85% network health
79% network health
Selecting analysis type
Other system health
Ping and wifi strength
MCP Server's Analysis
Initializing Arduino input stream and Firebase output stream
Model Validation
Check Network Health Status

NetPulse: Intelligent Edge AI for Real-Time Network Diagnostics

💡 Inspiration

Every minute of network downtime costs enterprises thousands of dollars, yet most monitoring systems are reactive, not preventive.
We wanted to change that by giving users, from everyday households to enterprise IT teams, a live, intelligent pulse on their network performance.
With the help of the T-Mobile 5G Network, a Raspberry Pi, and an Arduino hardware kit, we set out to build NetPulse, an embedded AI system that brings predictive network diagnostics to the edge.

🚀 What It Does

NetPulse is an end-to-end, real-time intelligent system that:
Collects environmental, motion, and system metrics from onboard sensors (temperature, humidity, CPU load, ping latency, packet loss, etc.)
Processes and classifies this data through an edge anomaly detection model running locally on the Raspberry Pi
Streams all metrics and AI insights to a mobile dashboard via Firestore and an MCP Server, providing instant visualization and personalized optimization tips

📱 Mobile App Features:

📊 Live graphs of network health metrics (latency, stability, Wi-Fi signal, etc.)
🧠 AI-powered diagnostics & recommendations (e.g., “Reduce router distance” or “High packet loss detected, potential interference”)
⚡ Realtime data sync between Arduino → Phone → MCP Server → Firestore

🛠️ How We Built It

🔧 Hardware: Arduino + environmental & IMU sensors to capture physical and performance parameters
🌐 Networking: T-Mobile 5G backbone for stable and fast data transmission
🐍 Backend: Python-based Firestore pipeline for structured logging and real-time analytics
🤖 AI Layer: Anomaly detection model trained to identify irregular system behavior and generate contextual diagnostics, integrated with an MCP Server running Claude Sonnet 4.5
📲 Frontend: Mobile app interface for live metric visualization and AI feedback

⚔️ Challenges We Ran Into

Building a robust data pipeline from Arduino → Mobile → MCP → Firestore with low latency
Managing real-time synchronization between Arduino and Raspberry Pi sensors
Ensuring consistent calibration and normalization for ML inference
Designing an ML model that performs well on small, noisy, and unlabeled datasets

🏆 Accomplishments We’re Proud Of

End-to-end integration across hardware, AI, and cloud, with a fully functional live demo
Successfully collected and parsed thousands of real-world sensor readings
Built a responsive mobile UI that visualizes real-time network health
Developed a modular ML inference architecture for seamless edge-to-cloud deployment

🧠 What We Learned

How system-level metrics like packet loss, CPU load, and Wi-Fi signal strength can predict instability before users notice
The importance of cleaning and scaling sensor data for reliable ML predictions
How to design for resilience, ensuring fault-tolerant data flow from embedded to cloud

🚀 What’s Next for NetPulse

Expanding the anomaly detection model with larger, labeled datasets
Integrating on-device model inference for fully offline network diagnostics
Building predictive analytics dashboards for enterprise IT teams
Exploring T-Mobile 5G APIs to automatically trigger network optimizations in real time