P2P Based Architecture for Global Home Agent Dynamic Discovery in IP Mobility

Mobility in packet networks has become a critical issue in the last years. Mobile IP and the Network Mobility Basic Support Protocol are the IETF proposals to provide mobility. However, both of them introduce performance limitations, due to the presence of an entity (Home Agent) in the communication path. Those problems have been tried to be solved in different ways. A family of solutions has been proposed in order to mitigate those problems by allowing mobile devices to use several geographically distributed Home Agents (thus making shorter the communication path). These techniques require a method to discover a close Home Agent, among those geographically distributed, to the mobile device. This paper proposes a peer-topeer based solution, called Peer-to-Peer Home Agent Network, in order to discover a close Home Agent. The proposed solution is simple, fully global, dynamic and it can be developed in IPv4 and IPv6.No publicad

Analysis of searching mechanisms in hierarchical p2p based overlay networks

Proceedings of: The 6th Annual Mediterranean Ad Hoc Networking Workshop (Med Hoc Net 2007. (Corfu, Greece), June 2007This work presents a study of searching mechanisms in Peer-to-Peer (p2p) networks. The aim of this research line is to analyse cross-searching mechanisms that will allow the hierarchical interconnection of p2p networks. A set of relevant metrics for interconnection scenarios are defined to evaluate scalability, robustness and routing latency.This work has been partially supported by the European Union under the IST Content (FP6-2006-IST-507295) project and by the Madrid regional government under the Biogridnet (CAM, S-0505/TIC-0101) project.Publicad

Optimizing the frequency capping: a robust and reliable methodology to define the number of ads to Maximize ROAS

The goal of digital marketing is to connect advertisers with users that are interested in their products. This means serving ads to users, and it could lead to a user receiving hundreds of impressions of the same ad. Consequently, advertisers can define a maximum threshold to the number of impressions a user can receive, referred to as Frequency Cap. However, low frequency caps mean many users are not engaging with the advertiser. By contrast, with high frequency caps, users may receive many ads leading to annoyance and wasting budget. We build a robust and reliable methodology to define the number of ads that should be delivered to different users to maximize the ROAS and reduce the possibility that users get annoyed with the ads" brand. The methodology uses a novel technique to find the optimal frequency capping based on the number of non-clicked impressions rather than the traditional number of received impressions. This methodology is validated using simulations and large-scale datasets obtained from real ad campaigns data. To sum up, our work proves that it is feasible to address the frequency capping optimization as a business problem, and we provide a framework that can be used to configure efficient frequency capping values.The research leading to these results received funding from the European Union’s Horizon 2020 innovation action programme under the grant agreement No 871370 (PIMCITY project); the Ministerio de Economía, Industria y Competitividad, Spain, and the European Social Fund(EU), under the Ramón y Cajal programme (Grant RyC-2015-17732); the Ministerio de Ciencia e Innovación under the project ACHILLES (Grant PID2019-104207RB-I00); the Community of Madrid synergic project EMPATIA-CM (Grant Y2018/TCS-5046); and the Fundación BBVA under the project AERIS

Digital Marketing Attribution: Understanding the User Path

This article belongs to the Section Computer Science & EngineeringDigital marketing is a profitable business generating annual revenue over USD 200B and an inter-annual growth over 20%. The definition of efficient marketing investment strategies across different types of channels and campaigns is a key task in digital marketing. Attribution models are an instrument used to assess the return of investment of different channels and campaigns so that they can assist in the decision-making process. A new generation of more powerful data-driven attribution models has irrupted in the market in the last years. Unfortunately, its adoption is slower than expected. One of the main reasons is that the industry lacks a proper understanding of these models and how to configure them. To solve this issue, in this paper, we present an empirical study to better understand the key properties of user-paths and their impact on attribution models. Our analysis is based on a large-scale dataset including more than 95M user-paths from real advertising campaigns of an international hoteling group. The main contribution of the paper is a set of recommendation to build accurate, interpretable and computationally efficient attribution models such as: (i) the use of linear regression, an interpretable machine learning algorithm, to build accurate attribution models; (ii) user-paths including around 12 events are enough to produce accurate models; (iii) the recency of events considered in the user-paths is important for the accuracy of the model.The research leading to these results has received funding from: the European Union’s Horizon 2020 innovation action programme under grant agreement No 786741 (SMOOTH project) and the gran agreement No 871370 (PIMCITY project); the Ministerio de Economía, Industria y Competitividad, Spain, and the European Social Fund(EU), under the Ramón y Cajal programme (grant RyC-2015-17732);the Ministerio de Ciencia e Innovación under the project ACHILLES (Grant PID2019-104207RB-I00); the Community of Madrid synergic project EMPATIA-CM (Grant Y2018/TCS-5046)

