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The Architectural Design of Service Management and Orchestration in 6G Communication Systems
Get PDFIn this poster paper, we propose and demonstrate an architectural framework for service Management and Orchestration (M&O) in Sixth-Generation (6G) communication systems. This architecture was designed by the Hexa-X project, which is a European flagship project dedicated to developing a vision and technological enablers for 6G. To provide a comprehensive and high-level description, we consider three views: (i) Functional View; (ii) Structural View; and (iii) Deployment View. We first discuss 6G service M&O before delving deeper into each view.TRUEpu
Mixed Signals: Analyzing Software Attribution Challenges in the Android Ecosystem
Get PDFThe ability to identify the author responsible for a given software
object is critical for many research studies
and for enhancing software transparency and accountability. However,
as opposed to other application markets like Apple's iOS App Store, attribution in the
Android ecosystem is known to be hard.
Prior research has leveraged market metadata and
signing certificates to identify software authors without questioning
the validity and accuracy of these attribution signals.
However, Android application (app) authors can, either intentionally or by mistake,
hide their true identity due to:
(1) the lack of policy enforcement by markets to ensure the
accuracy and correctness of the information disclosed
by developers in their market profiles during the app release process, and
(2) the use of self-signed certificates for signing apps instead of
certificates issued by trusted CAs.
In this paper, we perform the first empirical analysis
of the availability, volatility and overall aptness of publicly available
market and app
metadata for author attribution in Android markets.
To that end, we analyze a dataset of over
2.5 million market entries and apps extracted from five Android markets
for over two years. Our results show that widely used attribution signals are
often missing from market profiles and that they change over time. We also
invalidate the general belief about the
validity of signing certificates
for author attribution. For instance, we find
that apps from different authors share signing certificates
due to the proliferation of app building frameworks and software factories.
Finally, we introduce the concept of an \emph{attribution graph} and
we apply it to evaluate the validity of existing attribution signals
on the Google Play Store. Our results confirm that
the lack of control over publicly available signals can
confuse automatic attribution processes.TRUEpu
Wi-Fi Multi-Path Parameter Estimation for Sub-7 GHz Sensing: A Comparative Study
Get PDFThanks to the definition of the new IEEE 802.11bf standard, the development of Wi-Fi sensing applications is gaining momentum in the research community. In this regard, several studies have shown that learning-based approaches that leverage the frequency response of the Wi-Fi channel in the sub-7 GHz bands can reach high accuracy in different classification tasks, such as activity recognition, or person identification. Instead, more fine-grained applications – e.g., human localization and tracking, or respiration and heartbeat monitoring – require implementing model-based approaches to estimate the Wi-Fi multi-path parameters and analyze the time evolution of the paths associated with specific targets (the human body or chest). In this paper, we investigate the performance of six super-resolution algorithms for sub-7 GHz multi-path parameter estimation. Our extensive evaluation indicates that the estimation accuracy that can be achieved through commercial devices allows implementing human localization and tracking strategies but is insufficient to effectively design human vital signs monitoring applications due to the limited frequency and spatial diversity. We pledge to release our implementations for further investigations.TRUEpu
Using Survey Data to Estimate the Impact of the Omicron Variant on Vaccine Efficacy against COVID-19 Infection
Get PDFSymptoms-based detection of SARS-CoV-2 infection is not a substitute for precise diagnostic tests but can provide insight into the likely level of infection in a given population. This study uses symptoms data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on variants sequencing from GISAID. This work, conducted in January of 2022 during the emergence of the Omicron variant (subvariant BA.1), aims to improve the quality of infection detection from the available symptoms and to use the resulting estimates of infection levels to assess the changes in vaccine efficacy during a change of dominant variant; from the Delta dominant to the Omicron dominant period. Our approach produced a new symptoms-based classifier, Random Forest, that was compared to a ground-truth subset of cases with known diagnostic test status. This classifier was compared with other competing classifiers and shown to exhibit an increased performance with respect to the ground-truth data. Using the Random Forest classifier, and knowing the vaccination status of the subjects, we