arXiv subjects

Noel Crespi

Publications and source records attributed to Noel Crespi.

53 records · Page 3Linked to original sources

arXiv2017

Popularity Evolution of Professional Users on Facebook

Popularity in social media is an important objective for professional users (e.g. companies, celebrities, and public figures, etc). A simple yet prominent metric utilized to measure the popularity of a user is the number of fans or followers she succeed to attract to her page. Popularity is influenced by several factors which identifying them is an interesting research topic. This paper aims to understand this phenomenon in social media by exploring the popularity evolution for professional users in Facebook. To this end, we implemented a crawler and monitor the popularity evolution trend of 8k most popular professional users on Facebook over a period of 14 months. The collected dataset includes around 20 million popularity values and 43 million posts. We characterized different popularity evolution patterns by clustering the users temporal number of fans and study them from various perspectives including their categories and level of activities. Our observations show that being active and famous correlate positively with the popularity trend.

cs.SI↗

arXiv2017

Understanding the evolution of multimedia content in the Internet through BitTorrent glasses

Today's Internet traffic is mostly dominated by multimedia content and the prediction is that this trend will intensify in the future. Therefore, main Internet players, such as ISPs, content delivery platforms (e.g. Youtube, Bitorrent, Netflix, etc) or CDN operators, need to understand the evolution of multimedia content availability and popularity in order to adapt their infrastructures and resources to satisfy clients requirements while they minimize their costs. This paper presents a thorough analysis on the evolution of multimedia content available in BitTorrent. Specifically, we analyze the evolution of four relevant metrics across different content categories: content availability, content popularity, content size and user's feedback. To this end we leverage a large-scale dataset formed by 4 snapshots collected from the most popular BitTorrent portal, namely The Pirate Bay, between Nov. 2009 and Feb. 2012. Overall our dataset is formed by more than 160k content that attracted more than 185M of download sessions.

cs.NI↗

arXiv2017

Characterization of Cross-posting Activity for Professional Users across Facebook, Twitter and Google+

Professional players in social media (e.g., big companies, politician, athletes, celebrities, etc) are intensively using Online Social Networks (OSNs) in order to interact with a huge amount of regular OSN users with different purposes (marketing campaigns, customer feedback, public reputation improvement, etc). Hence, due to the large catalog of existing OSNs, professional players usually count with OSN accounts in different systems. In this context an interesting question is whether professional users publish the same information across their OSN accounts, or actually they use different OSNs in a different manner. We define as cross-posting activity the action of publishing the same information in two or more OSNs. This paper aims at characterizing the cross-posting activity of professional users across three major OSNs, Facebook, Twitter and Google+. To this end, we perform a large-scale measurement-based analysis across more than 2M posts collected from 616 professional users with active accounts in the three referred OSNs. Then we characterize the phenomenon of cross posting and analyze the behavioral patterns based on the identified characteristics.

cs.SI↗

arXiv2017

How far is Facebook from me? Facebook network infrastructure analysis

Facebook is today the most popular social network with more than one billion subscribers worldwide. To provide good quality of service (e.g., low access delay) to their clients, FB relies on Akamai, which provides a worldwide content distribution network with a large number of edge servers that are much closer to FB subscribers. In this article we aim to depict a global picture of the current FB network infrastructure deployment taking into account both native FB servers and Akamai nodes. Toward this end, we have performed a measurement-based analysis during a period of two weeks using 463 Planet- Lab nodes distributed across 41 countries. Based on the obtained data we compare the average access delay that nodes in different countries experience accessing both native FB servers and Akamai nodes. In addition, we obtain a wide view of the deployment of Akamai nodes serving FB users worldwide. Finally, we analyze the geographical coverage of those nodes, and demonstrate that in most of the cases Akamai nodes located in a particular country service not only local FB subscribers, but also FB users located in nearby countries.

