ESP-NOW MAC Drop Analysis Dashboard

This interactive web application models MAC-level packet drop probability for wireless communication using ESP-NOW over IEEE 802.11. It includes models for both saturated and unsaturated traffic conditions using the Bianchi model and Aloha approximation, with RF-level bit error probability factored in.

Features

• Selectable Traffic Models:

  • Saturated (Bianchi)
  • Unsaturated (Bianchi)
  • Unsaturated (Aloha)

• Adjustable parameters:

  • Bitrate (1 to 54 Mbps)
  • Payload size (1 to 250 bytes)
  • Publish frequency (Hz) — disabled for saturated model
  • RF Bit Error Rate (BER)

• Real-time interactive graph of overall drop percentage vs. number of devices.

• Mathematical formula summary displayed for educational/reference use.

Models Implemented

1. Saturated Bianchi: Solves the classic Bianchi model with collision probabilities and contention backoff.

2. Unsaturated Bianchi: Extends the model by incorporating an activity factor based on publish rate.

3. Unsaturated Aloha: Simplified model using exponential approximation: P_success = exp(-2G) where G = n × pub_freq × T_packet

4. RF Success Probability: P_success_RF = (1 - BER)^(payload × 8)

5. Overall Drop: Drop(%) = 100 × [1 - (P_success_MAC × P_success_RF)]

Run Locally

Prerequisites:

• Python 3.7+
• Dash, Plotly, NumPy, SciPy

Install dependencies: pip install -r requirements.txt

Run the App: python espnow_mac_drop_analysis.py

Visit http://127.0.0.1:8050 in your browser.

Use Case

Useful for analyzing:

• ESP-NOW based P2P communication systems
• MAC-level scalability for IoT/robotics devices
• Impact of RF BER and payload size on drop rates

License

MIT License – Use freely with attribution.

Preview