then proceeded to analyse the evolution of vaccine efficacy towards infection during different periods, geographies and dominant variants. In South Africa, where the first significant wave of Omicron occurred, a significant reduction of vaccine efficacy is observed from August-September 2021 to December 2021. For instance, the efficacy drops from 0.81 to 0.30 for those vaccinated with 2 doses (of Pfizer/BioNTech), and from 0.51 to 0.09 for those vaccinated with one dose (of Pfizer/BioNTech or Johnson&Johnson). We also extended the study to other countries in which Omicron has been detected, comparing the situation in October 2021 (before Omicron) with that of December 2021. While the reduction measured is smaller than in South Africa, we still found, for instance, an average drop in vaccine efficacy from 0.53 to 0.45 among those vaccinated with two doses. Moreover, we found a significant negative (Pearson) correlation of around −0.6 between the measured prevalence of Omicron in several countries and the vaccine efficacy in those same countries. This prediction, in January of 2022, of the decreased vaccine efficacy towards Omicron is in line with the subsequent increase of Omicron infections in the first half of 2022.IMDEA Networks, Comunidad de Madrid, Spain, the European Union through the European Regional Development Fund (ERDF), the Ministry of Sciences and Innovation, Spain, and individual donations to the CoronaSurveys ProjectTRUEpu
AutoManager: a Meta-Learning Model for Network Management from Intertwined Forecasts
Get PDFA variety of network management and orchestration (MANO) tasks take advantage of predictions to support anticipatory decisions. In many practical scenarios, such predictions entail two largely overlooked challenges: (i) the exact relationship between the predicted values (e.g., reserved resources) and the performance objective (e.g., quality of experience of end users) is often tangled and cannot be known a priori, and (ii) the objective is linked in many cases to multiple predictions that contribute to it in an intertwined way (e.g., resources to reserved are limited and must be shared among competing flows). We present AutoManager, a novel meta-learning model that can support complex MANO tasks by addressing these two challenges. Our solution learns how multiple intertwined predictions affect a common performance goal, and steers them so as to attain the correct operation point under a-priori unknown loss functions. We demonstrate AutoManager in practical, complex use cases based on real-world traffic measurements; our experiments show that the model produces forecasts that are accurate and tailored to the MANO task in a fully automated way.TRUEinpres
Autonomic Computing in a Beef-Production Process for Precision Livestock Farming
Get PDFPrecision livestock farming (PLF) offers farmers real-time monitoring and management system. PLF provides a real-time warning when something goes wrong so that the farmer can take immediate action to solve the problem. PLF introduces many new challenges and questions that must be resolved. Some of these challenges are related to the integration of grazing and animal health into the beef-production process. This article introduces an architecture for the self-managing of a beef-production farm. In particular, the architecture includes three autonomous cycles of data analysis tasks (ACODAT) that allow beef producers to have adequate coordination, optimization and planning of the productive process, which are: (i) circuit preparation, (ii) animal purchase, and (iii) animal fattening. This article also instantiates, in a farm, the autonomous animal-fattening cycle, as the first step towards efficient and effective beef-production processes. The main contributions of this architecture are (i) the ability to use everything mining to improve the knowledge of the system and decision-making processes, and (ii) three ACODAT for real-time analysis for sustainable and environmentally-friendly livestock production. The results are encouraging since the ACODAT allows smart management of the beef-production process, naturally introducing artificial-intelligence techniques to develop these tasks. Particularly, modeling using ACODAT allows an adequate description of a precision livestock process. Likewise, the preliminary results of some of the tasks of ACODAT are stimulating because they allow evaluating the feasibility of the proposal. For example, a first task for the identification of cattle fattening has a Mean Absolute Error (MAE) of 5.4 kg, which will be used by ACODAT to identify anomalies in the fattening process. The instantiation of the animal-fattening cycle shows the viability and robustness of this proposal.TRUEpu
TunnelLiFi: Bringing LiFi to Commodity Internet of Things Devices
Get PDFLiFi, light-fidelity, is a wireless technology that uses visible light for data transmission. It has several advantages, such as using a different part of electromagnetic spectrum than radio communication and providing enhanced privacy because light transmission is blocked by walls. Internet of Things applications with low-tomoderate data rates represent a promising arena for LiFi adoption.
