NetPulse AI
This project "NetPulse AI" focuses on network health monitoring and fault prediction using a Raspberry Pi and AI-powered analysis. The Raspberry Pi collects real-time system performance data—including CPU usage, temperature, signal strength, and packet loss—via sensors and serial communication. A VBScript automates data logging, storing readings in an Excel file without user intervention. The script detects anomalies and logs detailed error messages to help diagnose potential failures. This setup enables continuous monitoring without manual oversight, ensuring that network performance metrics are recorded efficiently. Once the data is logged, users can upload the Excel file to an AI-powered chatbot hosted on Hugging Face. The chatbot, built using Gradio, Pandas, and Zephyr-7B-Beta, processes the data to provide insights under three key sections: Future Performance Prediction, Risk Analysis and Potential Issues, and Preventive Actions and Recommendations. The AI detects patterns, predicts potential failures, and offers optimization strategies for network stability. Users can also download a detailed report and interact with the chatbot for additional troubleshooting and performance improvement suggestions.
Category tags:
"Clear presentation and demo shows immense value add for a very relevant and costly problem. "
Surabhi Nayak
Privacy Engineer
"Fantastic end to end workflow with a great demo. Would be good to understand how the LLM would handle large amounts of logs as the solution scales"
Thomas Blake
CTO
"The project demonstrates a clear understanding of network monitoring challenges and offers a practical solution by integrating hardware and AI-driven analysis. Leveraging a Raspberry Pi for real-time data collection showcases resourcefulness and adaptability. Automating data logging through VBScript to create seamless user experiences is commendable as well. Integrating AI analysis adds substantial value. The interactive chatbot interface and downloadable reports further enhance usability. Resourceful approach to hardware + sophisticated data analysis. Well done."
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Anastasia Nedayvoda
"A good integration of different network devices - however, a bit more thought on streaming the data from one to the other without manual upload is what would enable this to be feasible for the user. Understand on computational burden but consider also user burden. This would be most relevant to an IT administrator without network experience. Overall good project. "
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Maria Antonia Bravo
For Innovators & Creators