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Mobility-Aware Solutions for Edge Data Center Deployment in Urban Environments
No full textThe objective of Multi-access Edge Computing (MEC) is to better support lowlatency applications by bringing storage and computational capabilities at the edge of the network into so-called Edge Data Centers (EDCs). To this end, effective placement of EDCs in urban environments is key for proper load balancing, outage minimization and energy efficiency. This chapter tackles this problem and takes into account the mobility of citizens and their spatial patterns to estimate the optimal placement of MEC EDCs in urban environments that minimizes out- ages and energy efficiency. First, the chapter will discuss how the computational demand and user mobility affect EDC placement and expose three heuristics as solutions. These methods are validated with Crowd EdgeSim, a simulator build specifically for such problem and show that efficient EDCs placement significantly reduces outages.TRUEpu
TRANSIT: Fine-grained human mobility trajectory inference at scale with mobile network signaling data
No full textCall detail records (CDR) collected by mobile phone network providers have been largely used to model and analyze human-centric mobility. Despite their potential, they are limited in terms of both spatial and temporal accuracy thus being unable to capture detailed human mobility information. Network Signaling Data (NSD) represent a much richer source of spatio-temporal information currently collected by network providers, but mostly unexploited for fine-grained reconstruction of human-centric trajectories. In this paper, we present TRANSIT, TRAjectory inference from Network SIgnaling daTa, a novel framework capable of processing NSD to accurately distinguish mobility phases from stationary activities for individual mobile devices, and reconstruct, at scale, fine-grained human mobility trajectories, by exploiting, with a DBSCAN-based clustering approach, the inherent recurrence of human mobility and the higher sampling rate of NSD. The validation on a ground-truth dataset of GPS trajectories showcases the superior performance of TRANSIT (80% precision and 96% recall) with respect to state-of-the-art solutions in the identification of movement periods, as well as an average 190 m spatial accuracy in the estimation of the trajectories. We also leverage TRANSIT to process a unique large-scale NSD dataset of more than 10 millions of individuals and perform an exploratory analysis of city-wide transport mode shares, recurrent commuting paths, urban attractivity and analysis of mobility flows.TRUEpu
CoronaSurveys: Using Surveys with Indirect Reporting to Estimate the Incidence and Evolution of Epidemics
No full textFALSEpu
Detecting video-game injectors exchanged in game cheating communities
Get PDFVideogamecheatsdestroytheonlineplayexperienceofusers and result in financial losses for game developers. Similar to hacking communities, cheat developers often organize themselves around forums where they share game cheats and know-how. In this paper, we perform a large-scale measurement of two online forums, MPGH and UnknownCheats, devoted to video game cheating that are nowadays very active and altogether have more than 7 million posts. Video game cheats often require an auxiliary tool to access the victim process, i.e., an injector. This is a type of program that manipulates the game program memory, and it is a key piece for evading cheat detection on the client-side. We leverage the output of our measurement study to build a machine learn- ing classifier that identifies injectors based on their behavioral traits. Our system will help game developers and the anti-cheat industry to identify attack vectors more quickly and will reduce the barriers to study this topic within the academic community.TRUEinpres
Scalable Phase-Coherent Beam-Training for Dense Millimeter-wave Networks
Get PDFMillimeter-wave communications (mm-wave) use analog beamforming techniques, which steer the signal energy in a desired direction, to overcome the high path-loss at such frequencies. To determine the direction in which to steer, mm-wave standards such as IEEE802.11ad specify beam training mechanisms for both access points as well as client stations. However, the overhead of the beam training limits scalability as the density of network deployments increases and mobile devices that require constant training are supported. We design SPIDER, a low-overhead beam-training mechanism where only access points actively participate in the training and stations perform passive compressive estimation of the angle-of-arrival. To this end, stations carry out phase-coherent measurements by switching through multiple receive beam patterns on a time-scale of tens of nanoseconds when receiving a packet preamble. Since no suitable testbed platforms exist that support such fast antenna reconfiguration, we design a high-performance,full-bandwidth FPGA-based testbed platform for flexible mm-wave experimentation, that we make available as open source. The performance analysis with this testbed shows that our algorithm achieves highly accurate angle estimation used to drive the beam steering decisions and reduces overhead by an order of magnitude compared to IEEE 802.11ad beam training.TRUEpu
