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Measuring 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 monetise 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, shedding light on several totally unknown facts about it. We show that data products listed in commercial DMs may cost from few to hundreds of thousands of US dollars. We analyse the prices of different categories of data and the challenges of comparing across DMs. We also analise the pricing of specific sellers and products to identify features that apparently correlate with prices, and we point to the need and the challenges of building a quotation tool for data products based on market data.EU Horizon 2020TRUEpu
In-depth Study of RNTI Management in Mobile Networks: Allocation Strategies and Implications on Data Trace Analysis
Get PDFThe advance of mobile network technologies and components heavily relies on data-driven techniques. This is especially true for fifth generation (5G) and the upcoming sixth generation (6G) networks, as the optimization of network components and protocols is expected to be fueled by artificial intelligence (AI) based solutions. When using real-world radio access measurement traces, the identity of individual users is not directly accessible because at runtime operation Base Stations (BSs) assign Radio Network Temporary Identifiers (RNTIs) to users. RNTIs are not bound to a user but are reused upon expiration of an inactivity timer, whose duration is operator dependent. This implies that, over time, multiple users are mapped to the same RNTI. In fact, the allocation of RNTIs to users is implemented in diverse and proprietary ways by operators and equipment vendors. Distinguishing individual users within the RNTI space is a non-trivial task and key to analyze traffic traces properly. In this paper, we make the following contributions: i) we propose and validate two complementary methodologies to identify the RNTI inactivity threshold, and we characterize ii) the RNTI allocation process of network operators, and iii) the user traffic patterns given the specific RNTI allocation process. Our study is based on a large dataset we collected from production BSs of several mobile network operators across five different countries. We find that there exist heterogeneous strategies for RNTI allocation that BSs dynamically use depending on the traffic load and daytime. We further observe that the RNTI expiration threshold is in the order of minutes, and demonstrate how using thresholds around 10 seconds, as in the vast majority of the literature, can bias subsequent analyses. Overall, our work provides an important step towards dependable mobile network trace analysis, and lays solid foundations to research relying on traffic traces for data-driven analysis.Comunidad de MadridMinisterio de Ciencia e InnovaciónTRUEpu
Measuring Web Cookies in Governmental Websites
Get PDFIn recent years, governments worldwide have moved their services online to better serve their citizens. Benefits aside, this increases the danger of tracking via such sites. This is of great concern as governmental websites increasingly become the only interaction point with the government. In this paper, we investigate popular governmental websites across different countries and assess to what extent the visits to these sites are tracked by third parties. Our results show that, unfortunately, tracking is a serious concern as up to 90\% of these websites in some countries add cookies of third-party trackers without any consent from users. Even in countries with strict
user privacy laws, non-session cookies set by trackers that last for days or months are widely present. We also show that the above is also a problem for international organizations' official websites and popular websites that inform the public about the COVID-19 pandemic.TRUEpu
Enabling Unmanned Aerial Vehicles for the near future applications
No full textUntil today, Unmanned Aerial Vehicle (UAV) operations only include a single aerial vehicle (in most cases) that performs reconnaissance missions by sending telemetry captured by different onboarded sensors (e.g., video, temperature, air quality) to the Ground Control Station (GCS). Single-UAV applications, despite their apparent simplicity, are used in many different and significant fields (e.g., surveillance of livestock, monitoring of power lines, traffic monitoring, rescue). Many applications of UAV swarms have already been seen. Still, they are usually stunts and exhibitions with no actual functionality.
Recent research trends are founded on multiple UAVs operating collaborative implementing more complex services, and generally integrated into the urban environment. It would lead to new scenarios that are not yet adequately deployed (e.g., package delivery, monitoring of sports events or crowds such as concerts or demonstrations, increasing coverage, support to emergency services in cities (fire, police, emergency)). However, several challenges must be faced before integrating these applications into our everyday lives.
