Catálogo de publicaciones - revistas

<jats:p>Health data are being increasingly sensed from the health-based wearable Internet of Things (IoT) devices, providing much-needed fitness and health tracking. However, data generated also present opportunities within computer security, specifically with biometric systems used for identification and authentication purposes. This article performs a systematic review of health-based IoT data collected from wearable IoT technology. This involved performing research in the underlying data sources, what they are collected for in terms of their health monitoring, and the underlying data characteristics. Furthermore, it explores existing work in computer security using these data sources, identifying key themes of work, key limitations, and challenges. Finally, key opportunities are provided as summaries to the potential of health-based IoT data, highlighting challenges that are yet to be addressed, which motivate areas of future work.</jats:p>

<jats:p> Indoor device-free localization and tracking can bring both convenience and privacy to users compared with traditional solutions such as camera-based surveillance and RFID tag-based tracking. Technologies such as Wi-Fi, wireless sensor, and infrared have been used to localize and track people living in care homes and office buildings. However, the presence of multiple residents introduces further challenges, such as the ambiguity in sensor measurements and target identity, to localization and tracking. In this article, we survey the latest development of device-free indoor localization and tracking in the multi-resident environment. We first present the fundamentals of device-free localization and tracking. Then, we discuss and compare the <jats:italic>technologies</jats:italic> used in device-free indoor localization and tracking. After discussing the steps involved in multi-resident localization and tracking including target detection, target counting, target identification, localization, and tracking, the <jats:italic>techniques</jats:italic> related to each step are classified and discussed in detail along with the performance metrics. Finally, we identify the research gap and point out future research directions. To the best of our knowledge, this survey is the most comprehensive work that covers a wide spectrum of the research area of device-free indoor localization and tracking. </jats:p>

<jats:p>The demand for Blockchain innovation and the significance of its application has inspired ever-progressing exploration in various scientific and practical areas. Even though it is still in the initial testing stage, the blockchain is being viewed as a progressive solution to address present-day technology concerns, such as decentralization, identity, trust, character, ownership of data, and information-driven choices. Simultaneously, the world is facing an increase in the diversity and quantity of digital information produced by machines and users. While effectively looking for the ideal approach to storing and processing cloud data, the blockchain innovation provides significant inputs. This article reviews the application of blockchain technology for securing cloud storage.</jats:p>

<jats:p>Over the past decade, the Internet of Things (IoT) has advanced rapidly. New technologies have been proposed and existing approaches optimised to meet user, society and industry requirements. However, as the complexity and heterogeneity of the traffic that flows through the networks are continuously growing, the innovation becomes difficult to achieve in both IoT and legacy networks. This article provides an overview of IoT application domains from a traffic characteristics perspective. Specifically, it identifies several groups of major IoT application use cases and discusses the exhibited traffic characteristics, used network technologies for implementation, and their feasibility as well as challenges. We stress that a key factor in future IoT development is network technologies and the way they handle and forward network traffic. The traffic characteristics emerging from this work can serve as a basis for future design proposals to develop more efficient solutions and improve the network technologies.</jats:p>

<jats:p>Validating the network paths taken by packets is critical in constructing a secure Internet architecture. Any feasible solution must both enforce packet forwarding along end-host specified paths and verify whether packets have taken those paths. However, the current Internet supports neither enforcement nor verification. Likely due to the radical changes to the Internet architecture and a long-standing confusion between routing and forwarding, only limited solutions for path validation exist in the literature. This survey article aims to reinvigorate research on the essential topic of path validation by crystallizing not only how path validation works but also where seemingly qualified solutions fall short. The analyses explore future research directions in path validation aimed at improving security, privacy, and efficiency.</jats:p>

