Transitions between "realities" play an important role in designing XR experiences, as they significantly influence user experience.However, integrating these transitions into XR applications poses a significant challenge on multiple levels, including both technical and design aspects.Although this challenge has been tackled over the past decade, existing efforts seem to be fragmented, often examining different issues in isolation.This paper aims to address this issue by conducting a systematic literature review of research related to transitions between realities using XR technology.The study seeks to provide an overview of these transitions, enhancing our understanding of the relevant elements and structures involved in the process.The review covers 38 papers from the Scopus and ACM DL databases.To provide the current state of research, we classify the literature into three main themes: research topics, application domains, and transition entities.Our findings reveal three research gaps in this area: 1) limited exploration of XR transitions across diverse domains, 2) contradictions in the transition metaphor, and 3) a lack of comprehensive understanding of XR transitions across multiple scales.In conclusion, we outline future research opportunities aimed at advancing knowledge in the field.

Emerging Extended Reality (XR) applications bring new opportunities for digital healthcare systems, i.e., eHealth. XR-assisted surgery is one of the most outstanding examples of future technology that has a high social impact on the healthcare and medical educational system. The current work presents the intelligent design for remote XR-assisted surgery. The study presents the Field-of-View (FoV)-based viewport model empowered with behavioral data. It applies the viewport prediction model based on the behavioral data by applying Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). In the final analysis, LSTM showed lower errors and a higher coefficient of determination, but ANN performed much faster. Finally, the study defines the dynamic system’s states for adaptive and fast video delivery concerning Quality of Experience (QoE). The presented approach aims to mitigate the delay to ensure smooth playback and display high-quality images. • Explores advanced techniques for mitigating video traffic. • Presents a video delivery model for an advanced dynamic tiled-based video transmission. • Outlines an open-source datasets applicable to XR-related research. • Presents the results on behavioral prediction by applying ANN and LSTM. • Emphasizes the importance of behavioral study and the state of system dynamics in XR.

This research investigates an affordable, energy-efficient direction-of-arrival (DOA)-based localization solution for digital enhanced cordless telecommunications (DECTs) 2020 new radio (NR), a new standard lacking a native positioning feature. This standard enables massive Internet of Things (IoT) networks, a vast 5G network interconnecting an unparalleled number of low-cost and battery-operated smart sensors. However, integrating DOA localization into such networks is challenging due to cost constraints and power limitations. We propose a potentially cost-effective solution using a single radio-frequency (RF) chain for uniform L-shaped antenna arrays. Each antenna takes turns sampling the orthogonal frequency division multiplexing (OFDM) signal via an RF switch, enabled by time-dividing the OFDM signal into sample and switch slots. Further, we introduce a novel DOA method optimized for single Line-of-Sight (LOS) OFDM signals and array sequential sampling. This method leverages the dual shift-invariant properties of L-shaped antenna arrays and the array frequency response to estimate the azimuth and elevation angles. Experiments in an indoor environment reveal that at a signal-to-noise ratio (SNR) of 15 dB, over 50% of data achieve subdegree angular accuracy, increasing to 75% at 20 dB. Thus, over 50% of position estimations fall below the submeter error level at 15 dB SNR, rising to nearly 75% at 25 dB SNR. Our findings also indicate that halving the slot rate by proportionately reducing active subcarriers does not compromise accuracy. Experiments on the nRF52480 system-on-chip show the new DOA method is both fast and energy-efficient, taking only 0.76–2.26 ms and consuming 5.08–15.1 nWh.

