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Examining 5G Adoption: Effects on Network Traffic and Mobile Service Usage
Get PDFWith the launch of 5G and tremendous advancements in the traffic monitoring infrastructure of the modern telecom industry, we have an unprecedented opportunity to characterize the impact of this new wireless technology on end users. In this work, we conduct a first-of-its-kind analysis of 5G adoption over both space and time by leveraging massive measurement data at a national scale. We reveal the state of 5G incidence on total mobile traffic, its adoption across various services and devices, its temporal fluctuations, and the causes associated with the observed phenomena.Comunidad de MadridEuropean Union’s Horizon 2020Spanish Ministry of Digital Transformation and Public Service and the European Union-NextGenerationEU/PRTEU NextGenerationEU/PRTRTRUEinpres
Spatial and Temporal Exploratory Factor Analysis of Urban Mobile Data Traffic
Get PDFMobile data traffic is characterized by complex spatiotemporal fluctuations that are linked in entangled ways to the mobility and diverse activities of the mobile network subscribers. Unraveling such dynamics and understanding their root causes are challenging tasks that call for dedicated, complex data analysis tools. In this paper, we propose to employ Exploratory Factor Analysis (EFA) as a unified approach to identify both spatial and temporal structures hidden in the mobile data traffic. We provide a brief introduction to the EFA methodology, discuss how it can be tailored to a networking context, and outline its advantages in terms of versatility, unsupervised nature and interpretability of results. Experiments with large-scale measurement data collected in two urban regions demonstrate the effectiveness of the approach, which allows recognizing and explaining a variety of fundamental structures that underpin real-world spatiotemporal traffic dynamics. A thorough discussion of the results provides interesting insights, including that a reasonably small number of latent factors can describe well the majority of temporal and spatial structures observed in mobile traffic demands, providing valuable insights into key spatiotemporal patterns of population and becoming a valuable asset in understanding the attractiveness factors in urban areas.TRUEpu
Understanding the Price of Data in Commercial Data Marketplaces
Get PDFA large number of Data Marketplaces (DMs) have appeared in the last few years to help owners monetize their data, and data buyers optimize their marketing campaigns, train their ML models, and facilitate other data-driven decision processes. In this paper, we present a first of its kind measurement study of the growing DM ecosystem, focused on understanding which features of data are actually driving their prices in the market. We show that data products listed in commercial DMs may cost from few to hundreds of thousands of US dollars. We analyze the prices of different categories of data and show that products about telecommunications, manufacturing, automotive, and gaming command the highest prices. We also develop classifiers for comparing data products across different DMs, as well as a regression analysis for revealing features that correlate with data product prices of specific categories, such as update rate or history for financial data, and volume and geographical scope for marketing data.Horizon EuropeMinistry of Economic Affairs and Digital TransformationEuropean Union-NextGenerationEU/PRTRTRUEinpres
kaNSaaS: Combining Deep Learning and Optimization for Practical Overbooking of Network Slices
Get PDFCloud-native mobile networks pave the road for Network Slicing as a Service (NSaaS), where slice overbooking is a promising management strategy to maximize the revenues from admitted slices by
exploiting the fact they are unlikely to fully utilize their reserved resources concurrently. While seminal works have shown the potential of overbooking for NSaaS in simplistic cases, its realization is challenging in practical scenarios with realistic slice demands, where its actual performance remains to be tested. In this paper, we propose kaNSaaS, a complete solution for NSaaS management with slice overbooking that combines deep learning and classical optimization to jointly solve the key tasks of admission control and
resource allocation. Experiments with large-scale measurement data of actual tenant demands show that kaNSaaS increases the network operator profits by 300% with respect to NSaaS management strategies that do not employ overbooking, while outperforming by more than 20% state-of-the-art overbooking-based approaches.DAEMONUNICO 5G I+D 6G-CLARIONUNICO 5G I+D AEON ZEROTRUEpu
PRV-FCM: An extension of fuzzy cognitive maps for prescriptive modeling
Get PDFIn this paper, we present a methodology based on fuzzy cognitive maps (FCMs) and metaheuristic algorithms to generate prescriptive models, called PRescriptiVe FCM (PRV-FCM). FCMs are a set of concepts interrelated that describe the behavior of a system. This kind of modeling has been extensively used to build descriptive and predictive models. We propose an extension of FCMs to develop prescriptive models and support decision-making in different domains. This adaptation characterizes FCMs, using system and prescriptive concepts. After that, it uses a metaheuristic algorithm (in this case, we use a genetic algorithm) to optimize prescriptive concepts based on system concepts and the stability of the FCM. Our proposed prescriptive approach was implemented and tested in four scenarios where it demonstrated its capability to find solutions that lead to desired values for the variables of interest. Specifically, no significant differences were found between the values of the prescriptive variables in the datasets and those generated by PRV-FCM.TRUEpu
On the Effective Capacity of RIS-enabled mmWave Networks with Outdated CSI
