:pass@localhost:5432/dbname

   You can manage the database with pgAdmin or psql.

5. Create the PostgreSQL database (example using psql):

   createdb -U your_db_user your_db_name

6. Run database migrations and create a superuser:

   python manage.py migrate
   python manage.py createsuperuser

7. Start the backend server (development):

   python manage.py runserver

Frontend (Web)

This project includes a Django-based web frontend (templates). If you use a separate SPA (React/Vue/Angular), run its setup in the frontend/ folder if present:

1. Install frontend dependencies (optional SPA):

   npm install
   npm run build    # or: npm start for dev

2. Serve static files (Django): ensure STATIC_ROOT is configured and run collectstatic for production.

Common Commands

• Run tests:

  pytest

• Start IoT simulator:

  python simulator.py

• Check dependencies:

  pip-audit

🎯 How to Use

Starting the System

1. Launch the backend server:

   python manage.py runserver

   The API will be available at http://localhost:8000

2. Start the mobile app:

   flutter run

Key Features Guide

🔍 Fire Detection Dashboard

• Open the app and navigate to the Dashboard
• View real-time sensor data from connected IoT devices
• Monitor temperature, humidity, smoke levels, and air quality
• Visual indicators show fire risk status (Green/Yellow/Red)

🚨 Receiving Alerts

• Enable notifications in app settings
• Alerts are triggered automatically when fire is detected
• Tap on alert to view detailed information and location
• Share alerts with emergency contacts

📊 Viewing Analytics

• Navigate to the Analytics section
• View historical fire incident reports
• Generate custom reports by date range
• Export data for analysis in CSV format

🗺️ Satellite Image Monitoring

• Use the Map view to see satellite-captured fire locations
• Zoom in for detailed area monitoring
• Track multiple fire incidents simultaneously
• View weather conditions affecting fire spread

⚙️ Configuration

• Go to Settings to configure:
  • Alert preferences and notification channels
  • Sensor sensitivity thresholds
  • Connected IoT devices management
  • Emergency contact information

Common Commands

• Run tests:

  pytest

• Run linting:

  python -m pylint src/

• Start IoT simulator:

  python simulator.py

• Check dependencies:

  pip-audit

🔄 Development Methodology

This project follows the Agile methodology, allowing iterative development across six main phases:

Planning – Define project scope, goals, and risk assessment

Analysis – Gather and document system requirements

Design – Develop architecture, database, and UI prototypes

Implementation – Code system modules and integrate AI models

Testing – Perform functional, performance, and user acceptance tests

Deployment – Deploy to live servers and train users

📅 Project Timeline

Duration: Oct 6, 2025 – Jul 6, 2026

Method: Agile with 2-week sprints (≈18 sprints)

Platforms: Android & iOS

Phases Overlap: Designed for continuous iteration and improvement

🤝 Contributors

Project Leader: [SNFASE]

Supervisor: [Dr. MOHD ZANES BIN SAHID]

Institution: Universiti Tun Hussein Onn Malaysia (UTHM)

📫 Contact

For inquiries or collaboration: 📧 [ai230046@student.uthm.edu.my] 🌍 [https://www.linkedin.com/in/syed-nabil-b266341bb/]