MONROE-Nettest: A Configurable Tool for Dissecting Speed Measurements in Mobile Broadband Networks

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.Comment: 6 pages, 3 figures, submitted to INFOCOM CNERT Workshop 201

Exploration of Different Time Series Models for Soccer Athlete Performance Prediction

Professional sports achievements combine not only the individual physical abilities of athletes but also many modern technologies in areas such as medicine, equipment production, nutrition, and physical and mental health monitoring. In this work, we address the problem of predicting soccer players’ ability to perform, from subjective self-reported wellness parameters collected using a commercially deployed digital health monitoring system called PmSys. We use 2 years of data from two Norwegian female soccer teams, where players have reported daily ratings for their readiness-to-play, mood, stress, general muscle soreness, fatigue, sleep quality, and sleep duration. We explore various time series models with the goal of predicting readiness, employing both a univariate approach and a multivariate approach. We provide an experimental comparison of different time series models, such as purely recurrent models, models of mixed recursive convolutional types, ensemble of deep CNN models, and multivariate versions of the recurrent models, in terms of prediction performance, with a focus on detecting peaks. We use different input and prediction windows to compare the accuracy of next-day predictions and next-week predictions. We also investigate the potential of using models built on data from the whole team for making predictions about individual players, as compared to using models built on the data from the individual player only. We tackle the missing data problem by various methods, including the replacement of all gaps with zeros, filling in repeated values, as well as removing all gaps and concatenating arrays. Our case study on athlete monitoring shows that a number of time series analysis models are able to predict readiness with high accuracy in near real-time. Equipped with such insight, coaches and trainers can better plan individual and team training sessions, and perhaps avoid over training and injuries

Automatic thumbnail selection for soccer videos using machine learning

Thumbnail selection is a very important aspect of online sport video presentation, as thumbnails capture the essence of important events, engage viewers, and make video clips attractive to watch. Traditional solutions in the soccer domain for presenting highlight clips of important events such as goals, substitutions, and cards rely on the manual or static selection of thumbnails. However, such approaches can result in the selection of sub-optimal video frames as snapshots, which degrades the overall quality of the video clip as perceived by viewers, and consequently decreases viewership, not to mention that manual processes are expensive and time consuming. In this paper, we present an automatic thumbnail selection system for soccer videos which uses machine learning to deliver representative thumbnails with high relevance to video content and high visual quality in near real-time. Our proposed system combines a software framework which integrates logo detection, close-up shot detection, face detection, and image quality analysis into a modular and customizable pipeline, and a subjective evaluation framework for the evaluation of results. We evaluate our proposed pipeline quantitatively using various soccer datasets, in terms of complexity, runtime, and adherence to a pre-defined rule-set, as well as qualitatively through a user study, in terms of the perception of output thumbnails by end-users. Our results show that an automatic end-to-end system for the selection of thumbnails based on contextual relevance and visual quality can yield attractive highlight clips, and can be used in conjunction with existing soccer broadcast pipelines which require real-time operation

Soccer on Social Media

In the era of digitalization, social media has become an integral part of our lives, serving as a significant hub for individuals and businesses to share information, communicate, and engage. This is also the case for professional sports, where leagues, clubs and players are using social media to reach out to their fans. In this respect, a huge amount of time is spent curating multimedia content for various social media platforms and their target users. With the emergence of Artificial Intelligence (AI), AI-based tools for automating content generation and enhancing user experiences on social media have become widely popular. However, to effectively utilize such tools, it is imperative to comprehend the demographics and preferences of users on different platforms, understand how content providers post information in these channels, and how different types of multimedia are consumed by audiences. This report presents an analysis of social media platforms, in terms of demographics, supported multimedia modalities, and distinct features and specifications for different modalities, followed by a comparative case study of select European soccer leagues and teams, in terms of their social media practices. Through this analysis, we demonstrate that social media, while being very important for and widely used by supporters from all ages, also requires a fine-tuned effort on the part of soccer professionals, in order to elevate fan experiences and foster engagement

An Open Dataset of Operational Mobile Networks

Mobile networks have become ubiquitous and the primary meansto access the Internet, and the traffic they generate has rapidlyincreased over the last years. The technology and service diversityin mobile networks call for extensive and accurate measurementsto ensure the proper functioning of the networks and rapidly spotimpairments. However, the measurement of mobile networks iscomplicated by their scale, and, thus, expensive, especially due tothe diversity of deployments, technologies, and web services. Inthis paper, we present and provide access to the largest open in-ternational mobile network dataset collected using the MONROEplatform spanning six countries, 27 mobile network operators, and120 measurement nodes. We use them to run measurements tar-geting several web services from January 2018 to December 2019,collecting millions of TCP and UDP flows using these commercialmobile networks. We illustrate the data collection platforms and de-scribe some of the main experiments. Besides a high-level overviewof the dataset, we provide two practical use cases. First, we showhow our data can be used as a proxy for web service performance.Second, we study the content delivery infrastructure of Facebook

Speedtest-Like Measurements in 3G/4G Networks: The MONROE Experience

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: "speedtest-like" 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"

Evaluation Framework for Real-Time Adaptive 360-Degree Video Streaming over 5G Networks

The end-to-end distribution of real-time 360-degree video needs to be evaluated in a wholesome manner, considering all aspects from video capture to encoding, delivery, and playback, as well as timely and appropriate analytics, with a focus on end-user Quality of Experience (QoE). This requires a measurement framework which allows for the collection of metrics from multiple dimensions at the same time. In this work, we propose such a framework for evaluating real-time adaptive 360\degree video streaming over an experimental Fifth Generation (5G) network, which allows for the investigation of different aspects of the end-to-end video delivery chain

Smittestopp Backend

An efficient backend solution is of great importance for any large-scale system, and Smittestopp is no exception. The Smittestopp backend comprises various components for user and device registration, mobile app data ingestion, database and cloud operations, and web interface support. This chapter describes our journey from a vague idea to a deployed system. We provide an overview of the system internals and design iterations and discuss the challenges that we faced during the development process, along with the lessons learned. The Smittestopp backend handled around 1.5 million registered devices and provided various insights and analyses before being discontinued a few months after its launch

Poster: QoE-Based Analysis of Real-Time Adaptive 360-Degree Video Streaming

We propose a QoE-based analysis approach for real-time adaptive 360-degree video streaming measurements, focusing on the correlation between objective video metrics and subjective end-user scores