Javier Bustos-Jiménez

Along with the continuous growth of Internet usage, mobile users are becoming increasingly relevant as they are responsible for the largest percentage of web traffic. Consequently, a large and growing body of literature has employed cellular data to investigate a wide range of critical Internet-related concerns. However, accessing high-quality cellular datasets can be a challenge for research teams due to scarcity and restricted access. To address such a critical issue, we present a novel measurement methodology for mobile devices capable of passively monitoring Internet traffic in user space, providing comprehensive contextual information about the user and the network. Our proposed methodology is advantageous to collect inexpensive crowdsourced measurements from medium-sized sets of mobile users, and yet to obtain remarkable complementary insights on the target population. We show that the outcomes of this methodology are comparable and consistent with large-scale studies about critical topics such as human mobility patterns, traffic consumption trends, adoption of networking protocols, and penetration of mobile network technologies. Furthermore, we have open-sourced our measurement system to help the community explore potential research directions.

In this article we revise the football's performance score called PlayeRank, designed and evaluated by Pappalardo et al.\ in 2019. First, we analyze the weights extracted from the Linear Support Vector Machine (SVM) that solves the classification problem of "which set of events has a higher impact on the chances of winning a match". Here, we notice that the previously published results include the Goal-Scored event during the training phase, which produces inconsistencies. We fix these inconsistencies, and show new weights capable of solving the same problem. Following the intuition that the best team should always win a match, we define the team's quality as the average number of players involved in the game. We show that, using the original PlayeRank, in 94.13\% of the matches either the superior team beats the inferior team or the teams end tied if the scores are similar. Finally, we present a way to use PlayeRank in an online fashion using modified free analysis tools. Calculating this modified version of PlayeRank, we performed an online analysis of a real football match every five minutes of game. Here, we evaluate the usefulness of that information with experts and managers, and conclude that the obtained data indeed provides useful information that was not previously available to the manager during the match.

We briefly survey recent proposals that seek to capture in numerical terms the resilience and the robustness of a graph. After a brief introduction and the establishment of notation and terminology, we catalogue characterizations proposed in journal articles published within the last two decades. We then describe some of the numerous application areas for such characterizations. We experiment with implementations of numerous characteristics on several graph-generation models, after which we conclude with a discussion of open problems and future directions.

Due to its huge impact on the overall quality of service (QoS) of wireless networks, both academic and industrial research have actively focused on analyzing the received signal strength in areas of particular interest. In this paper, we propose the improvement of signal-strength aggregation with a special focus on Mobile Crowdsourcing scenarios by avoiding common issues related to the mishandling of log-scaled signal values, and by the proposal of a novel aggregation method based on interpolation. Our paper presents two clear contributions. First, we discuss the misuse of log-scaled signal-strength values, which is a persistent problem within the mobile computing community. We present the physical and mathematical formalities on how signal-strength values must be handled in a scientific environment. Second, we present a solution to the difficulties of aggregating signal strength in Mobile Crowdsourcing scenarios, as a low number of measurements and nonuniformity in spatial distribution. Our proposed method obtained consistently lower Root Mean Squared Error (RMSE) values than other commonly used methods at estimating the expected value of signal strength over an area. Both contributions of this paper are important for several recent pieces of research that characterize signal strength for an area of interest.

The Domain Name System (DNS) is a critical component of Internet infrastructure, as almost every activity on the Internet starts with a DNS query. Given its importance, there is increasing concern over its vulnerability to attacks and failures, as they can negatively affect all Internet-based resources. Thus, detecting these events is crucial to preserve the correct functioning of all DNS components, such as high-volume name servers for top-level domains (TLD). This article presents a near real-time Anomaly Detection Based on Prediction (AD-BoP) method, providing a useful and easily explainable methodology to effectively detect DNS anomalies. AD-BoP is based on the prediction of expected DNS traffic statistics, and could be especially helpful for TLD registry operators to preserve their services' reliability. After an exhaustive analysis, AD-BoP is shown to improve the current state-of-the-art for anomaly detection in authoritative TLD name servers.