440 research outputs found
Authenticated Digital Avatars on Metaverse by Cyber Security Procedures
Metaverse is the next generation Internet, aims to build a fully immersive, hyper spatiotemporal and self sustaining virtual shared space for humans to play, work, shop and socialize. In metaverse, users are represented as digital avatars and using identity, user can shuttle across various virtual worlds (i.e., sub-metaverses) to experience a digital life, as well as make digital creations and economic interactions supported by physical infrastructures and the metaverse engine. Virtual reality headsets are the main devices used to access the Metaverse. Privacy and security concerns of the metaverse. The users need to verify their identity to log into the metaverse platforms, and the security of this phase becomes vital. In this paper, the user authentication methods such as Information-based authentication, biometric based authentication, and multi-model methods are reviewed and compared in terms of users security but in some cases these methods are failed to secure from cyber attacks. In this paper, we proposed,Token-based authentication method to enhance the security for the users to access and work on the virtual environment
Crowd-based cognitive perception of the physical world: Towards the internet of senses
This paper introduces a possible architecture and discusses the research directions for the realization of the Cognitive Perceptual Internet (CPI), which is enabled by the convergence of wired and wireless communications, traditional sensor networks, mobile crowd-sensing, and machine learning techniques. The CPI concept stems from the fact that mobile devices, such as smartphones and wearables, are becoming an outstanding mean for zero-effort world-sensing and digitalization thanks to their pervasive diffusion and the increasing number of embedded sensors. Data collected by such devices provide unprecedented insights into the physical world that can be inferred through cognitive processes, thus originating a digital sixth sense. In this paper, we describe how the Internet can behave like a sensing brain, thus evolving into the Internet of Senses, with network-based cognitive perception and action capabilities built upon mobile crowd-sensing mechanisms. The new concept of hyper-map is envisioned as an efficient geo-referenced repository of knowledge about the physical world. Such knowledge is acquired and augmented through heterogeneous sensors, multi-user cooperation and distributed learning mechanisms. Furthermore, we indicate the possibility to accommodate proactive sensors, in addition to common reactive sensors such as cameras, antennas, thermometers and inertial measurement units, by exploiting massive antenna arrays at millimeter-waves to enhance mobile terminals perception capabilities as well as the range of new applications. Finally, we distillate some insights about the challenges arising in the realization of the CPI, corroborated by preliminary results, and we depict a futuristic scenario where the proposed Internet of Senses becomes true
Flexible Virtual Reality System for Neurorehabilitation and Quality of Life Improvement
As life expectancy is mostly increasing, the incidence of many neurological
disorders is also constantly growing. For improving the physical functions
affected by a neurological disorder, rehabilitation procedures are mandatory,
and they must be performed regularly. Unfortunately, neurorehabilitation
procedures have disadvantages in terms of costs, accessibility and a lack of
therapists. This paper presents Immersive Neurorehabilitation Exercises Using
Virtual Reality (INREX-VR), our innovative immersive neurorehabilitation system
using virtual reality. The system is based on a thorough research methodology
and is able to capture real-time user movements and evaluate joint mobility for
both upper and lower limbs, record training sessions and save electromyography
data. The use of the first-person perspective increases immersion, and the
joint range of motion is calculated with the help of both the HTC Vive system
and inverse kinematics principles applied on skeleton rigs. Tutorial exercises
are demonstrated by a virtual therapist, as they were recorded with real-life
physicians, and sessions can be monitored and configured through tele-medicine.
Complex movements are practiced in gamified settings, encouraging
self-improvement and competition. Finally, we proposed a training plan and
preliminary tests which show promising results in terms of accuracy and user
feedback. As future developments, we plan to improve the system's accuracy and
investigate a wireless alternative based on neural networks.Comment: 47 pages, 20 figures, 17 tables (including annexes), part of the MDPI
Sesnsors "Special Issue Smart Sensors and Measurements Methods for Quality of
Life and Ambient Assisted Living
Deep Learning in Mobile and Wireless Networking: A Survey
The rapid uptake of mobile devices and the rising popularity of mobile
applications and services pose unprecedented demands on mobile and wireless
networking infrastructure. Upcoming 5G systems are evolving to support
exploding mobile traffic volumes, agile management of network resource to
maximize user experience, and extraction of fine-grained real-time analytics.
Fulfilling these tasks is challenging, as mobile environments are increasingly
complex, heterogeneous, and evolving. One potential solution is to resort to
advanced machine learning techniques to help managing the rise in data volumes
and algorithm-driven applications. The recent success of deep learning
underpins new and powerful tools that tackle problems in this space.
In this paper we bridge the gap between deep learning and mobile and wireless
networking research, by presenting a comprehensive survey of the crossovers
between the two areas. We first briefly introduce essential background and
state-of-the-art in deep learning techniques with potential applications to
networking. We then discuss several techniques and platforms that facilitate
the efficient deployment of deep learning onto mobile systems. Subsequently, we
provide an encyclopedic review of mobile and wireless networking research based
on deep learning, which we categorize by different domains. Drawing from our
experience, we discuss how to tailor deep learning to mobile environments. We
complete this survey by pinpointing current challenges and open future
directions for research
Game Theory for Multi-Access Edge Computing:Survey, Use Cases, and Future Trends
Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios
Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration
Future AI applications require performance, reliability and privacy that the
existing, cloud-dependant system architectures cannot provide. In this article,
we study orchestration in the device-edge-cloud continuum, and focus on AI for
edge, that is, the AI methods used in resource orchestration. We claim that to
support the constantly growing requirements of intelligent applications in the
device-edge-cloud computing continuum, resource orchestration needs to embrace
edge AI and emphasize local autonomy and intelligence. To justify the claim, we
provide a general definition for continuum orchestration, and look at how
current and emerging orchestration paradigms are suitable for the computing
continuum. We describe certain major emerging research themes that may affect
future orchestration, and provide an early vision of an orchestration paradigm
that embraces those research themes. Finally, we survey current key edge AI
methods and look at how they may contribute into fulfilling the vision of
future continuum orchestration.Comment: 50 pages, 8 figures (Revised content in all sections, added figures
and new section
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