Algorithms for propagation-aware underwater ranging and localization
Get PDFWhile oceans occupy most of our planet, their exploration and conservation are one of the crucial research problems of modern time. Underwater localization stands among the key issues on the way to the proper inspection and monitoring of this significant part of our world. In this thesis, we investigate and tackle different challenges related to underwater ranging and localization. In particular, we focus on algorithms that consider underwater acoustic channel properties. This group of algorithms utilizes additional information about the environment and its impact on acoustic signal propagation, in order to improve the accuracy of location estimates, or to achieve a reduced complexity, or a reduced amount of resources (e.g., anchor nodes) compared to traditional algorithms. First, we tackle the problem of passive range estimation using the differences in the times of arrival of multipath replicas of a transmitted acoustic signal. This is a costand energy- effective algorithm that can be used for the localization of autonomous underwater vehicles (AUVs), and utilizes information about signal propagation. We study the accuracy of this method in the simplified case of constant sound speed profile (SSP) and compare it to a more realistic case with various non-constant SSP. We also propose an auxiliary quantity called effective sound speed. This quantity, when modeling acoustic propagation via ray models, takes into account the difference between rectilinear and non-rectilinear sound ray paths. According to our evaluation, this offers improved range estimation results with respect to standard algorithms that consider the actual value of the speed of sound. We then propose an algorithm suitable for the non-invasive tracking of AUVs or vocalizing marine animals, using only a single receiver. This algorithm evaluates the underwater acoustic channel impulse response differences induced by a diverse sea bottom profile, and proposes a computationally- and energy-efficient solution for passive localization. Finally, we propose another algorithm to solve the issue of 3D acoustic localization and tracking of marine fauna. To reach the expected degree of accuracy, more sensors are often required than are available in typical commercial off-the-shelf (COTS) phased arrays found, e.g., in ultra short baseline (USBL) systems. Direct combination of multiple COTS arrays may be constrained by array body elements, and lead to breaking the optimal array element spacing, or the desired array layout. Thus, the application of state-of-the-art direction of arrival (DoA) estimation algorithms may not be possible. We propose a solution for passive 3D localization and tracking using a wideband acoustic array of arbitrary shape, and validate the algorithm in multiple experiments, involving both active and passive targets.Telematics EngineeringUniversidad Carlos III de Madrid, Spai
Monitoring in fog computing: state-of-the-art and research challenges
No full textFog computing has rapidly become a widely accepted computing paradigm to mitigate cloud computing-based infrastructure limitations such as scarcity of bandwidth, large latency, security, and privacy issues. Fog computing resources and applications dynamically vary at run-time, and they are highly distributed, mobile, and appear-disappear rapidly at any time over the internet. Therefore, to ensure the quality of service and experience for end-users, it is necessary to comply with a comprehensive monitoring approach. However, the volatility and dynamism characteristics of fog resources make the monitoring design complex and cumbersome. The aim of this article is therefore three-fold: 1) to analyse fog computing-based infrastructures and existing monitoring solutions; 2) to highlight the main requirements and challenges based on a taxonomy; 3) to identify open issues and potential future research directions.pu
5G and Beyond for Contact Tracing
Get PDFThe COVID-19 pandemic has suddenly raised the need for technological solutions capable to trace
contacts of people and provide location-based analytics. Several countries have adopted proximity-based (short-range) technologies, such as Bluetooth, which however appear hindered by deployment issues, security leakages, lack of reliability, and data governance concerns. This paper posits that 5G and beyond can play a primary role in contact tracing and group movement monitoring. Contact tracing based on 5G location-based analytics benefits from the pervasive deployment of cellular networks, the several
years of effort to design cellular standards for localization and analytics, and the best practices of cellular operators to handle location data.H2020 EUMinisterio de Ciencia, Innovación y UniversidadesTRUEpu
On Telemetry and Classifiers
No full textThis presentation consists of two recent pieces inspired by Kirill. One is our last joint work submitted this January and accepted for publication already after Kirill's death. The other is an invited paper that we intended to write together. When I reached out to Kirill about writing it, he was already in the coma. Instead, I wrote and published the paper with Pavel and Vitalii, the only two PhD students whom Kirill graduated.
