IMDEA Networks Institute Digital Repository
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Characterization of mobile network services to assess the impact of network slicing in a nationwide scenario
Get PDFSeveral businesses are nowadays becoming more and more aware of the potential that lies beneath Big Data. From social media ‘titans’ and healthcare companies, to the mobile industry that propelled them, 5th Generation mobile network (5G) will be a fundamental factor that will
drive our new reality. Current mobile service usage has been explored for data-driven organizational growth in the touristic sector, as it allows forecasting hotel occupancy rates or targeting customers. However, mobile data is continuously increasing, and therefore it is enormously important to analyze such data for networking purposes.
Previous Ericsson Mobility Reports claimed that by the end of 2022 the total monthly traffic associated with mobile devices would be 77 exabytes (EB), representing 20% of the total Internet Protocol (IP) traffic around the world. They also declare that 50 EB/month would come from 2nd
Generation mobile network (2G), 3rd Generation mobile network (3G), and 4th Generation mobile network (4G) devices. In terms of 5G subscriptions, it is expected to reach up to 2.8 billion subscriptions globally by the end of 2025, accounting for about 30% of total mobile subscriptions. Experts stake out that by the year 2020, 1.7 megabytes of data will be generated every second
for every person on the planet, forcing the network to evolve and adapt to challenging new demands.
Network providers not only deal with the deployment of the required resources to support this growth, but also the potential newcomers into the business, and the stringent conditions of the distinct services to be provided. One of the features proposed to face this dilemma is using the network slicing technique. It allows to transform and orchestrate a 5G network by creating
multiple logical instances (i.e., slices) on top of it, while Big Data would provide the specifications of the services’ traffic dynamics to be served. In this way, operators achieve the best allocation of resources.
This thesis contributes to the ongoing Network Slicing research, assessing a nationwide scenario. Our results show mobile traffic similarities and differences across time, space, and frequency domains, whereas we intend for distinct service clusterizations that would enhance the network efficiency in terms of resource management. For instance, we show that benefits are achieved when considering the top 10 consuming Network Slice (NS). In addition, we could observe mobile service similarities in the spatial domain, while the spectral and time domains open the door for wavelets uncertainty, where we point future directions to address this research branch. Moreover, we propose two data-driven algorithms that shed light on the trade-off between complexity and multiplexing efficiency derived from the network slice specifications, both exhibiting promising performances (e.g., leading to a new architecture for traffic balancing in the cloud and edge clusters, with 60% and 400% gain in efficiency respectively and 1/3 of dedicated resources).Telematics EngineeringUniversidad Carlos III de Madrid, Spainpu
Poster: Integration between Home Automation and Visible Light Communications
Get PDFIn the near future, the Internet of Things (IoT) will take a predominant role in home automation and a reliable technology must satisfy the high demand on data traffic, energy consumption and reliability. This poster reveals a novel solution for home automation that relies on visible light communication.FALSEpu
Application of learning analytics to study the accuracy of self-reported working patterns in self-regulated learning questionnaires
No full textTime management strategies and self-regulated learning have received much attention. Nevertheless, there is a lack of research in terms of student self-awareness related to their own self-regulation and autonomy. This study aims to validate whether students are self-conscious about their working patterns and their time management. In order to do so, several parameters like work sessions regularity or invested time have been computed and analyzed. This invested time is measured thanks to a data-gathering tool that collects events generated by students. Results show that students are, in general, self-aware of their own working patterns and that the regularity of their weekly work time is correlated with their final marks.TRUEpu
Tracking the deployment of TLS 1.3 on the Web: A story of experimentation and centralization
Get PDFTransport Layer Security (TLS) 1.3 is a redesign of the Web’s most important security protocol. It was standardized in August 2018 after a four year-long, unprecedented design process involving many cryptographers and industry stakeholders. We use the rare opportunity to track deployment, uptake, and use of a new mission-critical security protocol from the early design phase until well over a year after standardization. For a profound view, we combine and analyze data from active domain scans, passive monitoring of large networks, and a crowd-sourcing effort on Android devices. In contrast to TLS 1.2, where adoption took more than five years and was prompted by severe attacks on previous versions, TLS 1.3 is deployed surprisingly speedily and without security concerns calling for it. Just 15 months after standardization, it is used in about 20% of connections we observe. Deployment on popular domains is at 30% and at about 10% across the com/net/org top-level domains (TLDs). We show that the development and fast deployment of TLS 1.3 is best understood as a story of experimentation and centralization. Very few giant, global actors drive the development. We show that Cloudflare alone brings deployment to sizable numbers and describe how actors like Facebook and Google use their control over both client and server endpoints to experiment with the protocol and ultimately deploy it at scale. This story cannot be captured by a single dataset alone, highlighting the need for multi-perspective studies on Internet evolution.pu
A Mixture Density Channel Model for Deep Learning-Based Wireless Physical Layer Design