A large-scale analysis of Facebook's user-base and user engagement growth

Understanding the evolution of the user base as well as the user engagement of online services is critical not only for the service operators but also for customers, investors, and users. While we can find research works addressing this issue in online services, such as Twitter, MySpace, or Google+, such detailed analysis is missing for Facebook, which is currently the largest online social network. This paper presents the first detailed study on the demographic and geographic composition and evolution of the user base and user engagement in Facebook over a period of three years. To this end, we have implemented a measurement methodology that leverages the marketing API of Facebook to retrieve actual information about the number of total users and the number of daily active users across 230 countries and age groups ranging between 13 and 65+. The conducted analysis reveals that Facebook is still growing and geographically expanding. Moreover, the growth pattern is heterogeneous across age groups, genders, and geographical regions. In particular, from a demography perspective, Facebook shows the lowest growth pattern among adolescents. Gender-based analysis showed that growth among men is still higher than the growth in women. Our geographical analysis reveals that while Facebook growth is slower in western countries, it has the fastest growth in the developing countries mainly located in Africa and Central Asia; analyzing the penetration of these countries also shows that these countries are at earlier stages of Facebook penetration. Leveraging external socioeconomic datasets, we also showed that this heterogeneous growth can be characterized by indicators, such as availability and access to Internet, Facebook popularity, and factors related with population growth and gender inequality.The work of Y. M. Kassa was supported by the European H2020 Project TYPES under Grant 653449. The work of R. Cuevas was supported in part by the European H2020 Project SMOOTH under Grant 786741, in part by the Spanish Ministry of Economy and Competitiveness, through the 5GCity Project, under Grant TEC2016-76795-C6-3-R, and in part by the La Caixa Foundation under Agreement LCF/PR/MIT17/11820009. The work of A. Cuevas was supported in part by the Ministerio de Economía, Industria y Competitividad, Spain, in part by the European Social Fund through the Ramón Y Cajal under Grant RyC-2015-17732, and in part by the Ministerio de Economía, Industria y Competitividad, Spain, through the Project TEXEO, under Grant TEC2016-80339-R.Publicad

Digital Contact Tracing: Large-scale Geolocation Data as an Alternative to Bluetooth-based Apps' Failure

The currently deployed contact-tracing mobile apps have failed as an efficient solution in the context of the COVID-19 pandemic. None of them has managed to attract the number of active users required to achieve an efficient operation. This urges the research community to re-open the debate and explore new avenues that lead to efficient contact-tracing solutions. This paper contributes to this debate with an alternative contact-tracing solution that leverages already available geolocation information owned by BigTech companies with very large penetration rates in most countries adopting contact-tracing mobile apps. Moreover, our solution provides sufficient privacy guarantees to protect the identity of infected users as well as precluding Health Authorities from obtaining the contact graph from individuals.Comment: 7 pages, 1 figure, 1 tabl

An ad-driven measurement technique for monitoring the browser marketplace

In this paper we present a novel active measurement methodology for monitoring the browser market landscape. It leverages the display ads delivered through online advertising campaigns to collect the browser brand and version of the device receiving the ad. While providing a similar accuracy to traditional techniques based on passive measurements, our methodology offers some advantages: (i) a lower entry barrier for researchers and practitioners interested in measuring the browser marketplace; (ii) it allows targeted measurements, which can be useful to fix biases in the data sample or to analyze specific aspects of the browser market. We analyze the performance, accuracy, and capabilities of our methodology through real experiments that overall produced more than 6M measurements.This work was supported in part by Ministerio de Economía y Empresa, Spain, under the Grant RyC-2015-17732, and in part by the European H2020 projects SMOOTH (786741) and PIMCITY (871370)

Measuring the bittorrent ecosystem: techniques, tips, and tricks

BitTorrent is the most successful peer-to-peer application. In the last years the research community has studied the BitTorrent ecosystem by collecting data from real BitTorrent swarms using different measurement techniques. In this article we present the first survey of these techniques that constitutes a first step in the design of future measurement techniques and tools for analyzing large-scale systems. The techniques are classified into macroscopic, microscopic, and complementary. Macroscopic techniques allow us to collect aggregated information of torrents and present very high scalability, able to monitor up to hundreds of thousands of torrents in short periods of time. Microscopic techniques operate at the peer level and focus on understanding performance aspects such as the peers¿ download rates. They offer higher granularity but do not scale as well as macroscopic techniques. Finally, complementary techniques utilize recent extensions to the BitTorrent protocol in order to obtain both aggregated and peer-level information. The article also summarizes the main challenges faced by the research community to accurately measure the BitTorrent ecosystem such as accurately identifying peers and estimating peers' upload rates. Furthermore, we provide possible solutions to address the described challenges.The research leading to these results has received funding from the European Union Seventh Framework Program (FP7/2007-2013) through the TREND NoE project (grant agreement no. 25774) and the Regional Government of Madrid through the MEDIANET project (S- 2009/TIC-1468).Publicad