IMDEA Networks Institute Digital Repository
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Covid-19 Contact Tracing through Multipath Profile Similarity
Get PDFContact tracing is a key approach to control the spread of Covid-
19 and any other pandemia. Recent attempts have followed either
traditional ways of tracing (e.g. patient interviews) or unreliable
app-based localization solutions. The latter has raised both privacy
concerns and low precision in the contact inference. In this work,
we present the idea of contact tracing through the multipath profile
similarity. At first, we collect Channel State Information (CSI) traces
from mobile devices, and then we estimate the multipath profile.
We then show that positions that are close obtain similar multipath
profiles, and only this information is shared outside the local network.
This result can be applied for deploying a privacy-preserving
contact tracing system for healthcare authorities.TRUEpu
Exploring the Security and Privacy Risks of Chatbots in Messaging Services
Get PDFThe unprecedented adoption of messaging platforms for work and recreation has made it an attractive target for malicious actors. In this context, third-party apps (so-called chatbots) offer a variety of attractive functionalities that support the experience in large channels. Unfortunately, under the current permission and deployment models, chatbots in messaging systems could steal information from channels without the victim’s awareness. In this paper, we propose a methodology that incorporates static and dynamic analysis for automatically assessing security and privacy issues in messaging platform chatbots. We also provide preliminary findings from the popular Discord platform that highlight the risks that chatbots pose to users. Unlike other popular platforms like Slack or MS Teams, Discord does not implement user-permission checks—a task entrusted to third-party developers. Among others, we find that 55% of chatbots from a leading Discord repository request the “administrator” permission, and only 4.35% of chatbots with permissions actually provide a privacy policy.“Ramon y Cajal” Fellowship RYC-2020-029401-ITRUEpu
Performance Evaluation and Anomaly detection in Mobile BroadBand Across Europe
Get PDFWith the rapidly growing market for smartphones and user’s confidence for immediate access to high-quality multimedia content, the delivery of video over wireless networks has become a big challenge. It makes it challenging to accommodate end-users with flawless quality of service. The growth of the smartphone market goes hand in hand with the development of the Internet, in which current transport protocols are being re-evaluated to deal with traffic growth. QUIC and WebRTC are new and evolving standards. The latter is a unique and evolving standard explicitly developed to meet this demand and enable a high-quality experience for mobile users of real-time communication services. QUIC has been designed to reduce Web latency, integrate security features, and allow a highquality experience for mobile users. Thus, the need to evaluate the performance of these rising protocols in a non-systematic environment is essential to understand the behavior of the network and provide the end user with a better multimedia delivery service. Since most of the work in the research community is conducted in a controlled environment, we leverage the MONROE platform to investigate the performance of QUIC and WebRTC in real cellular networks using static and mobile nodes. During this Thesis, we conduct measurements ofWebRTC and QUIC while making their data sets public to the interested experimenter. Building such data sets is very welcomed with the research community, opening doors to applying data science to network data sets. The development part of the experiments involves building Docker containers that act as QUIC and WebRTC clients. These containers are publicly available to be used candidly or within the MONROE platform. These key contributions span from Chapter 4 to Chapter 5 presented in Part II of the Thesis.
We exploit data collection from MONROE to apply data science over network data sets, which will help identify networking problems shifting the Thesis focus from performance evaluation to a data science problem.
Indeed, the second part of the Thesis focuses on interpretable data science. Identifying network problems leveraging Machine Learning (ML) has gained much visibility in the past few years, resulting in dramatically improved cellular network services. However, critical tasks like troubleshooting cellular networks are still performed manually by experts who monitor the network around the clock.