With a growing number of smart interconnected devices and services, managing and controlling network traffic is getting more complicated. Among the network traffic, the Darknet-related one is particularly interesting, as it is often used for anonymous and illicit activities that pose cyber security threats. Therefore, designing and developing methods for detecting and categorizing Darknet traffic is essential. Applying Deep Learning (DL) is one of the most suitable options in this case. The main reasons are the ability to process a large amount of data and detect the hidden patterns and relationships in these data. This work proposes a DL architecture based on UNet++, which can detect and categorize anonymous traffic. The core idea of this model is semantic segmentation, which can identify meaningful segments that share some common patterns in given data. Hereby, semantic segmentation is postulated as a possible way to investigate Darknet traffic to find some common and related features instead of widely used Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). According to the results on comparison with other Machine Learning (ML) and DL models, the UNet++ model outperforms the methods with a higher accuracy of 98.19% and 87.27% for Darknet detection and traffic categorization. Our work shows the potential of using UNet++ for network traffic analysis and Darknet traffic detection. We have also demonstrated that more advanced architecture with skip connections and trainable blocks provides more accurate results than pure U-Net, CNN, and other evaluated models.

Recent advancements in hardware and software systems have been driven by the deployment of emerging smart health and mobility applications. These developments have modernized the traditional approaches by replacing conventional computing systems with cyber-physical and intelligent systems combining the Internet of Things (IoT) with Edge Artificial Intelligence. Despite the many advantages and opportunities of these systems within various application domains, the scarcity of energy, extensive computing needs, and limited communication must be considered when orchestrating their deployment. Inducing savings in these directions is central to the Approximate Computing (AxC) paradigm, in which the accuracy of some operations is traded off with energy, latency, and/or communication reductions. Unfortunately, the dynamics of the environments in which AxC-equipped IoT systems operate have been paid little attention. We bridge this gap by surveying adaptive AxC techniques applied to three emerging application domains, namely autonomous driving, smart sensing and wearables, and positioning, paying special attention to hardware acceleration. We discuss the challenges of such applications, how adaptive AxC can aid their deployment, and which savings it can bring based on traits of the data and devices involved. Insights arising thereof may serve as inspiration to researchers, engineers, and students active within the considered domains.

Emerging communication technologies, such as 5G and beyond, have introduced diverse requirements that demand high performance and energy efficiency at all levels. Furthermore, the real-time requirements of different services vary significantly — increasing the baseband processor design complexity and demand for flexible hardware platforms. This paper identifies the key characteristics of hardware platforms for baseband processing and describes the existing processing limitations in traditional architectures. In this paper, Coarse-Grained Reconfigurable Architecture (CGRA) is examined as a prospective hardware platform and its characteristic features are highlighted as compared to traditionally employed architectures that make it a suitable candidate for incorporation as a domain-specific accelerator in baseband processing applications. We survey various CGRAs from the last two decades (2004-2023) and analyze their distinct architectural features which can serve as a reference while designing CGRAs for baseband processing applications. Moreover, we investigate the existing challenges toward developing CGRAs for baseband processing and explore their potential solutions. We also provide an overview of the emerging research directions for CGRA and how they can contribute toward the development of advanced baseband processors. Lastly, we highlight a conceptual RISC-V+CGRA framework that can serve as a potential direction toward integrating CGRA in future baseband processing systems.

The popularity of the Internet of Things and next-generation wireless networks calls for a greater distribution of small but high-performance and energy-efficient compute devices at the networks' Edge. These devices must integrate hardware acceleration to meet the latency requirements of relevant use cases. Existing work has highlighted Coarse-Grained Reconfigurable Arrays (CGRAs) as suitable compute architectures for this purpose. However, like other modern hardware design, research and design space exploration into CGRAs is hindered by long development times needed for Register Transfer Level implementation. In this paper, we propose mitigating these by extending the open-source CGRAgen tool with a Chisel-based hardware backend capable of transforming abstract Processing Element (PE) descriptions into synthesizable Verilog code. We present how CGRAgen's internal module representation is transformed to Chisel modules and demonstrate this on a selection of PE architectures from the literature. Finally, we outline future work on extending this flow to generate entire CGRAs.

