Aleksandr Ometov

Visualizing Big Data with augmented and virtual reality: challenges and research agenda

AbstractThis paper provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenges in visualization methods for existing Big Data, as well as to offer novel solutions for issues related to the current state of Big Data Visualization. This paper provides a classification of existing data types, analytical methods, visualization techniques and tools, with a particular emphasis placed on surveying the evolution of visualization methodology over the past years. Based on the results, we reveal disadvantages of existing visualization methods. Despite the technological development of the modern world, human involvement (interaction), judgment and logical thinking are necessary while working with Big Data. Therefore, the role of human perceptional limitations involving large amounts of information is evaluated. Based on the results, a non-traditional approach is proposed: we discuss how the capabilities of Augmented Reality and Virtual Reality could be applied to the field of Big Data Visualization. We discuss the promising utility of Mixed Reality technology integration with applications in Big Data Visualization. Placing the most essential data in the central area of the human visual field in Mixed Reality would allow one to obtain the presented information in a short period of time without significant data losses due to human perceptual issues. Furthermore, we discuss the impacts of new technologies, such as Virtual Reality displays and Augmented Reality helmets on the Big Data visualization as well as to the classification of the main challenges of integrating the technology.

Effects of Heterogeneous Mobility on D2D- and Drone-Assisted Mission-Critical MTC in 5G

mcMTC is starting to play a central role in the industrial Internet of Things ecosystem and have the potential to create high-revenue businesses, including intelligent transportation systems, energy/ smart grid control, public safety services, and high-end wearable applications. Consequently, in the 5G of wireless networks, mcMTC have imposed a wide range of requirements on the enabling technology, such as low power, high reliability, and low latency connectivity. Recognizing these challenges, the recent and ongoing releases of LTE systems incorporate support for lowcost and enhanced coverage, reduced latency, and high reliability for devices at varying levels of mobility. In this article, we examine the effects of heterogeneous user and device mobility -- produced by a mixture of various mobility patterns -- on the performance of mcMTC across three representative scenarios within a multi-connectivity 5G network. We establish that the availability of alternative connectivity options, such as D2D links and drone-assisted access, helps meet the requirements of mcMTC applications in a wide range of scenarios, including industrial automation, vehicular connectivity, and urban communications. In particular, we confirm improvements of up to 40 percent in link availability and reliability with the use of proximate connections on top of the cellular-only baseline.

Feasibility characterization of cryptographic primitives for constrained (wearable) IoT devices

The Internet of Things (IoT) employs smart devices as its building blocks for developing a ubiquitous communication framework. It thus supports a wide variety of application domains, including public safety, healthcare, education, and public transportation. While offering a novel communication paradigm, IoT finds its requirements closely connected to the security issues. The role of security following the fact that a new type of devices known as wearables constitute an emerging area. This paper delivers an applicability study of the state-of-the-art cryptographic primitives for wearable IoT devices, including the pairing-based cryptography. Pairing-based schemes are well-recognized as fundamental enablers for many advanced cryptographic applications, such as privacy protection and identity-based encryption. To deliver a comprehensive view on the computational power of modern wearable devices (smart phones, watches, and embedded devices), we perform an evaluation of a variety of them utilizing bilinear pairing for real-time communication. In order to deliver a complete picture, the obtained bilinear pairing results are complemented with performance figures for classical cryptography (such as block ciphers, digital signatures, and hash functions). Our findings show that wearable devices of today have the needed potential to efficiently operate with cryptographic primitives in real time. Therefore, we believe that the data provided during this research would shed light on what devices are more suitable for certain cryptographic operations.

A novel security-centric framework for D2D connectivity based on spatial and social proximity

Device-to-device (D2D) communication is one of the most promising innovations in the next-generation wireless ecosystem, which improves the degrees of spatial reuse and creates novel social opportunities for users in proximity. As standardization behind network-assisted D2D technology takes shape, it becomes clear that security of direct connectivity is one of the key concerns on the way to its ultimate user adoption. This is especially true when a personal user cluster (that is, a smartphone and associated wearable devices) does not have a reliable connection to the cellular infrastructure. In this paper, we propose a novel framework that embraces security of geographically proximate user clusters. More specifically, we employ game-theoretic mechanisms for appropriate user clustering taking into account both spatial and social notions of proximity. Further, our information security procedures implemented on top of this clustering scheme enable continuous support for secure direct communication even in case of unreliable/unavailable cellular connectivity. Explicitly incorporating the effects of user mobility, we numerically evaluate the proposed framework by confirming that it has the potential to substantially improve the resulting system-wide performance.

