Catálogo de publicaciones - revistas

<jats:p>Vehicular Edge Computing (VEC), based on the Edge Computing motivation and fundamentals, is a promising technology supporting Intelligent Transport Systems services, smart city applications, and urban computing. VEC can provide and manage computational resources closer to vehicles and end-users, providing access to services at lower latency and meeting the minimum execution requirements for each service type. This survey describes VEC’s concepts and technologies; we also present an overview of existing VEC architectures, discussing them and exemplifying them through layered designs. Besides, we describe the underlying vehicular communication in supporting resource allocation mechanisms. With the intent to overview the risks, breaches, and measures in VEC, we review related security approaches and methods. Finally, we conclude this survey work with an overview and study of VEC’s main challenges. Unlike other surveys in which they are focused on content caching and data offloading, this work proposes a taxonomy based on the architectures in which VEC serves as the central element. VEC supports such architectures in capturing and disseminating data and resources to offer services aimed at a smart city through their aggregation and the allocation in a secure manner.</jats:p>

<jats:p>Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing). Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.</jats:p>

<jats:p>Network traffic workloads are widely utilized in applied research to verify correctness and to measure the impact of novel algorithms, protocols, and network functions. We provide a comprehensive survey of traffic generators referenced by researchers over the last 13 years, providing in-depth classification of the functional behaviors of the most frequently cited generators. These classifications are then used as a critical component of a methodology presented to aid in the selection of generators derived from the workload requirements of future research.</jats:p>

<jats:p> With the increasing interest in the content creation field in multiple sectors such as media, education, and entertainment, there is an increased trend in the papers that use AI algorithms to generate content such as images, videos, audio, and text. <jats:bold>Generative Adversarial Networks (GANs)</jats:bold> is one of the promising models that synthesizes data samples that are similar to real data samples. While the variations of GANs models in general have been covered to some extent in several survey papers, to the best of our knowledge, this is the first paper that reviews the state-of-the-art video GANs models. This paper first categorizes GANs review papers into general GANs review papers, image GANs review papers, and special field GANs review papers such as anomaly detection, medical imaging, or cybersecurity. The paper then summarizes the main improvements in GANs that are not necessarily applied in the video domain in the first run but have been adopted in multiple video GANs variations. Then, a comprehensive review of video GANs models are provided under two main divisions based on existence of a condition. The conditional models are then further classified according to the provided condition into audio, text, video, and image. The paper concludes with the main challenges and limitations of the current video GANs models. </jats:p>

<jats:p>The increasing amount of human-related and/or human-originated data in current systems is both an opportunity and a challenge. Nevertheless, despite relying on the processing of large amounts of data, most of the so-called smart systems that we have nowadays merely consider humans as sources of data, not as system beneficiaries or even active “components.” For truly smart systems, we need to create systems that are able to understand human actions and emotions, and take them into account when deciding on the system behavior. Naturally, in order to achieve this, we first have to empower systems with human sensing capabilities, possibly in ways that are as inconspicuous as possible. In this context, in this article we survey existing approaches to unobtrusive monitorization of human beings, namely, of their activity, vital signs, and emotional states. After setting a taxonomy for human sensing, we proceed to present and analyze existing solutions for unobtrusive sensing. Subsequently, we identify and discuss open issues and challenges in this area. Although there are surveys that address some of the concerned fields of research, such as healthcare, human monitorization, or even the use-specific techniques like channel state information or image recognition, as far as we know this is the first comprehensive survey on unobtrusive sensing of human beings.</jats:p>

<jats:p>Personal information of online social networks (OSNs) is governed by the privacy policies chosen by users besides OSN’s policies. Users make these decisions using privacy mechanisms, but privacy problems and regrets are daily reported. This article reviews current privacy mechanisms and solutions. For this, we analyze all the sub-decisions and elements of online communication involved in the privacy decision-making process. However, the differences in users’ motivations and the disclosure of too sensitive information (among others) can lead to loss of privacy. In this work, we identify requirements such as automation, preference-centered, relationship-based, and multi-party privacy mechanisms, which have been more researched. But also other requirements (recently emerged), such as privacy preservation with risk metrics, explainability, and ephemeral messages. We explore all the advances made in the literature, and we have seen that most of these have been focused on matching the users’ preferences with their decision (which is not appropriate, because users cannot evaluate all of the potential privacy scenarios) instead of assessing privacy risk metrics, adaptation, and explainability. Therefore, we have identified open challenges, such as metrics for assessing privacy risks, explainable solutions for users, ephemeral communication solutions, and the application of these requirements to the multi-party privacy scenario.</jats:p>