Modern Edge Computing devices execute applications that must meet strict latency requirements as per traditional standardization activities. Achieving the needed performance implies a need for efficiency in all aspects, thus, flexible solutions are needed. In this Ph.D. project, we address this issue for error-tolerant applications by using Coarse-Grained Reconfigurable Arrays (CGRAs) enriched with Approximate Computing (AxC) features. To do so, we aim to develop a CGRA architecture modeling, mapping, and hardware generation flow complete with AxC hardware primitives and significance analysis.

As modern 5G systems are being deployed, researchers question whether they are sufficient for the oncoming decades of technological evolution.Growing numbers of interconnected intelligent devices put these networks under tremendous pressure, demanding their development.Paving the way for beyond 5G and 6G systems, commonly denoted by B5G herein, therefore means seeking enablers to increase efficiency from different perspectives.One novel look on this is the application of inexact computations where nine 9s reliability is not needed, for example, in non-critical mobile broadband traffic.The paradigm of Approximate Computing (AxC) focuses on such areas where constrained quality degradation results in savings that benefit the users and operators.This paper surveys the state-of-the-art publications on the intersection of AxC and B5G systems, identifying and emphasizing trends and tendencies in existing work and directions for future research.The work highlights resource allocation algorithms as particularly mesmerizing in the former, while research related to Intelligent Reflective Surfaces appears the most prominent in the latter.In both, problems are often NP-hard and, thus, only solvable using heuristics or approximations, Successive Convex Approximation and Reinforcement Learning are most frequently applied.

Despite the recent advances in semiconductor technology and energy-aware system design, the overall energy consumption of computing and communication systems is rapidly growing. On the one hand, the pervasiveness of these technologies everywhere in the form of mobile devices, cyber-physical embedded systems, sensor networks, wearables, social media and context-awareness, intelligent machines, broadband cellular networks, Cloud computing, and Internet of Things (IoT) has drastically increased the demand for computing and communications. On the other hand, the user expectations on features and battery life of online devices are increasing all the time, and it creates another incentive for finding good trade-offs between performance and energy consumption. One of the opportunities to address this growing demand is to utilize an Approximate Computing approach through software and hardware design. The APROPOS project aims at finding the balance between accuracy and energy consumption, and this short paper provides an initial overview of the corresponding roadmap, as the project is still in the initial stage.

Broad adoption of resource-constrained devices for medical use has additional limitations in terms of execution of delay-sensitive medical applications. As one of the solutions, new ways of computational offloading could be developed and integrated. The recently emerged Mobile Edge Computing (MEC) and Mobile Cloud Computing (MCC) paradigms attempt to address this problem by offloading tasks to a the resource-rich server. In the context of the availability of eHealth services for all patients, independently of the location, the implementation of MEC and MCC could help ensure a high availability of medical services. Remote medical examination, robotic surgery, and cardiac telemetry require efficient computing solutions. This work discusses three alternative computing models: local computing, MEC, and MCC. We have designed a Matlab-based tool to calculate and compare the response time and energy efficiency. We show that local computing demands 48 times more power than MEC/MCC with increasing packet workload. On the other hand, the throughput of MEC/MCC highly depends on the parameters of the communication channel. Finding an optimal trade-off between the response time and energy consumption is an important research question that could not be solved without investigating the system's bottlenecks.

Rapid technology advancement, economic growth, and industrialization have paved the way for developing a new niche of small body-worn personal devices, gathered together under a wearable-technology title. The triggers stimulated by end-users interest have introduced the first generation of mass-consumer wearables in just the past decade. Evidently, the trailblazing ones were not designed with strict energy-consumption restrictions in mind. Thus, wearable-computing-related research remained fragmented. Advanced and sophisticated batteries and communication technologies could be already procurable on devices. Additional solutions for efficient utilization of processing power are still a white spot on the wearable technology roadmap. A-WEAR EU project aims to enhance the understanding of how the superimposition of those technologies would improve wearable devices' energy efficiency, with the research area being far from saturation. We foresee enormous room for research as the Edge computing paradigm is emerging towards hand-held devices.

