During early 2020, the SARS-CoV-2 virus rapidly spread worldwide, forcing many governments to impose strict lockdown measures to tackle the pandemic. This significantly changed people's mobility and habits, subsequently impacting how they use telecommunication networks. In this paper, we investigate the effects of the COVID-19 emergency on a UK Mobile Network Operator (MNO). We quantify the changes in users' mobility and investigate how this impacted the cellular network usage and performance. Our analysis spans from the entire country to specific regions, and geodemographic area clusters. We also provide a detailed analysis for London. Our findings bring insights at different geotemporal granularity on the status of the cellular network, from the decrease in data traffic volume in the cellular network and lower load on the radio network, counterposed to a surge in the conversational voice traffic volume.

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Abstract-Mobile Broadband (MBB) Networks are evolving at a fast pace, with technology enhancements that promise drastic improvements in capacity, connectivity, coverage, i.e., better performance in general. But how to measure the actual performance of a MBB solution? In this paper, we present our experience in running the simplest of the performance test: "speedtestlike" measurements to estimate the download speed offered by actual 3G/4G networks. Despite their simplicity, download speed measurements in MBB networks are much more complex than in wired networks, because of additional factors (e.g., mobility of users, physical impairments, diversity in technology, operator settings, mobile terminals diversity, etc.).We exploit the MONROE open platform, with hundreds of multihomed nodes scattered in 4 different countries, and explicitly designed with the goal of providing hardware and software solutions to run large scale experiments in MBB networks. We analyze datasets collected in 4 countries, over 11 operators, from about 50 nodes, for more than 2 months. After designing the experiment and instrumenting both the clients and the servers with active and passive monitoring tools, we dig into collected data, and provide insight to highlight the complexity of running even a simple speedtest. Results show interesting facts, like the occasional presence of NAT, and of Performance Enhancing Proxies (PEP), and pinpoint the impact of different network configurations that further complicate the picture. Our results will hopefully contribute to the debate about performance assessment in MBB networks, and to the definition of much needed benchmarks for performance comparisons of 3G, 4G and soon of 5G networks.

The accuracy of measurement-driven mobile coverage maps depends on the quality, density and pattern of the signal strength observations. Thus, identifying an efficient measurement data collection methodology is essential, especially when considering the cost associated with the measurement collection approaches (e.g., drive tests, crowd approaches). We propose ZipWeave, a novel measurement data collection and fusion framework for building efficient and reliable measurement-based mobile coverage maps. ZipWeave incorporates a novel nonuniform sampling strategy to achieve reliable coverage maps with reduced sample size. Assuming prior knowledge of the propagation characteristics of the region of interest, we first examine the potential gains of this non-uniform sampling strategy in different cases via a measurement-based statistical analysis methodology; this involves irregular spatial tessellation of the region of interest into sub-regions with internally similar radio propagation characteristics and sampling based on these subregions. We then present a practical form of ZipWeave nonuniform sampling strategy that can be used even without any prior information. In all our evaluations, we show that the ZipWeave non-uniform sampling approach reduces the samples by half compared to the common systematic-random sampling, while maintaining similar accuracy. Moreover, we show that the other key feature of ZipWeave to combine high-quality controlled measurements (that present limited geographic footprint similar to drive tests) with crowdsourced measurements (that cover a wider footprint) leads to more reliable mobile coverage maps overall.

I am immensely thankful to my families and relatives for their unconditional love and support through my school life. Last but not least, I would like to thank my better half, Ichalem, for her love and everything else. This thesis is dedicated to my mother Tena Yihunie -who is my role model of perseverance and confidence -and Adisualem -youngest brother, who was born three weeks after this research was started.

As the demand for mobile connectivity continues to grow, there is a strong need to evaluate the performance of Mobile Broadband (MBB) networks. In the last years, mobile "speed", quantified most commonly by data rate, gained popularity as the widely accepted metric to describe their performance. However, there is a lack of consensus on how mobile speed should be measured. In this paper, we design and implement MONROE-Nettest to dissect mobile speed measurements, and investigate the effect of different factors on speed measurements in the complex mobile ecosystem. MONROE-Nettest is built as an Experiment as a Service (EaaS) on top of the MONROE platform, an open dedicated platform for experimentation in operational MBB networks. Using MONROE-Nettest, we conduct a large scale measurement campaign and quantify the effects of measurement duration, number of TCP flows, and server location on measured downlink data rate in 6 operational MBB networks in Europe. Our results indicate that differences in parameter configuration can significantly affect the measurement results. We provide the complete MONROE-Nettest toolset as open source and our measurements as open data.

In this paper, we propose the BGP Visibility Toolkit, a system for detecting and analyzing anomalous behavior in the Internet. We show that interdomain prefix visibility can be used to single out cases of erroneous demeanors resulting from misconfiguration or bogus routing policies. The implementation of routing policies with BGP is a complicated process, involving fine-tuning operations and interactions with the policies of the other active ASes. Network operators might end up with faulty configurations or unintended routing policies that prevent the success of their strategies and impact their revenues. As part of the Visibility Toolkit, we propose the BGP Visibility Scanner, a tool which identifies limited visibility prefixes in the Internet. The tool enables operators to provide feedback on the expected visibility status of prefixes. We build a unique set of ground-truth prefixes qualified by their ASes as intended or unintended to have limited visibility. Using a machine learning algorithm, we train on this unique dataset an alarm system that separates with 95% accuracy the prefixes with unintended limited visibility. Hence, we find that visibility features are generally powerful to detect prefixes which are suffering from inadvertent effects of routing policies. Limited visibility could render a whole prefix globally unreachable. This points towards a serious problem, as limited reachability of a non-negligible set of prefixes undermines the global connectivity of the Internet. We thus verify the correlation between global visibility and global connectivity of prefixes.