No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
A New Era for Web AR with Mobile Edge Computing
... The primary reason is the computing inefficiency of scripts like JavaScript in a mobile web browser for dense computing tasks [116,117]. Additionally, web browsers raise concerns about a lack of standards for capturing, processing, and generating holograms [115,116]. ...
... This architecture requests the remote cloud server when the application is not deployed in the edge server or cache [86,115]. Computation-intensive tasks that are difficult to be satisfied at the edge are then assigned to the cloud server [84]. ...
... where computationally intensive tasks are executed on a remote server. Such an architecture has appeared in pre-5G AR/MR applications [115,116]. Besides the latency reduction in 5G, cloud architecture is recommended for those applications that do not require low latency [41,140]. ...
Augmented and Mixed Reality (AR/MR) technologies enhance the human perception of the world by combining virtual and real environments. With the increase of mobile devices and the advent of 5G, this technology has the potential to become part of people’s life. This article aims to evaluate the impact of 5G and beyond mobile networks in the future of AR/MR. To attend to this objective, we surveyed four digital libraries to identify articles and reviews concerning AR/MR use based on mobile networks. The results describe the state-of-the-art of mobile AR/MR applications and the benefits and challenges of the technology. Finally, after the review, we propose a roadmap concerning AR/MR hardware and software development to run applications supported by future mobile networks.
... I N the past few years,, the rapid advancement of mobile devices and wireless sensors has enabled an increasing array of applications, including Augmented Reality (AR) [1], Virtual Reality (VR) [2], adaptive video streaming [3], and gaming [4], to operate effectively within edge network environments. The Mobile Edge Computing (MEC) paradigm has emerged within this evolving landscape, specifically designed to deliver cloud-like services at the network edge. ...
... We define Q i,t to quantify the objective. 1] are weights used to balance average delay, energy consumption, and task drop rate. The minimization problem can be formulated as: ...
Mobile edge computing (MEC) represents a promising computing paradigm within Artificial Intelligence Generated Content (AIGC), offering users instant, customized, and personalized services. However, with the continued growth of the AIGC user base and the expansion of service demands, edge computing nodes are required to handle an increasing number and complexity of offloading tasks, leading to significant energy consumption issues in edge systems. This paper introduces a distributed task offloading framework (EE-A2C) that utilizes the Advantage Actor-Critic algorithm to enhance energy efficiency in edge cloud environments. This framework facilitates distributed interactions among multiple agents and edge environments with communication queues, aiming to minimize average energy consumption and latency for AIGC users. Secondly, to achieve adaptive and efficient task offloading decisions, we have developed a complex reward-sharing model based on latency and energy consumption. Finally, we have also incorporated LSTM to enhance the model’s ability to capture critical information about energy consumption, thereby promoting more robust decision-making. Compared to eight state-of-the-art energy-saving algorithms, the proposed EE-A2C framework more effectively utilizes the computing power of edge nodes, significantly reduces average energy consumption and latency, and enhances the energy efficiency of the edge cloud system.
... The development of beyond 5G (B5G) networks [1] and vehicular networks (VNs) and the ever-increasing vehicles on the road hasten the flourishing of vehicular applications, such as online gaming [2], autonomous driving [3], and augmented reality [4], which is generally computation intensive and delay-sensitive [5][6][7]. Although cloud computing provides powerful computational capabilities, remote resources could lead to high latency due to unpredictable transmission latency. ...
... In order to effectively solve the above-mentioned problem with continuous variables and meet the requirement of convergence, cooperative optimization for privacy and priority based on DDPG (COPP-DDPG) is proposed. 4. ...
Vehicular edge computing (VEC) is emerging as a prospective technology in the era of 5G and beyond to support delay-sensitive and computation-intensive vehicular applications. However, designing an efficient approach for joint computation offloading and resource allocation is challenging due to the limited resources of VEC servers, the highly dynamic vehicular networks (VNs), different priorities of vehicular applications, and the threat of privacy disclosure. In this work, we propose a cooperative optimization for privacy-preserving and priority-aware offloading and resource allocation in VEC network (VECN) based on deep reinforcement learning (DRL). Firstly, we employed a privacy-preserving framework where the certificate authority (CA) is integrated into the VEC architecture. Furthermore, we formulated the dynamic optimization problem as a Markov decision process (MDP) by constructing a weighted cost function that integrates the priority of stochastic arrival tasks, privacy-preserving of offloading, and dynamic interaction between the edge servers and intelligent connected vehicles (ICVs). To solve this problem, a cooperative optimization for privacy and priority based on deep deterministic policy gradient (COPP-DDPG) is proposed by learning the optimal actions to minimize the weighted cost function. The simulation results show that COPP-DDPG has good convergence and outperforms the other four comparison algorithms in many aspects.
... The main sustainability objective is to decrease CE. A major portion (>80%) of produced energy by utilizing brown supplies [408]. Hence, sustainability is an essential key feature measured in the structure of EC-permitted organizations. ...
