Noel Crespi

The Cluster Between Internet of Things and Social Networks: Review and Research Challenges

The cluster between Internet of Things (IoT) and social networks (SNs) enables the connection of people to the ubiquitous computing universe. In this framework, the information coming from the environment is provided by the IoT, and the SN brings the glue to allow human-to-device interactions. This paper explores the novel paradigm for ubiquitous computing beyond IoT, denoted by Social Internet of Things (SIoT). Although there have been early-stage studies in social-driven IoT, they merely use one or some properties of SIoT to improve a number of specific performance variables. Therefore, this paper first addresses a complete view on SIoT and key perspectives to envision the real ubiquitous computing. Thereafter, a literature review is presented along with the evolutionary history of IoT research from Intranet of Things to SIoT. Finally, this paper proposes a generic SIoT architecture and presents a discussion about enabling technologies, research challenges, and open issues.

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 WSNs 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.

Alike people, alike interests? Inferring interest similarity in online social networks

Understanding how much two individuals are alike in their interests (i.e., interest similarity) has become virtually essential for many applications and services in Online Social Networks (OSNs). Since users do not always explicitly elaborate their interests in OSNs like Facebook, how to determine users interest similarity without fully knowing their interests is a practical problem. In this paper, we investigate how users interest similarity relates to various social features (e.g. geographic distance); and accordingly infer whether the interests of two users are alike or unalike where one of the users interests are unknown. Relying on a large Facebook dataset, which contains 479,048 users and 5,263,351 user-generated interests, we present comprehensive empirical studies and verify the homophily of interest similarity across three interest domains (movies, music and TV shows). The homophily reveals that people tend to exhibit more similar tastes if they have similar demographic information (e.g., age, location), or if they are friends. It also shows that the individuals with a higher interest entropy usually share more interests with others. Based on these results, we provide a practical prediction model under a real OSN environment. For a given user with no interest information, this model can select some individuals who not only exhibit many interests but also probably achieve high interest similarities with the given user. Eventually, we illustrate a use case to demonstrate that the proposed prediction model could facilitate decision-making for OSN applications and services. Pose a practical research problem: how to infer the interest similarity of two users where we do not know one of the users interests.Reveal the homophily of interest similarity with respect to various social features, relying on a large Facebook dataset (479,048 users and 5,263,351 user-generated interests).Devise an interest similarity prediction model based on the learned social features.A recommendation system for new users is illustrated to show the practicality of the proposed interest similarity prediction model.

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.

User Identity for WebRTC Services: A Matter of Trust

Secured interpersonal communications should come with asserted user identities and trust between the involved parties. No single trust model exists in Web Real-Time Communications services, so neither is there a single identity architecture. The authors discuss different models for provisioning user identity and their impact on user privacy.

Dynamic Social Structure of Things: A Contextual Approach in CPSS

The emergence of cyber-physical-social systems (CPSS) and context-aware technologies has helped boost a growing interest in building frameworks for adaptive smart services that hide heterogeneity in the infrastructure and support services by seamlessly integrating the cyber, physical, and social worlds. However, this entails an enormous amount of computational and networking contextual complexity. Here, the proposed smart services framework in CPSS (called Dynamic Social Structure of Things, or DSSoT) boosts sociality and narrows down the contextual complexity based on situational awareness. DSSoT monitors spatiotemporal situations and, depending on users individual goals and other social aspects, induces and structures relevant social objects and smart services in a temporal network of interactions. An application using DSSoT, called Airport Dynamic Social, provides a proof of concept.

An edge operating system enabling anything-as -a-service

This article argues that SDN and NFV, together with cloud and edge-fog computing, can be seen as different facets of a systemic transformation of telecommunications and ICT, called softwarization. The first impact will be at the edge of current telecommunications infrastructures, which are becoming powerful network and service platforms. The edge operating system (EOS) software architecture is proposed as the means to get there. In fact, the main feature of EOS is to bring several service domains, such as cloud robotics, Internet of Things, and Tactile Internet, into convergence at the edge. The development of EOS leverages available open source software. A use case is described to validate the EOS with a proof-of-concept.

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.

CSD: A multi-user similarity metric for community recommendation in online social networks

Abstract Communities are basic components in networks. As a promising social application, community recommendation selects a few items (e.g., movies and books) to recommend to a group of users. It usually achieves higher recommendation precision if the users share more interests; whereas, in plenty of communities (e.g., families, work groups), the users often share few. With billions of communities in online social networks, quickly selecting the communities where the members are similar in interests is a prerequisite for community recommendation. To this end, we propose an easy-to-compute metric, Community Similarity Degree (CSD), to estimate the degree of interest similarity among multiple users in a community. Based on 3460 emulated Facebook communities, we conduct extensive empirical studies to reveal the characteristics of CSD and validate the effectiveness of CSD. In particular, we demonstrate that selecting communities with larger CSD can achieve higher recommendation precision. In addition, we verify the computation efficiency of CSD: it costs less than 1 hour to calculate CSD for over 1 million of communities. Finally, we draw insights about feasible extensions to the definition of CSD, and point out the practical uses of CSD in a variety of applications other than community recommendation.

Controlling enterprise context-based session policy and mapping it to mobile broadband policy rules

In this paper we propose a method of translating enterprise business objectives into service delivery policy rules in Mobile Broadband networks. This enables enterprises to control their own session policies for BYOD (Bring Your Own Device) users and apply selective funding with prioritized service delivery. The proposed eBC (enterprise Business Context) Policy process uses internal corporate data to define session context attributes, which are evaluated against business policies to produce an eBC profile. This is used first to grant funding or defer the request to the users carrier as `personal use. Funded requests are processed further to determine service authorization levels and session delivery profiles. Finally, these values are mapped to 3GPP PCC parameters. We examine the feasibility of enterprises controlling and conveying session policies in terms of routing and intercepting employees requests, and in conveying policy information to carriers using standard interfaces and accepted 3GPP models, such as Sponsoring Data and Virtual Networks.