However, it is difficult to bring LiFi to IoT devices for several reasons, including some of LiFi’s strengths. We present TunnelLiFi, a new receiver architecture that acts like a bridge between the light and radio spectrums. A key aspect of TunnelLiFi’s design is the use of the unique self-oscillating mixing property of the tunnel diode oscillator, which enables the mixing of a photodiode signal with a locally generated radio frequency carrier signal while drawing under 100 μW of power consumption. In our experiments, Tunnel- LiFi demonstrates the ability to replicate the information contained in light signals onto radio signals at tens of microwatts, even in low-light conditions (300 lux) and at low bitrates (2.93 Kbps). We also show the potential of TunnelLiFi to support high bitrates. TunnelLiFi opens up new possibilities for LiFi technology by enabling communication in areas where light propagation is challenging. It also allows commodity IoT devices to receive LiFi transmissions using their existing transceivers, thus expanding the reach of LiFi.TRUEinpres
Statistical Age-of-Information Bounds for Parallel Systems: When Do Independent Channels Make a Difference?
Get PDFThis paper contributes tail bounds of the age-of-information of a general class of parallel systems
and explores their potential. Parallel systems arise in relevant cases, such as in multi-band mobile
networks, multi-technology wireless access, or multi-path protocols, just to name a few. Typically,
control over each communication channel is limited and random service outages and congestion cause
buffering that impairs the age-of-information. The parallel use of independent channels promises a
remedy, since outages on one channel may be compensated for by another. Surprisingly, for the wellknown
case of M|M|1 queues we find the opposite: pooling capacity in one channel performs better
than a parallel system with the same total capacity. A generalization is not possible since there are
no solutions for other types of parallel queues at hand. In this work, we prove a dual representation
of age-of-information in min-plus algebra that connects to queueing models known from the theory of
effective bandwidth/capacity and the stochastic network calculus. Exploiting these methods, we derive
tail bounds of the age-of-information of parallel G|G|1 queues. In addition to parallel classical queues,
we investigate Markov channels where, depending on the memory of the channel, we show the true
advantage of parallel systems. We continue to investigate this new finding and provide insight into
when capacity should be pooled in one channel or when independent parallel channels perform better.
We complement our analysis with simulation results and evaluate different update policies, scheduling
policies, and the use of heterogeneous channels that is most relevant for latest multi-band networks.Communidad de Madri
Computing the Relative Value of Spatio-Temporal Data in Data Marketplaces
Get PDFSpatio-temporal information is used for driving a plethora of intelligent transportation, smart-city and crowd-sensing applications.Data is now a valuable production factor and data marketplaces have appeared to help individuals and enterprises bring it to market and the ever-growing demand. Such marketplaces are able to combine data from different sources to meet the requirements of different applications. In this paper we study the problem of estimating the relative value of spatio-temporal datasets combined in marketplaces for predicting transportation demand and travel time in metropolitan areas. Using large datasets of taxi rides from Chicago, Porto and New York we show that simplistic but popular approaches for estimating the relative value of data, such as splitting it equally among the data sources, more complex ones based on volume or the “leave-one-out” heuristic, are inaccurate. Instead,
more complex notions of value from economics and game-theory, such as the Shapley value, need to be employed if one wishes to capture the complex effects of mixing different datasets on the accuracy
of forecasting algorithms. This does not seem to be a coincidental observation related to a particular use case but rather a general trend across different use cases with different objective functions.EUTRUEpu
Towards Improving Code Stylometry Analysis in Underground Forums
Get PDFCode Stylometry has emerged as a powerful mechanism to identify programmers. While there have
been significant advances in the field, existing mechanisms underperform in challenging domains. One such domain is studying the provenance of code shared in underground forums, where code posts tend to have small or incomplete source code fragments. This paper proposes a method designed to deal with the idiosyncrasies of code snippets shared in these forums. Our system fuses a forum-specific learning pipeline with Conformal Prediction to generate predictions with precise confidence levels as a novelty. We see that identifying unreliable code snippets is paramount to generate high accuracy predictions, and this is a task where traditional learning settings fail. Overall, our method performs as twice as well as the state-of-the-art in a constrained setting with a large number of authors (i.e., 100). When dealing with a smaller number of authors (i.e., 20), it performs at high accuracy (89%). We also evaluate our work on an open-world assumption and see that our method is more effective at retaining samples.TRUEpu