Internet Quality Barometer (IQB): A preliminary data-driven evaluation of the IQB framework

2026-06-09

AI Summary

This study addresses the challenge of transforming raw network speed test data into interpretable and comparable metrics for internet quality assessment by proposing the first scalable Internet Quality Benchmark (IQB) framework. The framework employs a data-driven approach to map network performance indicators to representative application scenarios and aggregates them into a normalized composite score within the 0–1 interval. Key contributions include the release of a fully open-source toolchain enabling IQB computation at multiple granularities—national, regional, and city levels—and a sensitivity analysis that identifies critical parameters affecting score stability. This work achieves an integrated alignment among measurement data, application requirements, and holistic evaluation, thereby establishing a principled foundation for systematic internet quality benchmarking.

Abstract

The Internet Quality Barometer (IQB) framework was designed to transform raw Internet measurement data into actionable insights about Internet quality. Specifically, the framework maps raw speed test measurements to network requirements (e.g., throughput, latency), maps these requirements to representative Internet use cases (such as video streaming or web browsing), and finally aggregates performance across use cases into a single IQB score. The IQB score is a composite index ranging from 0 to 1, intended to capture overall Internet quality in a way that is both interpretable and comparable across locations. We implemented the IQB framework in practice by developing an open-source IQB library and a prototype web application. These tools enabled us to compute IQB scores at scale, including global estimates aggregated at the level of countries, regions, and cities. In this report we conduct a preliminary sensitivity analysis of the IQB framework, investigating how different parameter choices affect the resulting IQB scores, identifying which parameters the framework is most sensitive to, and highlighting cases that may lead to outliers or potentially misleading results.

Problem

• Internet quality
• composite index
• sensitivity analysis
• performance measurement
• data-driven evaluation

Innovation

• Internet Quality Barometer
• composite index
• data-driven evaluation
• network performance mapping
• sensitivity analysis