The same, only different: Contrasting mobile operator behavior from crowdsourced dataset

Crowdsourcing mobile network performance evaluation is rapidly gaining popularity, with new applications aiming to deliver more accurate and reliable results every day. From the perspective of end-users, these utilities help them estimate the performance of their service provider in terms of throughput, latency and other key performance indicators of the network. In this paper, we build ORCA: Operator Classifier, a Machine Learning (ML) based framework to define and determine the behavior of Mobile Network Operators (MNOs) from crowdsourced datasets. We investigate whether one can differentiate MNOs by using crowdsourced end-to-end network measurements. We consider different performance metrics (e.g. Download (DL)/Upload (UL) data rate, latency, signal strength) and study the impact of them individually but also collectively on differentiating MNOs. We use RTR Open Data, an open dataset of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), to characterize the three major mobile native operators and two virtual operators in Austria. Our results show that ORCA can be used to identify patterns between various mobile systems and disclose their differences from the end-user perspective.