AI for Connectivity Hackathon II: Building Resilient Networks 

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VIBOT

hassaan04

danial

Ureedhussain

This project leverages machine learning to optimize TVWS base station locations using datasets on schools, towers, demographics, elevation, and spectrum. The dashboard provides key insights, including predicted interference, failure risks, and AI-driven recommendations to enhance network performance. A dedicated Potential Location page simulates TVWS availability on an interactive map, identifying optimal base station placements based on factors like population density and elevation. Additionally, a Procurement page, currently in progress, will track infrastructure and equipment acquisition for future deployments. By integrating historical data and predictive analytics, this system enables proactive decision-making to improve TVWS coverage and reliability.

This project uses machine learning to optimize TVWS base stations, predicting interference, failures, and providing AI recommendations. It features a dashboard, a simulation map, and procurement tracking.

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