In this context, this Thesis contributes by proposing the use of simple interpretable ML algorithms, moving away from the current trend of high-accuracy ML algorithms (e.g., deep learning) that do not allow interpretation (and hence understanding) of their outcome. We prefer having lower accuracy since we consider it interesting (anomalous) the scenarios misclassified by the ML algorithms, and we do not want to miss them by overfitting. To this aim, we design TTrees (from Troubleshooting Trees), a practical and interpretable ML software tool that implements an unsupervised methodology we have designed to automate the causes of performance anomalies in a cellular network and compare it to a supervised counterpart, named STress (from Supervised Trees). Both methodologies require small volumes of data and are quick at training. Our experiments using real data from operational commercial mobile networks e.g., sampled with MONROE probes, show that STrees and TTrees can automatically identify and accurately classify network anomalies—e.g., cases for which a low network performance is not justified by operational conditions—training with just a few hundreds of data samples, hence enabling precise troubleshooting actions. Most importantly, our experiments show that a fully automated unsupervised approach is viable and efficient. In Part III of the Thesis which includes Chapter 6 and 7.
In conclusion, in this Thesis, we go through a data-driven networking roller coaster, from performance evaluating upcoming network protocols in real mobile networks to building methodologies that help identify and classify the root cause of networking problems, emphasizing the fact that these methodologies are easy to implement and can be deployed in production environments.Telematics EngineeringUniversidad Carlos III de Madrid, Spai
FreqyWM: Frequency WaterMarking for the New Data Economy
Get PDFWe present a novel technique for modulating the appearance
frequency of a few tokens within a dataset for encoding an
invisible watermark that can be used to protect ownership
rights upon data. We develop optimal as well as fast heuristic
algorithms for creating and verifying such watermarks.
We also demonstrate the robustness of our technique against
various attacks and derive analytical bounds for the false positive
probability of erroneously “detecting” a watermark on
a dataset that does not carry it. Our technique is applicable
to both single dimensional and multidimensional datasets, is
independent of token type, and can be used in a variety of use
cases that involve buying and selling data in contemporary
data marketplaces
Towards Native Explainable and Robust AI in 6G Networks
Get PDF6G networks are expected to face the daunting task of providing support to a
set of extremely diverse services, each more demanding than those of previous
generation networks (e.g., holographic communications, unmanned mobility,
etc.), while at the same time integrating non-terrestrial networks, incorporating new technologies, and supporting joint communication and sensing. The
resulting network architecture, component interactions, and system dynamics
are unprecedentedly complex, making human-only operation impossible, and
thus calling for AI-based automation and configuration support. For this to
happen, AI solutions need to be robust and interpretable, i.e., network engineers
should trust the way AI operates and understand the logic behind its decisions.
In this paper, we revise the current state of tools and methods that can make
AI robust and explainable, shed light on challenges and open problems, and
indicate potential future research directions.Spanish Ministry of Science and InnovationEuropean Union’s Horizon 2020 research and innovation programmeComunidad de MadridMadrid Regional GovernmentFALSEpu
Robust multivariate control chart based on shrinkage for individual observations
No full textA robust multivariate quality control technique for individual observations is proposed, based on the robust reweighted shrinkage estimators. A simulation study is done to check the performance and compare the method with the classical Hotelling approach, and the robust alternative based on the reweighted minimum covariance determinant estimator. The results show the appropriateness of the method even when the dimension or the Phase I contamination are high, with both independent and correlated variables, showing additional advantages about computational efficiency. The approach is illustrated with two real data-set examples from production processes.FALSEpu
Content-Aware Adaptive Point Cloud Delivery
Get PDFPoint clouds are an important enabler for a wide range of applications in various domains, including autonomous vehicles and virtual reality applications. Hence, the practical applicability of point clouds is gaining increasing importance and presenting new challenges for communication systems where large amounts of data need to be shared with low latency. Point cloud content can be very large, especially when multiple objects are involved in the scene. Major challenges of point clouds delivery are related to streaming in bandwidth-constrained networks and to resource-constrained devices. In this work, we are exploiting object-related knowledge, i.e., content-driven metrics, to improve the adaptability and efficiency of point clouds transmission. This study proposes applying a 3D point cloud semantic segmentation deep neural network and using object-related knowledge to assess the importance of each object in the scene. Using this information, we can semantically adapt the bit rate and utilize the available bandwidth more efficiently. The experimental results conducted on a real-world dataset showed that we can significantly reduce the requirement for multiple object point cloud transmission with limited quality degradation compared to the baseline without modifications.TRUEpu