<jats:p>Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. Timely detection of traffic violations and abnormal behavior of pedestrians at public places through computer vision and visual surveillance can be highly effective for maintaining traffic order in cities. However, despite a handful of computer vision–based techniques proposed in recent times to understand the traffic violations or other types of on-road anomalies, no methodological survey is available that provides a detailed insight into the classification techniques, learning methods, datasets, and application contexts. Thus, this study aims to investigate the recent visual surveillance–related research on anomaly detection in public places, particularly on road. The study analyzes various vision-guided anomaly detection techniques using a generic framework such that the key technical components can be easily understood. Our survey includes definitions of related terminologies and concepts, judicious classifications of the vision-guided anomaly detection approaches, detailed analysis of anomaly detection methods including deep learning–based methods, descriptions of the relevant datasets with environmental conditions, and types of anomalies. The study also reveals vital gaps in the available datasets and anomaly detection capability in various contexts, and thus gives future directions to the computer vision–guided anomaly detection research. As anomaly detection is an important step in automatic road traffic surveillance, this survey can be a useful resource for interested researchers working on solving various issues of Intelligent Transportation Systems (ITS).</jats:p>

<jats:p>We describe a problem in complex networks we call the Node Vector Distance (NVD) problem, and we survey algorithms currently able to address it. Complex networks are a useful tool to map a non-trivial set of relationships among connected entities, or nodes. An agent—e.g., a disease—can occupy multiple nodes at the same time and can spread through the edges. The node vector distance problem is to estimate the distance traveled by the agent between two moments in time. This is closely related to the Optimal Transportation Problem (OTP), which has received attention in fields such as computer vision. OTP solutions can be used to solve the node vector distance problem, but they are not the only valid approaches. Here, we examine four classes of solutions, showing their differences and similarities both on synthetic networks and real world network data. The NVD problem has a much wider applicability than computer vision, being related to problems in economics, epidemiology, viral marketing, and sociology, to cite a few. We show how solutions to the NVD problem have a wide range of applications, and we provide a roadmap to general and computationally tractable solutions. We have implemented all methods presented in this article in a publicly available open source library, which can be used for result replication.</jats:p>

<jats:p>Platforms combining Central Processing Systems (CPUs) with Field Programmable Gate Arrays (FPGAs) have become popular, as they promise high performance with energy efficiency. This is the result of the combination of FPGA accelerators tuned to the application, with the CPU providing the programming flexibility. Unfortunately, the security of these new platforms has received little attention: The classic software security assumption that hardware is immutable no longer holds. It is expected that attack surfaces will expand and threats will evolve, hence the trust models, and security solutions should be prepared. The attacker model should be enhanced and consider the following three basic entities as the source of threats: applications run by users, accelerators designed by third-party developers, and the cloud service providers enabling the computation on their platforms. In our work, we review current trust models and existing security assumptions and point out their shortcomings. We survey existing research that target secure remote FPGA configuration, the protection of intellectual property, and secure shared use of FPGAs. When combined, these are the foundations to build a solution for secure use of FPGAs in the cloud. In addition to analysing the existing research, we provide discussions on how to improve it and disclose various concerns that have not been addressed yet.</jats:p>

<jats:p>Data on social media contain a wealth of user information. Big data research of social media data may also support standard surveillance approaches and provide decision-makers with usable information. These data can be analyzed using Natural Language Processing (NLP) and Machine Learning (ML) techniques to detect signs of mental disorders that need attention, such as depression and suicide ideation. This article presents the recent trends and tools that are used in this field, the different means for data collection, and the current applications of ML and NLP in the surveillance of public mental health. We highlight the best practices and the challenges. Furthermore, we discuss the current gaps that need to be addressed and resolved.</jats:p>

<jats:p>Nowadays, with the recent advances of wireless underwater communication and acoustic sensor devices technology, we are witnessing a surge in the exploration and exploitation of the ocean’s abundant natural resources. Accordingly, to fulfill the requirements of the exploration of the ocean, researchers have focused their work toward the design of methods and algorithms for the underwater acoustic sensor networks (UASNs). Although considerable research effort has been devoted to the development of a variety of UASN-based applications, very limited work has addressed the algorithmic design and analysis for UASN. To this end, we propose to provide a comprehensive design, development, and analysis of algorithms and protocols for UASNs. We discuss each of the fundamental UASN building blocks, such as (i) underwater acoustic communication channel modeling, (ii) sustainable coverage and target detection, (iii) Medium Access Control (MAC-layer design and time synchronization, (iv) localization algorithms design, and (v) underwater routing protocol. Then, we illustrate the different protocols from each category and compare their benefits and drawbacks. Finally, we discuss a few potential directions for future research related to the design of future generations of UASNs.</jats:p>