⚛️ EmION — Research-Grade ION-DTN Simulation

Built with 💻 by Team DOMinators

EmION is an authentic, research-grade Delay-Tolerant Networking (DTN) simulation framework. Unlike other simulators, EmION uses the real NASA-JPL ION-DTN C-engine and Contact Graph Routing (CGR) for 100% protocol authenticity — with a state-of-the-art visual dashboard and per-node ML module attachment.

✨ Key Features

🌍 Platform Support

🚀 Installation

From PyPI

pip install "emion[dashboard]"

From Source

git clone https://github.com/info-gallary/emion.git
cd emion
pip install -e ".[dashboard]"

For Testing

pip install -e ".[dashboard,test]"

Full Environment (Linux/WSL)

chmod +x install.sh && ./install.sh

🛰️ Usage

Launch the Dashboard

emion dashboard

Open http://localhost:8420 in your browser.

CLI

emion info       # ION system status
emion dashboard  # Start the visual dashboard

Run Tests

pytest -q

For a reviewer-friendly quick pass:

pytest -q tests/test_scenario_import.py tests/test_dashboard_scenarios.py

Run Automated Experiments

python3 scripts/run_experiments.py \
  --config examples/experiments/scalability_matrix.json \
  --output-dir artifacts/experiments_matrix

This writes per-scenario CSVs plus aggregate SVG plots under artifacts/experiments_matrix/.

📖 Documentation

• Quickstart
• Architecture Overview
• Dashboard API Reference
• Custom XML Scenario Format
• Troubleshooting
• Reproducibility Guide

🧪 XML Scenario Import

Upload any CORE .xml scenario directly in the dashboard:

1. Open Scenario Engine in the sidebar
2. Drag-and-drop your .xml file (or click Browse Files)
3. A Scenario Briefing appears in the telemetry panel with a plain-English summary
4. Click ▶ Start to begin the simulation

See examples/ion_mars/ for a sample scenario.

For a reproducibility-oriented validation workflow, see docs/reproducibility.md.

Experiment configs are available in examples/experiments/, including:

• scalability_matrix.json
• robustness_matrix.json
• ml_matrix.json
• mars_actual_schedule.json

📚 Citation

Citation metadata is available in CITATION.cff.

🔌 Per-Node ML Modules

Attach ML inference modules (anomaly detectors, security analyzers) to specific nodes. The node automatically feeds all telemetry and bundle data to the attached module.

Quick Start

1. Start your module (a FastAPI server with /health and /analyze endpoints)
2. In the dashboard sidebar → ML MODULES
3. Select a node → pick module type → enter the API URL → Attach
4. Watch the canvas LED indicators (🟢/🟡/🔴) change in real-time

Module API Contract

Your module must expose:

• GET /health → {"status": "ok"}
• POST /analyze → receives {"payload": ..., "metadata": {...}}, returns {"is_anomaly": bool, "score": float}

See examples/anomaly_detector/ for a reference implementation.

🐳 Docker

docker build -t emion .
docker run -p 8420:8420 emion

For a containerized experiment run:

docker compose run --rm --profile experiments runner

📦 Project Structure

emion/
├── emion/                  # Core Python package (BPv7, CGR, Dashboard)
│   ├── core/               # ION engine, scenarios, WLAN mobility
│   ├── dashboard/          # FastAPI server + static UI
│   ├── plugins/            # Module base classes (APIPlugin)
│   └── pyion/              # C-extension bindings for ION
├── examples/               # User-facing research examples
│   ├── ion_mars/           # CORE XML scenario (5 WLAN nodes)
│   ├── anomaly_detector/   # Sample ML inference module
│   └── core_services/      # CORE integration scripts
├── scripts/                # Maintenance & developer utilities
├── docs/                   # Coding guide & usage notebook
├── tests/                  # Test suite
├── Dockerfile              # Self-contained build
├── pyproject.toml          # Package config (v1.0)
├── LICENSE                 # MIT
└── CONTRIBUTING.md         # How to contribute

🤝 Contributing

See CONTRIBUTING.md for guidelines.

📜 License

MIT License — see LICENSE.

Authentic. Professional. Space-Ready. ⚛️

Built by Team DOMinators using the real NASA-JPL ION-DTN engine.