Get PDFReconfigurable intelligent surfaces (RISs) have great potential to improve the coverage of mmWave networks; however, acquiring perfect channel state information (CSI) of a RISenabled mmWave network is very costly and should thus be done infrequently. At the same time, finding an optimal RIS configuration when CSI is outdated is challenging. To this end, this work aims to provide practical insights into the tradeoff between the outdatedness of the CSI and the system performance by using the effective capacity as analytical tool. We consider a RIS-enabled mmWave downlink where the base station (BS) operates under statistical quality-of-service (QoS) constraints. We find a closed-form expression for the effective capacity that incorporates the degree of optimism of packet scheduling and correlation strength between instantaneous and outdated CSI. Moreover, our analysis allows us to find optimal values of the signal-to-interference-plus-noise-ratio (SINR) distribution parameter and their impact on the effective capacity in different network scenarios. Simulation results demonstrate that better effective capacity can be achieved with suboptimal RIS configuration when the channel estimates are known to be outdated. It allows us to design system parameters that guarantee better performance while keeping the complexity and cost associated with channel estimation to a minimum.Ministerio de Asuntos Económicos y Transformación DigitalEuropean UnionTRUEpu
Your Code is 0000: An Analysis of the Disposable Phone Numbers Ecosystem
Get PDFShort Message Service (SMS) is a popular channel for online service providers to verify accounts and authenticate users registered to a particular service. Specialized applications, called Public SMS Gateways (PSGs), offer free Disposable Phone Numbers (DPNs) that can be used to receive SMS messages. DPNs allow users to protect their privacy when creating online accounts. However, they can also be abused for fraudulent activities and to bypass security mechanisms like Two-Factor Authentication (2FA). In this paper, we perform a large-scale and longitudinal study of the DPN ecosystem by monitoring 17,141 unique DPNs in 29 PSGs over the course of 12 months. Using a dataset of over 70M messages, we provide an overview of the ecosystem and study the different services that offer DPNs and their relationships. Next, we build a framework that (i) identifies and classifies the purpose of an SMS; and (ii) accurately attributes every message to more than 200 popular Internet services that require SMS for creating registered accounts. Our results indicate that the DPN ecosystem is globally used to support fraudulent account creation and access, and that this issue is ubiquitous and affects all major Internet platforms and specialized online services.tSpanish Ministry of ScienceHorizon EuropeTRUEpu
Federated learning approaches for fuzzy cognitive maps to support clinical decision-making in dengue
Get PDFFederated learning is a distributed machine learning approach developed to guarantee the privacy and security of data stored on local devices. In healthcare, specifically in diseases of public health interest such as dengue, it is necessary to develop strategies that guarantee such data properties. Therefore, the aim of this work was to develop three federated learning approaches for fuzzy cognitive maps for the prediction of mortality and the prescription of treatment of severe dengue. The validation of the approaches was performed on severe dengue datasets from two dengue endemic regions in Colombia. According to the results, the use of federated learning significantly improves the performance of models developed in centralized environments. Additionally, the use of federated learning allows guaranteeing the privacy and security of each client’s data due to the local training of the models. Federated learning is a useful tool in healthcare because it guarantees the privacy and security of patient data. Our results demonstrated the ability of aggregated models to predict mortality and prescribe treatment for severe dengue.TRUEpu
Compressive Spectral Video Sensing Using The Convolutional Sparse Coding Framework CSC4D
Get PDFSpectral Videos (SV) contain a scene’s spatial–spectral-time information. Just as with Spectral Images (SI), SVs require expensive sensing hardware, storage plus high frame ratios. Although Super Resolution techniques improve the quality of low-resolution SVs, Compressive Spectral Video Sensing (CSVS) senses high-quality SVs by extending the Compressive Sensing Image (CSI) techniques. CSI uses the universal Sparse Signal Representation (SSR) model for SVs and SIs despite the limited quality of the recovered signals. On the other hand, dictionaries synthesis models are used successfully for representing SIs, SVs, and in CSI. This work proposes the 4D convolutional sparse representation (CSC4D) for recovering full-resolution SV from CSVS measurements. It is based on a multidimensional formulation of the CSC model, profiting from its robustness without additional optical flow information. Extensive numerical simulations (two CSI architectures and noise models) show that the proposed CSC4D+CSVS improves the state-of-the-art in both quality and border sharpness by up to 1.5 dB.TRUEpu
Not Your Average App: A Large-scale Privacy Analysis of Android Browsers
No full textThe privacy-related behavior of mobile browsers has remained widely unexplored by the research community. In fact, as opposed to regular Android apps, mobile browsers may present contradicting privacy behaviors. On the one hand, they can have access to (and can expose) a unique combination of sensitive user data, from users' browsing history to permission-protected personally identifiable information (PII) such as unique identifiers and geolocation. On the other hand, they are in a unique position to protect users' privacy by limiting data sharing with other parties by implementing ad-blocking features.
In this paper, we perform a comparative and empirical analysis on how hundreds of Android web browsers protect or expose user data during browsing sessions. To this end, we collect the largest dataset of Android browsers to date, from the Google Play Store and four Chinese app stores. Then, we develop a novel analysis pipeline that combines static and dynamic analysis methods to find a wide range of privacy-enhancing (e.g., ad-blocking) and privacy-harming behaviors (e.g., sending browsing histories to third parties, not validating TLS certificates, and exposing PII---including non-resettable identifiers---to third parties) across browsers. We find that various popular apps on both Google Play and Chinese stores have
these privacy-harming behaviors, including apps that claim to be privacy-enhancing in their descriptions.
Overall, our study not only provides new insights into important yet overlooked considerations for browsers' adoption and transparency, but also that automatic app analysis systems (e.g., sandboxes) need
context-specific analysis to reveal such privacy behaviors.TRUEinpres