Technology is continually undergoing a constituent development caused by the appearance of billions new interconnected “things” and their entrenchment in our daily lives. One of the underlying versatile technologies, namely wearables, is able to capture rich contextual information produced by such devices and use it to deliver a legitimately personalized experience. The main aim of this paper is to shed light on the history of wearable devices and provide a state-of-the-art review on the wearable market. Moreover, the paper provides an extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology. Finally, the survey highlights the critical challenges and existing/future solutions.

This article aims to put forward the utilization of onshore narrowband IoT (NB-IoT) infrastructure for tracking of containers transported by marine cargo vessels while operating near the coastline. We introduce and evaluate three connectivity strategies, including direct sensor-to-onshore base station (BS) transmission as well as two relay-aided schemes using dedicated vessel-BS or unmanned-aerial-vehicle (UAV)-mounted BS as intermediate nodes. To assess and compare the proposed schemes in terms of the message loss and delay metrics as well as sensor lifetimes, we first employ stochastic geometry to characterize the connectivity process with the onshore deployment and then resort to system-level simulations. Our results indicate that the direct access option suffers from the poorest performance. The relay-based alternatives allow to dramatically improve the system operation by effectively distributing the transmission requests over time at the relay side and thus mitigating contention. Furthermore, gains enabled with UAV relaying are due to extended coverage that increases the available BS density. The considered relaying operation may help tolerate intermittent connectivity across a broad range of system parameters.

Today’s dominant design for the Internet of Things (IoT) is a Cloud-based system, where devices transfer their data to a back-end and in return receive instructions on how to act. This view is challenged when delays caused by communication with the back-end become an obstacle for IoT applications with, for example, stringent timing constraints. In contrast, Fog Computing approaches, where devices communicate and orchestrate their operations collectively and closer to the origin of data, lack adequate tools for programming secure interactions between humans and their proximate devices at the network edge. This paper fills the gap by applying Action-Oriented Programming (AcOP) model for this task. While originally the AcOP model was proposed for Cloud-based infrastructures, presently it is re-designed around the notion of coalescence and disintegration, which enable the devices to collectively and autonomously execute their operations in the Fog by serving humans in a peer-to-peer fashion. The Cloud’s role has been minimized—it is being leveraged as a development and deployment platform.

Recently, new opportunities for utilizing extremely high frequencies have become instrumental to designing the fifth-generation mobile technology. The use of highly directional antennas in millimeter-wave (mm-wave) bands poses an important question of whether 2D modeling suffices to capture the resulting system performance accurately. In this paper, we develop a novel mathematical framework for performance assessment of the emerging 3D mm-wave communication scenarios, which takes into account vertical and planar directivities at both ends of a radio link, blockage effects in three dimensions, and random heights of the communicating entities. We also formulate models having different levels of details and verify their accuracy for a wide range of system parameters. We show that capturing the randomness of both the transmitting and receiving heights as well as the vertical antenna directivities becomes crucial for accurate system characterization. The conventional planar models provide overly optimistic results that overestimate performance. For instance, the model with fixed heights that disregards the effect of vertical exposure is utterly pessimistic. The other two models, one having random heights and neglecting vertical exposure and another one characterized by fixed heights and capturing vertical exposure are less computationally expensive and can be used as feasible approximations for certain ranges of input parameters.

Today, the number of interconnected Internet of Things (IoT) devices is growing tremendously followed by an increase in the density of cellular base stations. This trend has an adverse effect on the power efficiency of communication, since each new infrastructure node requires a significant amount of energy. Numerous enablers are already in place to offload the scarce cellular spectrum, thus allowing utilization of more energy‐efficient short‐range radio technologies for user content dissemination, such as moving relay stations and network‐assisted direct connectivity. In this work, we contribute a new mathematical framework aimed at analyzing the impact of network offloading on the probabilistic characteristics related to the quality of service and thus helping relieve the energy burden on infrastructure network deployments.

