First introduced in 2013, QUIC protocol has been in constant development and it has gained great importance over time, currently being standardized by the Internet Engineering Task Force (IETF). Previous experiments have shown that the performance of QUIC can be of particular interest for lossy wireless networks. Thus, it becomes relevant to measure and profile QUIC traffic from mobile devices in the wild. This paper analyzes the adoption of QUIC from a mobile development perspective, by using crowdsourced data from real Android users during the normal use of their mobile devices. The results obtained show a significant increase in the number of Android apps using QUIC and evidence newer efforts of companies to adopt this protocol. To the best of the authors' knowledge, this is the first work focused on analyzing and profiling QUIC traffic from crowdsourced network measurements taken by mobile end-user devices.

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.

We propose stochastic splitting algorithms for solving large-scale composite inclusion problems involving monotone and linear operators. They activate at each iteration blocks of randomly selected resolvents of monotone operators and, unlike existing methods, achieve almost sure convergence of the iterates to a solution without any regularity assumptions or knowledge of the norms of the linear operators. Applications to image recovery and machine learning are provided.