cs.NI↗

arXiv2017

Analysis of publicly disclosed information in Facebook profiles

Facebook, the most popular Online social network is a virtual environment where users share information and are in contact with friends. Apart from many useful aspects, there is a large amount of personal and sensitive information publicly available that is accessible to external entities/users. In this paper we study the public exposure of Facebook profile attributes to understand what type of attributes are considered more sensitive by Facebook users in terms of privacy, and thus are rarely disclosed, and which attributes are available in most Facebook profiles. Furthermore, we also analyze the public exposure of Facebook users by accounting the number of attributes that users make publicly available on average. To complete our analysis we have crawled the profile information of 479K randomly selected Facebook users. Finally, in order to demonstrate the utility of the publicly available information in Facebook profiles we show in this paper three case studies. The first one carries out a gender-based analysis to understand whether men or women share more or less information. The second case study depicts the age distribution of Facebook users. The last case study uses data inferred from Facebook profiles to map the distribution of worldwide population across cities according to its size.

cs.SI↗

arXiv2017

NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media

Nowadays, a big part of people rely on available content in social media in their decisions (e.g. reviews and feedback on a topic or product). The possibility that anybody can leave a review provide a golden opportunity for spammers to write spam reviews about products and services for different interests. Identifying these spammers and the spam content is a hot topic of research and although a considerable number of studies have been done recently toward this end, but so far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature type. In this study, we propose a novel framework, named NetSpam, which utilizes spam features for modeling review datasets as heterogeneous information networks to map spam detection procedure into a classification problem in such networks. Using the importance of spam features help us to obtain better results in terms of different metrics experimented on real-world review datasets from Yelp and Amazon websites. The results show that NetSpam outperforms the existing methods and among four categories of features; including review-behavioral, user-behavioral, reviewlinguistic, user-linguistic, the first type of features performs better than the other categories.

cs.SI↗

arXiv2016

An EV Charging Scheduling Mechanism to Maximize User Convenience and Cost Efficiency

This paper studies charging scheduling problem of electric vehicles (EVs) in the scale of a microgrid (e.g., a university or town) where a set of charging stations are controlled by a central aggregator. A bi-objective optimization problem is formulated to jointly optimize total charging cost and user convenience. Then, a close-to-optimal online scheduling algorithm is proposed as solution. The algorithm achieves optimal charging cost and is near optimal in terms of user convenience. Moreover, the proposed method applies an efficient load forecasting technique to obtain future load information. The algorithm is assessed through simulation and compared to the previous studies. The results reveal that our method not only improves previous alternative methods in terms of Pareto-optimal solution of the bi-objective optimization problem, but also provides a close approximation for the load forecasting.

eess.SY↗

arXiv2016

Maximum-Quality Tree Construction for Deadline-Constrained Aggregation in WSNs

In deadline-constrained wireless sensor networks (WSNs), quality of aggregation (QoA) is determined by the number of participating nodes in the data aggregation process. The previous studies have attempted to propose optimal scheduling algorithms to obtain the maximum QoA assuming a fixed underlying aggregation tree. However, there exists no prior work to address the issue of constructing optimal aggregation tree in deadline-constraints WSNs. The structure of underlying aggregation tree is important since our analysis demonstrates that the ratio between the maximum achievable QoAs of different trees could be as large as O(2^D), where D is the deadline. This paper casts a combinatorial optimization problem to address optimal tree construction for deadline-constrained data aggregation in WSNs. While the problem is proved to be NP-hard, we employ the recently proposed Markov approximation framework and devise two distributed algorithms with different computation overheads to find close-to-optimal solutions with bounded approximation gap. To further improve the convergence of the proposed Markov-based algorithms, we devise another initial tree construction algorithm with low computational complexity. Our extensive experiments for a set randomly-generated scenarios demonstrate that the proposed algorithms outperforms the existing alternative methods by obtaining better quality of aggregations.

cs.DC↗

arXiv2016

Microgrid Revenue Maximization by Charging Scheduling of EVs in Multiple Parking Stations