Owing to a steadily increasing demand for efficient spectrum utilization as part of the fifth‐generation (5G) cellular concept, it becomes crucial to revise the existing radio spectrum management techniques and provide more flexible solutions for the corresponding challenges. A new wave of spectrum policy reforms can thus be envisaged by producing a paradigm shift from static to dynamic orchestration of shared resources. The emerging Licensed Shared Access (LSA) regulatory framework enables flexible spectrum sharing between a limited number of users that access the same frequency bands, while guaranteeing better interference mitigation. In this work, an advanced user satisfaction‐aware spectrum management strategy for dynamic LSA management in 5G networks is proposed to balance both the connected user satisfaction and the Mobile Network Operator (MNO) resource utilization. The approach is based on the MNO decision policy that combines both pricing and rejection rules in the implemented processes. Our study offers a classification built over several types of users, different corresponding attributes, and a number of MNO’s decision scenarios. Our investigations are built on Criteria‐Based Resource Management (CBRM) framework, which has been specifically designed to facilitate dynamic LSA management in 5G mobile networks. To verify the proposed model, the results (spectrum utilization, estimated Secondary User price for the future connection, and user selection methodology in case of user rejection process) are validated numerically as we yield important conclusions on the applicability of our approach, which may offer valuable guidelines for efficient radio spectrum management in highly dynamic and heterogeneous 5G environments.

Recently, new opportunities for utilizing extremely high frequencies have become instrumental in developing fifth-generation (5G) mobile technology. The use of highly directional antennas in millimeter-wave (mmWave) bands poses an important question of whether two-dimensional modeling suffices to capture the resulting system performance. Accounting for the effects of human body blockage by mmWave transmissions, in this work we compare the performance of the conventional two-dimensional and the proposed three- dimensional modeling. With our stochastic geometry based approach, we consider the aggregate interference and signal-to-interference ratio (SIR) to be the main metrics of interest. Both counterpart models attempt to capture the inherent behavior of 5G mmWave systems by incorporating the effects of human body blockage and antenna directivity. We thus deliver a realistic numerical assessment by comparing the three-dimensional modeling with its two-dimensional projection to reveal the resulting discrepancy.

The Internet undergoes a fundamental transformation as billions of connected ”things” surround us and embed themselves into the fabric of our everyday lives. However, this is only the beginning of true convergence between the realm of humans and that of machines, which materializes with the advent of connected machines worn by humans, or wearables. The resulting shift from the Internet of Things to the Internet of Wearable Things (IoWT) brings along a truly personalized user experience by capitalizing on the rich contextual information, which wearables produce more than any other today’s technology. The abundance of personally identifiable information handled by wearables creates an unprecedented risk of its unauthorized exposure by the IoWT devices, which fuels novel privacy challenges. In this paper1, after reviewing the relevant contemporary background, we propose efficient means for the delegation of use applicable to a wide variety of constrained wearable devices, so that to guarantee privacy and integrity of their data. Our efficient solutions facilitate contexts when one would like to offer their personal device for temporary use (delegate it) to another person in a secure and reliable manner. In connection to the proposed protocol suite for the delegation of use, we also review the possible attack surfaces related to advanced wearables.

Motivated by the unprecedented penetration of mobile communications technology, this work carefully brings into perspective the challenges related to heterogeneous communications and offloaded computation operating in cases of fault-tolerant computation, computing, and caching. We specifically focus on the emerging augmented reality applications that require reliable delegation of the computing and caching functionality to proximate resource-rich devices. The corresponding mathematical model proposed in this work becomes of value to assess system-level reliability in cases where one or more nearby collaborating nodes become temporarily unavailable. Our produced analytical and simulation results corroborate the asymptotic insensitivity of the stationary reliability of the system in question (under the “fast” recovery of its elements) to the type of the “repair” time distribution, thus supporting the fault-tolerant system operation.