To limit the impacts of climate change, the carbon dioxide CO2 emissions (CE) correlated with the energy sector must be decreased. Reduction of CE will have a positive effect on the atmosphere by avoiding the adverse impact of global warming. To attain an eco-environment, the initial energy resource needs to move from traditional fossil fuels to unpolluted renewable energy (RE). Thus, enhancing the utilization of RE actively decreases air pollution and adds secure sustainable energy allocation to ensure future energy needs. Integrating sources of RE not only drops CE but also decreases fuel consumption, leading to significant economic savings. This paper presents the transition of global energy that will have a largely positive impact on the growth and future stability of economies with cost-effective and more sustainable all over the world. Significant reductions can be accomplished by using applicable policies and technologies. In the context of current discussions about climate change and the reduction of CE, this paper critically analyses some policies, technologies, and commonly discussed solutions. Technologies like digital twin (DT), transfer learning (TL), Edge Computing (EC), Distributed Computing (DC), and some other technologies with their work for the reduction of CE are discussed thoroughly in this paper. The given techniques in this survey paper present the best optimal solutions for CE reduction.
... WBAR introduces a layer of interactivity by integrating AR experiences directly into web browsers (Qiao et al., 2019). This approach eliminates the need for specialised applications, enhancing accessibility and ease of deployment (Qiao et al., 2018). In educational settings, WBAR has been recognised for its versatility, enabling learners to access augmented content seamlessly through web browsers on various devices. ...
This study investigates the integration of augmented reality (AR) into engineering education, with a specific focus on its impact on spatial visualization abilities and the cognitive load in the context of an engineering drawing course. The research employs a structured approach, involving three experimental groups utilizing marker-based (MBAR), markerless (MLAR), and web-based AR (WBAR) applications, in comparison to a control group receiving traditional instruction. The primary objective is to rigorously assess the comparative effectiveness of these instructional approaches in enhancing spatial visualization skills and reducing the cognitive load of undergraduate engineering students. Spatial visualization, a vital cognitive skill for engineering disciplines, is evaluated using the Purdue Spatial Visualization Test: Rotations (PSVT-R) questionnaire. The cognitive load of students is assessed with the help of a questionnaire comprising items related to mental load and effort. This study not only quantifies the impact of AR applications but also highlights subtle differences among the various AR modalities. The findings suggested that the group that used marker-based AR had better improvement in their spatial ability and felt less cognitive load compared to those who utilized markerless AR and web-based AR.
... 5G networks support edge computing applications by providing high-speed, low-latency connectivity and enabling real-time data processing. This enables the development of applications like autonomous vehicles, industrial automation, and remote healthcare monitoring, and real-time analytics of sensor data from IoT devices in smart cities. [29]. 5G's potential for edge computing applications is a major driver of its transformative power, enabling faster and more efficient data processing that unlocks new possibilities for innovation and drives the development of new products, services, and business models. ...
The advent of the fifth generation of wireless technology, commonly referred to as 5G, heralds a transformative era in communication and technology interaction. With its promise of high-speed connectivity, minimal latency, and support for massive machine-type communications, 5G networks are poised to enable a wide array of applications across various domains such as the Internet of Things (IoT), smart cities, virtual and augmented reality, Industry 4.0, and edge computing. However, the deployment of 5G networks presents numerous challenges, including infrastructure requirements, limited availability of high-frequency spectrum, security vulnerabilities, high deployment costs, and interoperability issues with existing networks. This paper meticulously examines the challenges, opportunities, and applications of 5G networks. It delves into the potential of 5G networks to offer rapid connectivity, facilitate novel applications, and revolutionize industries. Moreover, the paper scrutinizes the hurdles associated with deploying and sustaining 5G networks, underscoring the imperative need for further research in this domain to ensure the seamless adoption and integration of 5G technology. As 5G networks continue to evolve, addressing these challenges will be crucial for unlocking the full potential of this transformative technology and realizing its societal and economic benefits on a global scale. Additionally, collaboration between stakeholders including governments, telecommunications companies, technology providers, and regulatory bodies will play a pivotal role in overcoming these challenges and driving the successful deployment and adoption of 5G networks worldwide.
... Mobile edge computing (MEC), as a novel approach to edge computing, diverges from traditional cloud computing by enabling cloud services to operate near users, thereby diminishing network latency. This is particularly beneficial for highly mobile devices like those on the Internet of Vehicles, mobile terminals, and wearable gadgets, aiming to minimize network delays [72]. For instance, the authors of [73] integrated MEC into the Metaverse, employing multiple dynamic edge nodes to efficiently deliver services to users, addressing the latency issues arising from user mobility. ...
