An animated, step-by-step visualization of the Ad-hoc On-Demand Distance Vector (AODV) routing protocol using Python, NetworkX, and Matplotlib.

AODV is a reactive routing protocol designed for mobile ad-hoc networks (MANETs). Unlike proactive protocols that maintain routing tables at all times, AODV only discovers routes on demand — when a source node needs to communicate with a destination.

The protocol operates in two phases:

Once the route is established, data can flow from source → target along the discovered path.

The simulation visualizes all three stages in sequence:

1. INITIALIZING — Source and target nodes are identified on the graph
2. RREQ Flood (cyan) — BFS expands outward from the source, lighting up each visited node and edge as the route request propagates
3. RREP Traceback (pink) — Once the target is found, the reply traces back hop-by-hop to the source
4. COMPLETE (gold) — The final discovered route is highlighted, with the full path displayed

pip install networkx matplotlib numpy

Open aodv_improved.ipynb in Jupyter and run all cells:

jupyter notebook aodv_improved.ipynb

The animation will render inline as an interactive HTML widget. To save as a GIF, run the final cell — this produces aodv_simulation.gif in the working directory.

All key parameters are set at the top of the notebook and easy to tweak:

The target node is automatically selected to be at least 4 hops from the source, ensuring a meaningful flood animation.

.
├── aodv_improved.ipynb     # Main simulation notebook
├── aodv_simulation.gif     # Exported animation
└── README.md               # This file

• Graph generation uses nx.fast_gnp_random_graph with automatic connectivity repair — isolated components are bridged so the graph is always fully connected.
• Route discovery is implemented as a proper BFS with depth tracking, matching AODV's hop-count semantics.
• Animation uses FuncAnimation with blit=True for performance. All dynamic edge layers use LineCollection.set_segments() for in-place updates — arrows=False is explicitly set to prevent matplotlib from returning FancyArrowPatch objects, which are incompatible with this update pattern.
• Edge weights (1–5) are assigned randomly and available for extension (e.g., weighted shortest path).

• RFC 3561 — AODV Specification
• NetworkX Documentation
• Matplotlib FuncAnimation