<jats:p>Fog computing is a paradigm that brings computational resources and services to the network edge in the vicinity of user devices, lowering latency and connecting with cloud computing resources. Unlike cloud computing, fog resources are based on constrained and heterogeneous nodes whose connectivity can be unstable. In this complex scenario, there is a need to define and implement orchestration processes to ensure that applications and services can be provided, considering the settled agreements. Although some publications have dealt with orchestration in fog computing, there are still some diverse definitions and functional intersection with other areas, such as resource management and monitoring. This article presents a systematic review of the literature with focus on orchestration in fog computing. A generic architecture of fog orchestration is presented, created from the consolidation of the analyzed proposals, bringing to light the essential functionalities addressed in the literature. This work also highlights the main challenges and open research questions.</jats:p>

<jats:p>In general, biometry-based control systems may not rely on individual expected behavior or cooperation to operate appropriately. Instead, such systems should be aware of malicious procedures for unauthorized access attempts. Some works available in the literature suggest addressing the problem through gait recognition approaches. Such methods aim at identifying human beings through intrinsic perceptible features, despite dressed clothes or accessories. Although the issue denotes a relatively long-time challenge, most of the techniques developed to handle the problem present several drawbacks related to feature extraction and low classification rates, among other issues. However, deep learning-based approaches recently emerged as a robust set of tools to deal with virtually any image and computer-vision-related problem, providing paramount results for gait recognition as well. Therefore, this work provides a surveyed compilation of recent works regarding biometric detection through gait recognition with a focus on deep learning approaches, emphasizing their benefits and exposing their weaknesses. Besides, it also presents categorized and characterized descriptions of the datasets, approaches, and architectures employed to tackle associated constraints.</jats:p>

<jats:p>Network representation learning (NRL) advances the conventional graph mining of social networks, knowledge graphs, and complex biomedical and physics information networks. Dozens of NRL algorithms have been reported in the literature. Most of them focus on learning node embeddings for homogeneous networks, but they differ in the specific encoding schemes and specific types of node semantics captured and used for learning node embedding. This article reviews the design principles and the different node embedding techniques for NRL over homogeneous networks. To facilitate the comparison of different node embedding algorithms, we introduce a unified reference framework to divide and generalize the node embedding learning process on a given network into preprocessing steps, node feature extraction steps, and node embedding model training for an NRL task such as link prediction and node clustering. With this unifying reference framework, we highlight the representative methods, models, and techniques used at different stages of the node embedding model learning process. This survey not only helps researchers and practitioners gain an in-depth understanding of different NRL techniques but also provides practical guidelines for designing and developing the next generation of NRL algorithms and systems.</jats:p>

<jats:p>With the development of real-time critical systems, the ever-increasing communication data traffic puts forward high-bandwidth and low-delay requirements for communication networks. Therefore, various real-time Ethernet protocols have been proposed, but these protocols are not compatible with each other. The IEEE 802.1 Working Group developed standardized protocols named Audio Video Bridging (AVB) in 2005, and renamed it Time-Sensitive Networking (TSN) later. TSN not only adds new features but also retains the original functions of AVB. Proposing real-time Ethernet modeling and design methodologies is the key to meeting high-bandwidth and low-delay communication requirements. This article surveys the modeling from AVB to TSN, mainly including: (1) AVB and TSN modeling; (2) end-to-end delay modeling; (3) real-time scheduling modeling; (4) reliability modeling; and (5) security modeling. Based on these models, this article surveys the recent advances in real-time Ethernet design methodologies from AVB to TSN: (1) end-to-end delay analysis from AVB to TSN; (2) real-time scheduling from AVB to TSN; (3) reliability-aware design for TSN; and (4) security-aware design for TSN. Among the above four points, the last two points are only for TSN, because AVB lacks reliability and security mechanisms. This article further takes the automotive use case as an example to discuss the application of TSN in automobiles. Finally, this article discusses the future trends of TSN. By surveying the recent advances and future trends, we hope to provide references for researchers interested in real-time Ethernet modeling and design methodologies for AVB and TSN.</jats:p>