Characterizing Large-Scale Mobile Traffic Measurements for Urban, Social and Networks Sciences

Abstract

Over the last few decades, it is difficult to pinpoint a technological advancement that shifted the daily life of the world’s population in a more disrupting way than mobile phones and their applications. Their ubiquitousness has reshaped global behaviors and routines, transforming portable devices into the essential and on-the-go personal computer. Mobile phones enable communication, information access, and entertainment with little to no location constraints, thanks to their connectivity to the internet through a pervasive radio access network infrastructure. Of course, this seamless mobile access was not always a given, and decades of research, development and technology integration were needed to reach today’s high-capacity support for broadband and low-latency mobile services. From the first generations of mobile networks offering only on-the-go voice and text to the current fourth and fifth generations supporting high-resolution on-demand video streaming and low-latency cloud applications, every new release contributed to transforming mobile phones into essential items. With the rise in popularity of mobile applications in smartphones, any company or developer could release their own application, giving access to their product to consumers anywhere. Advancements in mobile networks meant an increase in data transfer capacities, leading users to be more comfortable utilizing their smartphones for tasks anywhere and at any time. The sucess of mobile technologies also signifies that the patterns of usage captured by mobile operators reflect in a rich and detailed way the endeavors of their vast user population. Due to this reason, the data collected in operational mobile networks has today become a primary source of information for research in networking and beyond. Early research utilized analysis of mobile network traffic as feedback for the mobile operator itself, as a way to understand the spatiotemporal dynamics of the operational demands in the network, and how this could be leveraged to improve network deployments and operation according to consumption patterns. A second and broader direction lies in interdisciplinary research, seeking to explore how these measurements could be used to understand populations and urban environment dynamics. This drives the need of research oriented towards networks data science: the study of tools and methodologies capable of handling large-scale measurements, asserting the quality and precision of the collected data in reflecting reality, as well as developing tools capable of extracting insights and making the vastness of collected information useful for analysis. This thesis is a step in the direction of establishing said tools and methodologies, as well as showcasing several potential directions that mobile network measurement can support in interdisciplinary research domains. The first part of this thesis presents a full contextualization of networks data science, expanding on the problem of the ever-growing scale of collected sets and presenting the many different fields that have been explored over the years, from classical network engineering applications to the study of populations, epidemics, socioeconomic and people’s movement and transportation across cities. These studies are not possible without a well-established routine of data collection and processing, which is also discussed in the first part of this thesis. The second part of the manuscript presents the original contributions provided by the thesis. Four chapters explore different directions where the collected data can be leveraged to derive new insights. First, an overview of the adoption of new technologies provided by the mobile network operator, with new findings into how changes in their traffic patterns may happen according to these new capabilities. Second, an exploration of how mobile consumption patterns and demands can be utilized to better understand the space within cities, with new methodologies presenting how both city-wide and location- specific insights can be gained just by looking at the traffic being consumed within base stations of the network. Third, a look into how special events may impact mobile networks, as such occurrences affect directly how users interact with their smartphones. It becomes important for the network operator to be able to extract insights and understand how these variations in traffic demand across time, space, and applications affect the functioning of the network, as well as these insights can be used by lawmakers to understand how these events affect populations. Lastly, a study characterizing session- level measurements to derive insights used to generate synthetic data sets containing new dynamics, generating simple models that can be utilized by anyone interested in research within mobile networks to test and validate their data-driven solutions. In summary, the age of pervasive digital services leaves researchers with oceans of data and information to be explored in many different areas, with mobile networks shaping into a major source of rich information to guide innovation in both cutting edge research and technology development. This thesis guides the reader through the current state of affairs, showcasing the current opportunities opened by mobile network data and also presenting potential future directions that can be pursed in the following years.