Nowadays, there has been a rapid growth in global usage of the electronic vehicles (EV). Despite apparent environmental and economic advantages of EVs, their high demand charging jobs pose an immense challenge to the existing electricity grid infrastructure. In microgrids, as the small-scale version of traditional power grid, however, the EV charging scheduling is more challenging. This is because, the microgrid owner, as a large electricity customer, is interested in shaving its global peak demand, i.e., the aggregated demand over multiple parking stations, to reduce total electricity cost. While the EV charging scheduling problem in single station scenario has been studied extensively in the previous research, the microgrid-level problem with multiple stations subject to a global peak constraint is not tackled. This paper aims to propose a near-optimal EV charging scheduling mechanism in a microgrid governed by a single utility provider with multiple charging stations. The goal is to maximize the total revenue while respecting both local and global peak constraints. The underlying problem, however, is a NP-hard mixed integer linear problem which is difficult to tackle and calls for approximation algorithm design. We design a primaldual scheduling algorithm which runs in polynomial time and achieves bounded approximation ratio. Moreover, the proposed global scheduling algorithm applies a valley-filling strategy to further reduce the global peak. Simulation results show that the performance of the proposed algorithm is 98% of the optimum, which is much better than the theoretical bound obtained by our approximation analysis. Our algorithm reduces the peak demand obtained by the existing alternative algorithm by 16% and simultaneously achieves better resource utilization.

eess.SY↗

arXiv2016

A Trust Model for Data Sharing in Smart Cities

The data generated by the devices and existing infrastructure in the Internet of Things (IoT) should be shared among applications. However, data sharing in the IoT can only reach its full potential when multiple participants contribute their data, for example when people are able to use their smartphone sensors for this purpose. We believe that each step, from sensing the data to the actionable knowledge, requires trust-enabled mechanisms to facilitate data exchange, such as data perception trust, trustworthy data mining, and reasoning with trust related policies. The absence of trust could affect the acceptance of sharing data in smart cities. In this study, we focus on data usage transparency and accountability and propose a trust model for data sharing in smart cities, including system architecture for trust-based data sharing, data semantic and abstraction models, and a mechanism to enhance transparency and accountability for data usage. We apply semantic technology and defeasible reasoning with trust data usage policies. We built a prototype based on an air pollution monitoring use case and utilized it to evaluate the performance of our solution.

cs.CY↗

arXiv2016

Wireless Sensor Network Virtualization: A Survey

Wireless Sensor Networks (WSNs) are the key components of the emerging Internet-of-Things (IoT) paradigm. They are now ubiquitous and used in a plurality of application domains. WSNs are still domain specific and usually deployed to support a specific application. However, as WSN nodes are becoming more and more powerful, it is getting more and more pertinent to research how multiple applications could share a very same WSN infrastructure. Virtualization is a technology that can potentially enable this sharing. This paper is a survey on WSN virtualization. It provides a comprehensive review of the state-of-the-art and an in-depth discussion of the research issues. We introduce the basics of WSN virtualization and motivate its pertinence with carefully selected scenarios. Existing works are presented in detail and critically evaluated using a set of requirements derived from the scenarios. The pertinent research projects are also reviewed. Several research issues are also discussed with hints on how they could be tackled.

cs.NI↗

arXiv2015

Reality Mining with Mobile Big Data: Understanding the Impact of Network Structure on Propagation Dynamics

Information and epidemic propagation dynamics in complex networks is truly important to discover and control terrorist attack and disease spread. How to track, recognize and model such dynamics is a big challenge. With the popularity of intellectualization and the rapid development of Internet of Things (IoT), massive mobile data is automatically collected by millions of wireless devices (e.g., smart phone and tablet). In this article, as a typical use case, the impact of network structure on epidemic propagation dynamics is investigated by using the mobile data collected from the smart phones carried by the volunteers of Ebola outbreak areas. On this basis, we propose a model to recognize the dynamic structure of a network. Then, we introduce and discuss the open issues and future work for developing the proposed recognition model.

cs.SI↗

arXiv2015

Are You Really Hidden? Predicting Current City from Profile and Social Relationship