VoronoiBoost: Data-driven Probabilistic Spatial Mapping of Mobile Network Metadata
Get PDFMapping information collected at the level of individual base stations onto the geographical space is a required operation for many works relying on mobile network metadata. The common practice is to represent base station coverage as Voronoi cells, and assume that users are uniformly distributed therein. In this paper, we leverage a large-scale dataset of realistic spatial association probabilities to over 5,000 operational base stations, and quantify the substantial problems of such a simplistic mapping approach. To address the limitations of legacy Voronoi representations, we develop VoronoiBoost, a data-driven model that scales Voronoi cells to match the probabilistic distribution of users associated to each base station. VoronoiBoost relies on the same input as traditional Voronoi decompositions, but provides a richer and more accurate rendering of where users are located: hence, it can be readily used by researchers to substantially improve the spatial representation of mobile network metadata. Our experiments demonstrate that VoronoiBoost improves the quality of mapping by 44% on average over standard Voronoi cells. We also showcase the utility of our model in a practical Edge network planning use case, where the information produced by VoronoiBoost drives a deployment up to 28% more accurate than that obtained with Voronoi cells.Comunidad de MadridTRUEpu
Algorithms for robust indoor localization and sensing using off-the-shelf devices
Get PDFLocalization has been mainly an optional feature of cellular networks since they have been designed for communication and many location-based services have used GPS to provide more functionalities like navigation, rescue and many more. GPS gets outstanding accuracy in outdoor environments, but its performance drastically degrades in indoor settings since the GPS signals hardly go through walls. However, most human activities are concentrated in indoor environments and location-based services cannot be carried out successfully by GPS. To overcome this limitation, wireless protocols are appealing to fulfill indoor localization requirements while communication is ongoing. Operators, chipset vendors and application developers are paying attention to exploiting location information to provide new applications like augmented reality and indoor navigation. Moreover, localization can be used for network optimization and researchers are actively investigating it. For instance, intelligent handover can exploit location information to guess which Access Point (AP) is the most suitable one before doing the handover. In addition, a range of applications can exploit it as well such as Multiple-Input Multiple-Output beamforming, millimeter-wave beam alignment, etc. For the last decade, sensing has been appealing to not only provide location information but also context awareness. This enables human activity and event recognition, vital sign monitoring, user identification, mapping, imaging, etc. To ensure the good performance of these applications, accurate and ubiquitous positioning is needed. To this end, 5G and the newest Wi-Fi protocols, IEEE 802.11ac and 802.11ax, are becoming the key technologies to provide outstanding indoor localization since they incorporate larger array configurations and wider channel bandwidths than previous wireless protocols.
Researchers have made a great effort to provide indoor localization and decimeter level of accuracy has been achieved. However, this outstanding performance has been evaluated using a great number of APs and assuming that every AP has a clear Line-Of-Sight (LOS) to the device. However, typical indoor wireless deployments tend to have sparse AP densities since they are optimized for coverage and not for localization. For instance, a Wi-Fi infrastructure usually contains one AP per room and a 5G deployment tends to have a limited number of AP as well. Moreover, indoor environments are generally rich in multipath components that interfere with the estimation of the direct path. This is particularly challenging in Non-Line-Of-Sight (NLOS) settings as obstacles can block the direct path and a system might detect an NLOS path and not the obstructed LOS path. As a result, the performances of state-of-the-art localization schemes drastically degrade their accuracy in realistic deployments.
A localization algorithm that copes well with NLOS settings and wireless deployments with sparse AP densities is needed for precise and pervasive localization. Also, implementing and testing it in cutting edge devices is crucial to exploit the improved hardware features of the newest wireless protocols. Therefore, this thesis aims at providing a framework for accurate localization even in challenging scenarios. Sensing research shares methodologies with localization since sensing applications require extracting location information from NLOS paths as localization does from the direct path. Hence, this thesis also aims at exploring how the proposed localization framework can be used for sensing applications.
