IoTrace

A powerful IoT evidence extraction and management system specifically designed for WearOS smartwatches.

Team Members

• Pranav Hemanth - PES1UG23CS433 GitHub
• Pranavjeet Naidu - PES1UG23CS586 GitHub
• Shailja Shaktawat - PES1UG23CS534 GitHub
• Nishant K Holla - PES1UG23CS401 GitHub

Overview

IoTrace is a specialized platform designed for extracting, analyzing, and managing evidence from IoT devices, with a particular focus on WearOS smartwatches. The platform leverages advanced AI capabilities to provide detailed insights into device behavior and patterns.

Key Features

• 🔍 Smart Log Extraction: Automated extraction of logs from WearOS devices
• 📊 Interactive Visualization: Real-time data visualization with charts and graphs
• 🤖 AI-Powered Analysis: Intelligent log analysis using Google's Gemini AI
• 📄 Evidence Reports: Generate comprehensive PDF reports for legal documentation
• 🔒 Secure Storage: Enterprise-grade security with Supabase
• 📱 Responsive Design: Mobile-first interface for easy access

Getting Started

Prerequisites

• Node.js 18.x or later
• npm or yarn
• Supabase account
• Google Cloud account (for Gemini AI)
• WearOS device (tested with Samsung Watch5 Pro)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/IoTrace.git
cd IoTrace

2. Install dependencies:

cd frontend
npm install

3. Set up environment variables:

cp .env.example .env.local

4. Configure your environment variables in .env.local:

NEXT_PUBLIC_SUPABASE_URL=your_supabase_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key
NEXT_PUBLIC_GEMINI_API_KEY=your_gemini_api_key

5. Start the development server:

npm run dev

Project Structure

IoTrace/
├── frontend/                    # Next.js frontend application
│   ├── app/                    # Next.js app directory (pages and routes)
│   │   ├── cases/             # Case management pages
│   │   ├── dashboard/         # Dashboard pages
│   │   ├── login/             # Authentication pages
│   │   └── signup/            # User registration pages
│   ├── components/            # Reusable UI components
│   │   └── ui/                # Base UI components
│   ├── lib/                   # Utility functions and services
│   │   ├── supabase.ts        # Supabase client configuration
│   │   ├── gemini.ts          # Gemini AI integration
│   │   └── pdf-generator.ts   # PDF report generation
│   └── public/                # Static assets
├── backend/                    # Backend services
│   ├── api/                   # API endpoints
│   │   ├── auth/             # Authentication endpoints
│   │   ├── cases/            # Case management endpoints
│   │   └── logs/             # Log processing endpoints
│   ├── services/             # Business logic services
│   │   ├── log-extractor/    # Log extraction service
│   │   ├── data-processor/   # Data processing service
│   │   └── report-generator/ # Report generation service
│   └── utils/                # Utility functions
├── supabase/                  # Supabase configuration
│   ├── migrations/           # Database migrations
│   ├── seed/                 # Seed data
│   └── types/                # TypeScript types
├── scripts/                   # Utility scripts
│   ├── setup.sh              # Project setup script
│   └── deploy.sh             # Deployment script
├── docs/                      # Documentation
│   ├── api/                  # API documentation
│   └── setup/                # Setup guides
├── tests/                     # Test files
│   ├── unit/                 # Unit tests
│   ├── integration/          # Integration tests
│   └── e2e/                  # End-to-end tests
├── .env.example              # Example environment variables
├── package.json              # Project dependencies
├── tsconfig.json             # TypeScript configuration
└── README.md                 # Project documentation

Features in Detail

Log Extraction

• Automated extraction of system logs from WearOS devices
• Support for multiple log types and formats
• Real-time log streaming and processing

Data Visualization

• Interactive time-series charts
• Component distribution analysis
• Customizable data views
• Export capabilities

AI Analysis

• Pattern recognition in device behavior
• Anomaly detection
• Component interaction analysis
• Time-based pattern analysis

Evidence Reports

• Professional PDF report generation
• AI-enhanced insights
• Customizable report templates
• Secure storage and sharing

Security

• End-to-end encryption for sensitive data
• Role-based access control
• Secure file storage
• Audit logging

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Next.js for the React framework
• Supabase for the backend infrastructure
• Google Gemini AI for the AI capabilities
• Recharts for the visualization components

Support

For support, please open an issue in the GitHub repository or contact the maintainers.

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Made with ❤️ by the IoTrace Team