The central objective of the thesis is to contribute to some of the significant challenges identified in the UAV communications services sector. In the first place, this thesis contributes with an emulation solution for validating environments with connected UAVs, including different use cases and verticals. Additionally, it contributes to communications solutions in complex connectivity environments based on experimentation where the Fifth Generation of cellular network technology (5G) softwarization technologies are integrated into the UAV ecosystem. In the last place, this thesis contributes to the proposal of new solutions to solve some limitations, such as the high energy consumption in combination with UAVs’ limited flight autonomy or the complexity of traffic management and the establishment of the network infrastructure in such volatile environments.Telematics EngineeringUniversidad Carlos III de Madrid, Spai
Driving Under Influence: Robust controller migration for MEC-enabled platooning
Get PDFConnected cars are becoming more common. With the development of multi-access edge computing (MEC) for low-latency applications, it will be possible to manage the cooperative adaptive cruise control (CACC, also known as platooning) of such vehicles from the edge of cellular networks. In this paper, we present a controller that carries out platooning from the network edge by adapting to varying network conditions. We incorporate a mechanism in the controller that allows vehicles to switch to automated cruise control when delays exceed safety thresholds, and switches back to platooning when the delays are sufficiently low to support it. We also formulate the problem of maintaining a low-latency connection in the presence of high mobility through migration and propose a Q-Learning algorithm to solve this problem. We finally propose an Asynchronous Shared Learning scheme that enables multiple migration agents to cooperate, in order to expedite the convergence of migration policies. Compared to state-of-the-art migration techniques, our scheme exhibits better compliance of vehicle speed and spacing values to preset targets, and ameliorates statistical dispersion.Spanish State Research AgencyTRUEpu
Cleaning Matters! Preprocessing-enhanced Anomaly Detection and Classification in Mobile Networks
Get PDFMobile communications providers often monitor key performance indicators (KPIs) with the goal of identifying anomalous operation scenarios that can affect the quality of Internet-based services. In this regard, anomaly detection and classification in mobile networks has become a challenging task due to the unknown distributions exhibited by the collected data and the lack of interpretability of the embedded (machine learning) models. This paper proposes an unsupervised end-to-end methodology based on both a data cleaning strategy and explainable machine learning models to detect and classify performance anomalies in mobile networks. The proposed approach, dubbed clean and explainable anomaly detection and classification (KLNX), aims at identifying attributes and operation scenarios that could induce anomalous KPI values without resorting to parameter tuning. Unlike previous methodologies, the proposed method includes a data cleaning stage that extracts and removes experiments and attributes considered outliers in order to train the anomaly detection engine with the cleanest possible dataset. Additionally, machine learning models provide interpretable information about features and boundaries describing both the normal network behavior and the anomalous scenarios. To evaluate the performance of the proposed method, a testbed generating synthetic data is developed using a known TCP throughput model. Finally, the methodology is assessed on a real data set captured by operational tests in commercial networks.MCIN/AEI /10.13039/501100011033 and the European Union through the Next GenerationEU/ PRTR program.Mario Gerla Best Paper AwardTRUEpu
Performance analysis of the LAMDA fuzzy algorithm improvements in different case studies
Get PDFLearning Algorithm for Multivariable Data Analysis (LAMDA) is a fuzzy approach, which has been used in clustering and classification processes. Recently, extensions have been proposed for LAMDA, to improve its performance in classification tasks. The first one is called LAMDA-FAR, which proposes a new criterion to validate functional states after recognition, based on the minimum and maximum calculated distances between the two membership degrees with the highest values. The second extension is called LAMDA-HAD, which proposes two strategies to improve LAMDA performance. The first strategy calculates an adaptive Global Adequacy Degree (GAD) of the Non-Informative Class (NIC) to each class to prevent that correctly classified individuals will be assigned to the NIC class. The second strategy calculates the similarity among the GAD of an individual and all ones of each class, to make a more reliable assignment. This article analyzes the performance of these techniques for different classification problems. The goal is to define the application context for each one. Each case study was defined by a set of data in an operational context, which must be used by the classification techniques to obtain accurate results. LAMDA-HAD was better with unbalanced classes, while LAMDA-FAR was excellent for discovering new classes. Both algorithms worked well for different levels of noise (which can represent faults in the sensors), a factor important in diagnostic tasks. The aim of this paper is to determine the correct utilization profile of each LAMDA technique adjusted to the properties of the problems under study.TRUEpu
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.TRUEpu
Optimizing network control and resource allocation in large scale ultra dense mm-wave networks
No full textWireless communication is a transformative technology that has changed the way we work, communicate and enjoy our free time. The number of connected devices is expected to increase to 29.4 billion by 2030. As a result, there is a demand for higher data rates, lower delays and constant connectivity of wireless devices. These demands have driven the networking community to seek new technologies as these requirements are beyond the capabilities of current networks. Communication at higher frequencies, beyond 10 GHz where most of current communications systems operate, could be the game changing technology for the next generation of networks. At millimeter-wave frequencies, 30-300 GHz, the available spectrum is larger than all spectrum currently allocated to cellular and wireless area networks (WLAN). The unlicensed spectrum at 60 GHz alone can offer 10 to 100 times more spectrum than it is available in current unlicensed WLANs. The larger bandwidth allocations allow for increased datarates. These multi-gigabit per second rates and milisecond latencies are now achievable thanks to new directional high gain antennas and cost-effective CMOS technology that can operate at mm-wave frequencies.
However, with the use of this new technology new challenges arise.
The thesis is divided into two parts, first we study the challenges in the optimization and features of conventional mm-wave networks, and second, we consider a new way of designing mm-wave communications using Machine Learning.Telematics EngineeringUniversidad Carlos III de Madrid, Spai
Uncovering 5G Performance on Public Transit Systems with an App-based Measurement Study
Get PDFFifth-generation (5G) networks are now entering a stable phase in terms of commercial release. 5G design is flexible to support a diverse range of radio bands (i.e., low-, mid-, and high-band) and application requirements. Since its initial roll-out in 2019, extensive measurements studies have revealed key aspects of commercial 5G deployments (e.g., coverage, signal strength, throughput, latency, handover, and power consumption among the others) for several scenarios (e.g., pedestrian and car mobility, mid-, and high-bands, etc.). In this paper, we take a different angle than previous studies and carry out an in-depth measurement study of 5G in a large public bus transit system in a major European city. For several mobile network operators, we identify how flexible the network deployment is by analyzing Radio Resource Control (RRC) messages, mobility management, and application performance.Comunidad de MadridMinisterio de Ciencia e InnovaciónMinisterio de Asuntos Económicos y Transformación DigitalTRUEpu