A Harmonized Perspective on Transportation Management in Smart Cities: the Novel IoT-Driven Environment for Road Traffic Modeling

The unprecedented growth of today’s cities together with increased population mobility are fueling the avalanche in the numbers of vehicles on the roads. This development led to the new challenges for the traffic management, including the mitigation of road congestion, accidents, and air pollution. Over the last decade, researchers have been focusing their efforts on leveraging the recent advances in sensing, communications, and dynamic adaptive technologies to prepare the deployed road traffic management systems (TMS) for resolving these important challenges in future smart cities. However, the existing solutions may still be insufficient to construct a reliable and secure TMS that is capable of handling the anticipated influx of the population and vehicles in urban areas. Along these lines, this work systematically outlines a perspective on a novel modular environment for traffic modeling, which allows to recreate the examined road networks in their full resemblance. Our developed solution is targeted to incorporate the progress in the Internet of Things (IoT) technologies, where low-power, embedded devices integrate as part of a next-generation TMS. To mimic the real traffic conditions, we recreated and evaluated a practical traffic scenario built after a complex road intersection within a large European city.

Securing Network-Assisted Direct Communication: The Case of Unreliable Cellular Connectivity

Network-assisted device-to-device (D2D) communication is a next-generation wireless technology enabling direct connectivity between proximate user devices under the control of cellular infrastructure. It couples together the centralized and the distributed network architectures, and as such requires respective enablers for secure, private, and trusted data exchange especially when cellular control link is not available at all times. In this work, we conduct the state-of-the-art overview and propose a novel algorithm to maintain security functions of proximate devices in case of unreliable cellular connectivity, whether a new device joins the secure group of users or an existing device leaves it. Our proposed solution and its rigorous mathematical implementation detailed in this work open door to a novel generation of secure proximity-based services and applications in future wireless communication systems.

Fairness characterization in contemporary IEEE 802.11 deployments with saturated traffic load

This paper studies a widely used wireless technology (IEEE 802.11-2014) and the simulation establishment of the efficient Wireless Local Area Network (WLAN) in modern environment. However, currently used saturation based analysis may be applied only for fair systems, hence, the question arises which system may be considered as fair. Mostly used metric for such an analysis (Jains Fairness Index) does not apply for 802.11-2014 standard in a particular case of a small number of users. So, we propose a novel metric to define fairness.

Toward trusted, social-aware D2D connectivity: bridging across the technology and sociality realms

Driven by the unprecedented increase of mobile data traffic, D2D communications technology is rapidly moving into the mainstream of the 5G networking landscape. While D2D connectivity originally emerged as a technology enabler for public safety services, it is likely to remain at the heart of the 5G ecosystem by spawning a wide diversity of proximate applications and services. In this work, we argue that the widespread adoption of the direct communications paradigm is unlikely without embracing the concepts of trust and social-aware cooperation between end users and network operators. However, such adoption remains conditional on identifying adequate incentives that engage humans and their connected devices in a plethora of collective activities. To this end, the mission of our research is to advance the vision of social-aware and trusted D2D connectivity, as well as to facilitate its further adoption. We begin by reviewing the various types of underlying incentives with the emphasis on sociality and trust, discuss these factors specifically for humans and for networked devices (machines), and also propose a novel framework allowing construction of much needed incentive-aware D2D applications. Our supportive system-level performance evaluations suggest that trusted and social-aware direct connectivity has the potential to decisively augment network performance. We conclude by outlining the future perspectives of its development across the research and standardization sectors.

Implementing secure network-assisted D2D framework in live 3GPP LTE deployment

Device-to-Device (D2D) communication constitutes an emerging network paradigm that promises to unlock decisive capacity gains without the need for expensive cellular resources. However, while deployment of this promising enabler technology in 5G-grade mobile networks is currently underway, the complete understanding of feasible use cases and their respective limitations has not yet been provided in literature. Today, employing D2D connectivity both in human-to-human and machine-to-machine scenarios, the attention of research community focuses on security, privacy, and trust. Inspired by this increasing demand, we provide in this paper a comprehensive summary on our live trial of secure cellular-assisted D2D communication technology within the full-featured 3GPP LTE network deployment. Correspondingly, we describe a novel D2D framework capable of delivering secure direct connectivity even if the managing cellular link is temporarily not available (unreliable), so that communicating devices could continue to exchange confidential data in their private coalitions. To this end, our prototype implementation characterizes the practical capabilities of secure D2D communication in dynamic, urban environments suffering from intermittent 3GPP LTE connectivity.

Towards Big Data Visualization for Augmented Reality

This article attempts to summarize challenges in visualization methods for existing Big Data and beyond. Moreover, it classifies types of data, analytical methods, visualization techniques and tools, known up to date, with a particular emphasis placed on surveying evolution of visualization techniques over the past years. Following the identified shortcomings of the existing techniques, the role of human eyes physical limitations on perception of the large amounts of information is evaluated and presented along with discussion on Augmented Realty technology and its capabilities for visualization of Big Data and beyond. Based on the results, a novel approach is proposed -- it allows one to obtain represented information segment in a short period of time without any significant data losses by placing the most essential data in the central area of the human eye visual field. This article also discusses impact of new technologies, such as Augmented Reality displays and helmets, on the Big Data visualization.