Abstract
Crowdsourced network measurements (CNMs) are becoming increasingly popular as they assess the performance of a mobile network from the end user’s perspective on a large scale. Here, network measurements are performed directly on the end-users’ devices, thus taking advantage of the real-world conditions end-users encounter. However, this type of uncontrolled measurement raises questions about its validity and reliability. The problem lies in the nature of this type of data collection. In CNMs, mobile network subscribers are involved to a large extent in the measurement process, and collect data themselves for the operator. The collection of data on user devices in arbitrary locations and at uncontrolled times requires means to ensure validity and reliability. To address this issue, our paper defines concepts and guidelines for analyzing the precision of CNMs; specifically, the number of measurements required to make valid statements. In addition to the formal definition of the aspect, we illustrate the problem and use an extensive sample data set to show possible assessment approaches. This data set consists of more than 20.4 million crowdsourced mobile measurements from across France, measured by a commercial data provider.
Highlights
Mobile internet is increasingly used in every-day life, and end users expect to have the same quality as when they are at home
All areas with sufficient measurements are colored in blue, as the precision here corresponds to the target value of at least δ∗ = 100 kbit/s, i.e., the actual average throughput lies within an interval of
When using crowdsourced network measurements (CNMs), network operators, regulators, and big data companies are faced with the challenge of making valid statements out of measurements in uncontrolled environments of the crowd
Summary
Mobile internet is increasingly used in every-day life, and end users expect to have the same quality as when they are at home. The evaluations of the two providers differ significantly Possible reasons for this include the different spatial distribution of the respective measurement data, as well as the number of measurements collected and the used evaluation methods. This shows that it is important to define guidelines on how to evaluate the validity of CNM data. We tackle the following aspect for analyzing the validity of crowdsourced mobile network measurements: we consider the precision of an evaluation, in particular, the precision of a certain metric such as the downlink throughput.
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