Smart and Fast AI-Driven WiFi Test Automation
Smart and Fast AI-Driven WiFi Test Automation
The objective of this project is to revolutionize WiFi testing by integrating AI-driven automation to optimize test execution, enhance anomaly detection, and ensure seamless device interoperability. Traditional WiFi testing methods are slow, labor-intensive, and fail to adapt dynamically to real-world network conditions. As WiFi networks become more complex with 5G, IoT, and high-density deployments, current approaches struggle to detect and resolve performance issues efficiently.
This project aims to develop an AI-powered test automation system that enables:
Intelligent Test Case Generation – AI dynamically adapts test scenarios based on network behavior.
Automated Device Classification – Machine learning categorizes devices for optimized testing.
Real-Time Anomaly Detection – AI identifies network failures and performance degradation before they impact users.
Scalable Cloud-Based Execution – Enables remote, high-volume testing across different environments.
Optimized Test Execution via Reinforcement Learning – Reduces test execution time
By integrating supervised learning (Random Forest, Decision Trees), unsupervised learning (clustering), reinforcement learning, and neural networks, the system will enhance WiFi network performance, reduce costs, and improve quality assurance for manufacturers, telecom providers, and IoT solution developers.
This AI-driven approach will set a new industry standard for WiFi testing, reducing inefficiencies, eliminating manual intervention, and accelerating time-to-market for next-generation wireless solutions
- D2 Future Internet Use-case scenarios / Test environment
Country: Turkey