The first part deals with in-band real-time telemetry, which is a promising direction for management of modern programmable networks. While network noise in the form of packet reordering and loss affects in-band collection of distributed state, there is a need to compute telemetry functions on the collected state correctly despite the network noise. To address this common need, we propose TeleNoise that equips each packet with few sync bits and offers primitives of group affiliation and group completion to support noise-resilient computation of per-group telemetry functions. We give real-world examples of such functions, elaborate on the role of TeleNoise in a modular in-band telemetry architecture, and present algorithms for the two TeleNoise primitives. We derive analytical guarantees on correctness and performance of the algorithms and report a trace-driven evaluation that corroborates the effective low-overhead profile of TeleNoise, e.g., the assuredly correct operation and at most 1.6 packets of the average measurement lag for 12-packet groups and 3 sync bits.
The second part is on the topic of semantically equivalent transformation of packet classifiers, which Kirill pioneered in his SIGCOMM 2014 article. We explore transformation of ternary classifiers to equivalent prefix representations so that classification can benefit from efficient Longest Prefix Match solutions. We propose the property of prefix disjointness and design PREDICAT, a method that leverages this new property in combination with a variety of existing techniques to convert an arbitrary ternary classifier to an equivalent prefix representation. The paper analyzes prefix disjointness and evaluates PREDICAT against state-of-the-art transformation alternatives on a packet classification benchmark in regard to the number of lookups. The evaluation shows that PREDICAT outperforms a ternary-to-binary method by up to an order of magnitude, improves on another ternary-to-prefix solution by up to a factor of 5, and performs similarly to a ternary-to-ternary approach that requires costly power-hungry Ternary Content-Addressable Memories to efficiently handle the resulting ternary representation.FALSEpu
How resilient is the Open Web to the COVID-19 pandemic?
Get PDFhttps://doi.org/10.1016/j.tele.2021.101692In this paper we refer to the Open Web to the set of services offered freely to Internet users, representing a pillar of modern societies. Despite its importance for society, it is unknown how the COVID-19 pandemic is affecting the Open Web. In this paper, we address this issue, focusing our analysis on Spain, one of the countries which have been most impacted by the pandemic.
On the one hand, we study the impact of the pandemic in the financial backbone of the Open Web, the online advertising business. To this end, we leverage concepts from Supply–Demand economic theory to perform a careful analysis of the elasticity in the supply of ad-spaces to the financial shortage of the online advertising business and its subsequent reduction in ad spaces’ price. On the other hand, we analyze the distribution of the Open Web composition across business categories and its evolution during the COVID-19 pandemic. These analyses are conducted between Jan 1st and Dec 31st, 2020, using a reference dataset comprising information from more than 18 billion ad spaces.
Our results indicate that the Open Web has experienced a moderate shift in its composition across business categories. However, this change is not produced by the financial shortage of the online advertising business, because as our analysis shows, the Open Web’s supply of ad spaces is inelastic (i.e., insensitive) to the sustained low-price of ad spaces during the pandemic. Instead, existing evidence suggests that the reported shift in the Open Web composition is likely due to the change in the users’ online behavior (e.g., browsing and mobile apps utilization patterns).Spanish State Research Agency - Spanish Ministry of Science and InnovationDepartment of Education and Research of the Regional Government of MadridRegional Government of MadridTRUEpu