Privacy has become a major concern in Online Social Networks (OSNs) due to threats such as advertising spam, online stalking and identity theft. Although many users hide or do not fill out their private attributes in OSNs, prior studies point out that the hidden attributes may be inferred from some other public information. Thus, users' private information could still be at stake to be exposed. Hitherto, little work helps users to assess the exposure probability/risk that the hidden attributes can be correctly predicted, let alone provides them with pointed countermeasures. In this article, we focus our study on the exposure risk assessment by a particular privacy-sensitive attribute - current city - in Facebook. Specifically, we first design a novel current city prediction approach that discloses users' hidden `current city' from their self-exposed information. Based on 371,913 Facebook users' data, we verify that our proposed prediction approach can predict users' current city more accurately than state-of-the-art approaches. Furthermore, we inspect the prediction results and model the current city exposure probability via some measurable characteristics of the self-exposed information. Finally, we construct an exposure estimator to assess the current city exposure risk for individual users, given their self-exposed information. Several case studies are presented to illustrate how to use our proposed estimator for privacy protection.

cs.SI↗

arXiv2015

Social and Collaborative Services for Organizations: Back to Requirements

Social and collaborative services have widely spread within the enterprises as they play a part in improving productivity and business outcomes. However, the deployment of these services fluctuates between success and failure. This paper intends to assess their deployment and how they can contribute to value creation in different industries. We investigate the relationship between the services' functionalities and the organizational requirement of these services represented by the coordination. We also consider the organizational transformation driven by servitization and emphasize its impact on the act of coordination. We highlight the tight correlation between the functionalities and the requirement in organic forms which suggests a successful deployment in such enterprises. We nonetheless find that, when the servitization is a strategic intent in organizations with mechanistic characteristics, deploying social and collaborative services can contribute to achieving this aim.

cs.CY↗

arXiv2015

Self-Modeling Based Diagnosis of Software-Defined Networks

Networks built using SDN (Software-Defined Networks) and NFV (Network Functions Virtualization) approaches are expected to face several challenges such as scalability, robustness and resiliency. In this paper, we propose a self-modeling based diagnosis to enable resilient networks in the context of SDN and NFV. We focus on solving two major problems: On the one hand, we lack today of a model or template that describes the managed elements in the context of SDN and NFV. On the other hand, the highly dynamic networks enabled by the softwarisation require the generation at runtime of a diagnosis model from which the root causes can be identified. In this paper, we propose finer granular templates that do not only model network nodes but also their sub-components for a more detailed diagnosis suitable in the SDN and NFV context. In addition, we specify and validate a self-modeling based diagnosis using Bayesian Networks. This approach differs from the state of the art in the discovery of network and service dependencies at run-time and the building of the diagnosis model of any SDN infrastructure using our templates.

cs.NI↗

arXiv2015

A Data Annotation Architecture for Semantic Applications in Virtualized Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have become very popular and are being used in many application domains (e.g. smart cities, security, gaming and agriculture). Virtualized WSNs allow the same WSN to be shared by multiple applications. Semantic applications are situation-aware and can potentially play a critical role in virtualized WSNs. However, provisioning them in such settings remains a challenge. The key reason is that semantic applications provisioning mandates data annotation. Unfortunately it is no easy task to annotate data collected in virtualized WSNs. This paper proposes a data annotation architecture for semantic applications in virtualized heterogeneous WSNs. The architecture uses overlays as the cornerstone, and we have built a prototype in the cloud environment using Google App Engine. The early performance measurements are also presented.

cs.NI↗

arXiv2015

Wireless Sensor Network Virtualization: Early Architecture and Research Perspectives

Wireless sensor networks (WSNs) have become pervasive and are used in many applications and services. Usually the deployments of WSNs are task oriented and domain specific; thereby precluding re-use when other applications and services are contemplated. This inevitably leads to the proliferation of redundant WSN deployments. Virtualization is a technology that can aid in tackling this issue, as it enables the sharing of resources/infrastructure by multiple independent entities. In this paper we critically review the state of the art and propose a novel architecture for WSN virtualization. The proposed architecture has four layers (physical layer, virtual sensor layer, virtual sensor access layer and overlay layer) and relies on the constrained application protocol (CoAP). We illustrate its potential by using it in a scenario where a single WSN is shared by multiple applications; one of which is a fire monitoring application. We present the proof-of-concept prototype we have built along with the performance measurements, and discuss future research directions.

cs.NI↗