README.md

metadata

license: mit
title: >-
  Energy-Efficient Wildlife Tracking Using a Splay Tree Inspired Clustering
  Policy and Vision-Aided Analytics
sdk: docker
colorFrom: blue
colorTo: red
pinned: true
short_description: This project implements a cohesive system that combines **Sp

Energy-Efficient Wildlife Tracking Using a Splay Tree Inspired Clustering Policy and Vision-Aided Analytics

This project implements a cohesive system that combines Splay Network Algorithms for efficient sensor communication with Real-Time AI Computer Vision for wildlife tracking. It is designed to simulate a high-tech conservation network where "Edge Nodes" (sensors) capture low-resolution data, and a central "Server" restores and analyzes it.

Key Features

1. Splay Network Simulation (The Backbone)

• Dynamic Clustering: Visualizes how sensor nodes self-organize into clusters to save energy.
• Splay-Tree Optimization: Uses splay algorithms to determine optimal routing paths to the gateway.
• Energy Modeling: Simulates battery drain (green -> yellow -> red -> dead) and tracks network lifespan.
• Live Dashboard: Monitors network health, tracking active vs. dead nodes and downtime.

2. AI Wildlife Analytics (The Intelligence)

• Simulated Edge-to-Server Pipeline:
  • Edge Step: Takes raw video frames and downscales them to 128px "thumbnails" (mimicking low-bandwidth transmission).
  • Restoration Step: Uses SwinIR (simulated) on the server to upconvert thumbnails back to 640px.
• Object Detection: Utilizing YOLOv8 (Nano) to identify animals in the restored feed.
• Tracking: Implementing DeepSORT to assign unique IDs to animals and track their movement across frames.

How to Run

Option 1: Live Demo (Hugging Face)

This project is deployed on Hugging Face Spaces for high-performance AI inference. View Live Demo

Option 2: Run Locally

1. Clone the Repository:

   git clone https://github.com/john-osborne-j/-splay-network.git
   cd splay-network
   

2. Install Dependencies:

   python -m venv venv
   venv\Scripts\activate  # Windows
   pip install -r requirements.txt
   

3. Run the App:

   python app.py
   

4. Access: Open http://localhost:5000 in your browser.

🛠 Tech Stack

• Backend: Python, Flask, Gunicorn
• Frontend: HTML5, Vanilla CSS (Glassmorphism), JavaScript (Canvas API)
• AI/ML: PyTorch, Ultralytics YOLOv8, DeepSORT, OpenCV
• Deployment: Docker, Hugging Face Spaces

Project Structure

• app.py: Main Flask server endpoints.
• simulation.py: Logic for Splay Network nodes, energy, and routing.
• detection.py: AI pipeline (SwinIR Restoration + YOLO Detection + DeepSORT).
• static/: CSS styling and JavaScript simulation loop.
• templates/: HTML frontend interface.

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Created by John Osborne And Nishanth A Powered by Splay Algorithms & Modern AI