Get PDFMachine learning is a highly promising tool to design the physicallayer of wireless communication systems, but it usually requiresthat a channel model is known. As data rates increase and wirelesstransceivers become more complex, the wireless channel, hard-ware imperfections, and their interactions become more difficult tomodel and compensate explicitly. New machine learning schemesfor the physical layer do not require an explicit model butimplic-itly learnthe end-to-end link including channel characteristics andnon-linearities of the system directly from the training data.In this paper, we present a novel neural network architecturethat provides anexplicitstochastic channel model, by learning theparameters of a Gaussian mixture distribution from real channelsamples. We use this channel model in conjunction with an au-toencoder for physical layer design to learn a suitable modulationscheme. Since our system learns an explicit model for the channel,we can use transfer learning to adapt more quickly to changes inthe environment. We apply our model to millimeter wave commu-nications with its challenges of phased arrays with a large numberof antennas, high carrier frequencies, wide bandwidth and complexchannel characteristics. We experimentally validate the systemusing a 60 GHz FPGA-based testbed and show that it is able toreproduce the channel characteristics with good accuracy.TRUEpu
Signal Transmitter Localization using Low-cost SDR receivers
Get PDFThe popularity of wireless communications is growing every year and with it the possibility of threats in the
electromagnetic spectrum increases. One of the key aspects to stop these attacks is to be able to localize where the threat is coming from. Current commercial products make use of expensive hardware and GPS-based synchronization techniques to perform geolocation. In this thesis, we propose a network architecture based on low-cost, GPS-free Software-Defined Radio (SDR) receivers that localizes a signal transmitter in a collaborative manner. We perform evaluations on different components of the architecture such as receiver imperfection correction, reference signals and multilateration approaches. Our results indicate that signal transmitter localization is feasible with the architecture proposed even using low-cost radio receivers.Telematics EngineeringUniversidad Carlos III de Madrid, Spai
The Price is (Not) Right:Comparing Privacy in Free and Paid Apps
Get PDFIt is commonly assumed that “free” mobile apps come at the cost of consumer privacy and that paying for apps could offer consumers protection from behavioral advertising and long-term tracking. This work empirically evaluates the validity of this assumption by comparing the privacy practices of free apps and their paid premium versions, while also gauging consumer expectations surrounding free and paid apps. We use both static and dynamic analysis to examine 5,877 pairs of free Android apps and their paid counterparts for differences in data collection practices and privacy policies between pairs. To understand user expectations for paid apps, we conducted a 998-participant online survey and found that consumers expect paid apps to have better security and privacy behaviors. However, there is no clear evidence that paying for an app will actually guarantee protection from extensive data collection in practice. Given that the free version had at least one third-party library or dangerous permission, respectively, we discovered that 45% of the paid versions reused all of
the same third-party libraries as their free versions, and
74% of the paid versions had all of the dangerous permissions held by the free app. Likewise, our dynamic
analysis revealed that 32% of the paid apps exhibit all
of the same data collection and transmission behaviors
as their free counterparts. Finally, we found that 40%
of apps did not have a privacy policy link in the Google
Play Store and that only 3.7% of the pairs that did reflected differences between the free and paid versions.TRUEpu
Understanding Incentivized Mobile App Installs on Google Play Store
Get PDF“Incentivized” advertising platforms allow mobile app developers to acquire new users by directly paying users to install and engage with mobile apps (e.g., create an account, make in-app purchases).
Incentivized installs are banned by the Apple App Store and discouraged by the Google Play Store because they can manipulate app store metrics (e.g., install counts, appearance in top charts).
Yet, many organizations still offer incentivized install services for Android apps. In this paper, we present the first study to understand the ecosystem of incentivized mobile app install campaigns in Android and its broader ramifications through a series of measurements. We identify incentivized install campaigns that requireusers to install an app and perform in-app tasks targeting manipulation of a wide variety of user engagement metrics (e.g., daily active users, user session lengths) and revenue. Our results suggest that these artificially inflated metrics can be effective in improving app store metrics as well as helping mobile app developers to attract funding from venture capitalists. Our study also indicates lax enforcement of the Google Play Store’s existing policies to prevent these behaviors. It further motivates the need for stricter policing of incentivized install campaigns. Our proposed measurements can also be leveraged by the Google Play Store to identify potential policy violations.TRUEpu
LOCUS: Localization and analytics on-demand embedded in the 5G ecosystem
Get PDFLocation information and context-awareness are essential for a variety of existing and emerging 5G-based applications. Nevertheless, navigation satellite systems are denied in indoor environments, current cellular systems fail to provide high-accuracy localization, and other local localization technologies (e.g., Wi-Fi or Bluetooth) imply high deployment, maintenance and integration costs. Raw spatiotemporal data are not sufficient by themselves and need to be integrated with tools for the analysis of the behavior of physical targets, to extract relevant features of interests. In this paper, we present LOCUS, an H2020
project (https://www.locus-project.eu/) funded by the European Commission, aiming at the design and implementation of an innovative location management layered platform which will be able to: i) improve localization accuracy, close to theoretical bounds, as well as localization security and privacy, ii) extend
localization with physical analytics, iii) extract value out from the combined interaction of localization and analytics, while guaranteeing users’ privacy.TRUEpu