We start delving into wireless localization by exploring what an LTE localization system can achieve. This is particularly beneficial since 5G and LTE will coexist for a while until 5G provides ubiquitous coverage. Therefore, LTE needs to fulfill the localization requirements for a range of applications if 5G is not available. To this end, we implement and evaluate an LTE localization system for a single AP using software-defined radios. We observe that LTE achieves a median error of 2~m in LOS cases. However, the LTE performance drastically degrades to 4.6~m of median error in NLOS settings. These results point out that LTE provides a positioning accuracy that complies with a great number of location-based services in LOS. Nevertheless, applications that demand ubiquitous localization may not be correctly carried out in NLOS settings.
To tackle the NLOS issue, we implement UbiLocate, a Wi-Fi location system that copes well with common AP deployment densities and works ubiquitously, i.e., without excessive degradation under NLOS. UbiLocate demonstrates that meter-level median accuracy NLOS localization is possible through (i) an innovative angle estimator based on a Nelder-Mead search, (ii) a fine-grained time of flight ranging system with nanosecond resolution, and (iii) the accuracy improvements brought about by the increase in bandwidth and number of antennas of IEEE 802.11ac. In combination, they provide superior resolvability of multipath components, significantly improving location accuracy over prior work. We implement our location system on off-the-shelf 802.11ac devices. Our experimental evaluation shows an overall improvement of the localization performance by a factor of 2-3.
The latest generation of Wi-Fi standards, IEEE 802.11ax, brings new hardware capabilities that improve the performance of localization and sensing systems. In particular, the 160MHz of channel bandwidth and the four times denser spectrum significantly improve the resolvability of the multipath components compared to its predecessor, IEEE 802.11ac. We present the first tool to collect the most accurate CSI ever from off-the-shelf devices. To further validate the platform, we carry out a preliminary measurement campaign to compare the localization accuracy of IEEE 802.11ax with 802.11ac. Our results show that, as expected, IEEE 802.11ax provides superior performance improving the accuracy by a factor of 1.75 for LOS and NLOS settings.
Sensing research goes beyond localization since it aims at providing context awareness. We explore the integration of the proposed multipath decomposition algorithm as well as the testbed for sensing applications. In particular, we tackle human respiration rate estimation since it is appealing as it does not require any specialized hardware. Our results show that an accurate respiration rate estimation is possible by decomposing the channel.
In summary, location-based services demand accurate and ubiquitous localization. However, the state-of-the-art localization systems do not cope well with realistic wireless deployments and their positioning performances drastically degrade in these environments. Hence, we provide a localization framework that copes well with realistic wireless deployments and with NLOS settings. We conclude that resolving accurately the multipath components enables pervasive and precise localization. In addition, sensing enables new applications that are helpful in many issues since it provides not only location but also context awareness. Hence, we show that algorithms and testbeds that are designed for localization can be also utilized for sensing applications by tackling respiration rate estimation.Telematics EngineeringUniversidad Carlos III de Madrid, Spai
CartaGenie: Context-Driven Synthesis of City-Scale Mobile Network Traffic Snapshots
Get PDFMobile network traffic data offers unprecedented opportunities for innovative studies within and beyond networking. However, progress is hindered by the very limited access that the research community at large has to the real-world mobile network data that is needed to develop and dependably test mobile traffic data-driven solutions. As a contribution to overcome this barrier, we propose CartaGenie, a generator of realistic mobile traffic snapshots at city scale. Taking a deep generative modeling approach and through a tailored conditional generator design, CartaGenie can synthesize high-fidelity and artifact-free spatial traffic snapshots using only contextual information about the target geographical region that is easily found in public repositories. Hence, CartaGenie allows researchers to create their own realistic datasets of spatial traffic from open data about their region of interest. Experiments with real-world mobile traffic measurements collected in multiple metropolitan areas show that CartaGenie can produce dependable network traffic loads for areas where no prior traffic information is available, significantly outperforming a comprehensive set of benchmarks. Moreover, tests with practical case studies demonstrate that the synthetic data generated by CartaGenie is as good as real data in supporting diverse research-oriented mobile traffic data-driven applications.TRUEpu