INLQOSD: Wireless Network Quality of Service and Security Dataset

The Challenge

Modern wireless networks—whether Wi-Fi or 5G—must balance two critical elements: quality of service (QoS) and security. Poor QoS can degrade user experience, while insufficient security exposes networks to vulnerabilities. Researchers and developers often struggle to gather reliable, real-world data to study this balance, making it challenging to optimize wireless networks for both performance and protection.

How can network researchers simulate wireless environments and collect high-quality data to develop smarter, safer, and more efficient wireless systems?

How It Works

The INL Quality of Service Dataset (INLQOSD) provides a powerful software suite for simulating, testing, and analyzing wireless network traffic. It enables researchers to generate network traffic data and train machine learning models to improve QoS in Wi-Fi and 5G environments.

Key features include:

1. Network Simulation and Testing

    ◦ Supports Wi-Fi networks using OpenWRT.

    ◦ Simulates 5G networks using Open5GS and UERANSIM.

2. Traffic Generation and Data Collection

    ◦ Runs tests simulating network activity, including downloads and uploads with varying numbers of concurrent users.

    ◦ Uses tcpdump to capture network traffic, storing only summarized datasets to streamline analysis.

3. Machine Learning Integration

    ◦ Tools for training machine learning models on collected data to identify patterns and optimize network QoS.

4. Open-Source Codebase

    ◦ Developed in collaboration with Idaho National Laboratory (INL) and Idaho State University (ISU) under Statement of Work SOW-20246.

    ◦ Designed for public release, enabling broad access to researchers and developers.

Key Advantages

• Real-World Data Simulation: Simulates Wi-Fi and 5G networks under realistic conditions, generating datasets for meaningful analysis of QoS and security.

• Optimized Data Collection: Captures summarized network traffic data for efficient storage and analysis without overwhelming data volumes.

• Research and Development Focus: Supports the development of machine learning models to improve QoS and strengthen wireless security simultaneously.

Market Applications

• Academic and Industrial Research: Universities and research labs can analyze network QoS and security to drive advancements in wireless technology.

• Wireless Network Development: Developers of Wi-Fi and 5G systems can test and refine network performance under simulated traffic scenarios.

• Machine Learning Model Training: AI and ML researchers can use the dataset to train models that optimize QoS while maintaining robust network security.

• Telecommunications Testing: Telecom providers can simulate and analyze real-world network behaviors to enhance system resilience and performance.

This software is open source and available at no cost. Download now by visiting the product's GitHub page.