Multisensory Metaverse applications ignite a revolution across industries, captivating audiences with immersive experiences. With the arrival of 6G technologies on the horizon, the stage is set for an unprecedented leap into a realm of even greater immersion and engagement. This survey explores the transformative potential of integrating multisensory Metaverse applications with 6G technology, focusing on commerce and education within the Metaverse framework. With the imminent arrival of 6G networks, characterized by ultra-high-speed connectivity, low latency, and massive connectivity, we delve into how the convergence of 6G and multisensory Metaverse marks and reshapes a paradigm shift in digital connectivity and immersion. Through the lens of commerce, we examine how immersive metaverse experiences enhance consumer engagement, virtual shopping, and personalized marketing strategies. In the realm of learning, we explore the use of Metaverse technologies for interactive education, skill training, and remote collaboration, revolutionizing traditional pedagogical approaches. By envisioning a future where multisensory Metaverse-6G experiences drive innovation across commerce and learning, we aim to create a more connected, immersive, and inclusive digital landscape. We also outline future research directions and potential innovations, envisioning a future where Metaverse-6G convergence revolutionizes communication, and collaboration. By embracing innovation and addressing challenges, we can harness the full potential of this convergence to create a more connected, immersive, and inclusive digital future.
... In [19], authors optimize QoS for context-aware AR (ConAR) using the ConAR system on edge and cloud infrastructures. 5G is ideal for low-latency tasks, while the cloud serves as backup. ...
Extended reality (XR) would become the main enabler of future immersive experience services. The challenge today is how to make this new set of immersive experience services running through a mobile network such as beyond 5G efficiently in terms of latency and Quality of experience (QoE). Academics and industry have already started to study and provide solutions towards enabling XR services in beyond 5G networks. These solutions affect both the network and the service elements by either introducing new ones or modifying existing ones. In the present paper, we proffer an extensive survey in this topic with details on the solutions proposed by academia per part of the network, i.e. radio access, core, edge etc. Moreover, we provide a thorough overview of 3rd Generation Partnership Project (3GPP) standards in Releases 17 and 18 focusing on the proposed solutions, Key Performance Indicators (KPIs), services and network elements. Finally, we summarize the lessons gained from this survey and draw the attention regarding open affairs for further research exploration for enabling XR services in beyond 5G networks and towards 6G.
... The IoT is made more powerful as the Artificial Intelligence of Things (AIoT). Traditionally, the IoT analytics were deployed on cloud services, thus the edge reacted based on instructions from the cloud [1]. With the emergence of edge computing, cloud-edge collaboration facilitates the monitoring and management of edge devices, reducing the computational and storage burden on cloud resources. ...
The soaring potential of edge computing leads to the emergence of cloud-edge collaboration. This hierarchy enables the deployment of artificial intelligence models in the cyber-physical venue. This paper presents Dual Digital Twin, the next level of digital twin, in the presence of two levels of communication availability, for battery system real-time monitoring and control in electric vehicles. To implement the dual digital twin concept, an online adaptive model reduction problem is formulated with time scale differences induced by the time sensitivity property of industrial applications and limitations of infrastructure. To minimize the model reduction error and battery system control penalty, the online adaptive battery reduced order model framework is proposed, consisting of the gated recurrent unit neural network to construct battery internal states given Internet of things sensor measurements, and incremental learning techniques to facilitate the update of the reduced-order model given data stream. Moreover, we design the physics-informed update of the neural network using the Lyapunov stability theorem to enhance the synchronization with the physical battery behavior. A Li-ion battery and single particle digital twin model with electrolyte and thermal dynamics are utilized in the simulation to justify the effectiveness of the proposed framework. Numerical results demonstrate 1.70% average reduced-order model prediction error and 43.3% accuracy improvement with the novel physics-informed online adaptive framework. The method is also robust concerning varying environmental factors and noise.
... Концепція Multi-access Edge Computing (MEC) спрямована на наближення обчислювальних процесів до кінцевих пристроїв, що зменшує затримки, пов'язані з виконанням завдань на віддалених хмарових серверах, та сприяє спрощенню оптимізації завдань з критичними затримками [36]- [39]. Використання мобільних периферійних обчислень відкриває можливості здійснення обробки на найближчих базових станціях, створюючи перспективний підхід до вирішення завдань з розподіленими обчисленнями AR за допомогою бездротових радіомереж [40]. Передбачена майбутнім розвитком Доповненої Реальності ставить високі вимоги до бездротового з'єднання, яке може бути реалізоване завдяки складним системам зв'язку, такими як мережі 5G. ...
Дана стаття присвячена дослідженню технології доповненої реальності (AR) та її впливу на різні сфери людської діяльності. У вступі визначено поняття AR як інноваційної технології, яка розширює сприйняття реального світу за допомогою накладання на нього віртуальних об'єктів. У розділі "Варіанти архітектури доповненої реальності" розглянуто різні підходи до організації AR-систем. Виділені хмарна архітектура, архітектура на основі межевих обчислень (MEC), локальна архітектура та гібридна архітектура, кожна з яких має свої особливості та переваги. Розділ "Інструменти розробника" розглядає ключові засоби, які використовують розробники для створення AR-додатків. Детально розглянуто ігрові двигуни та набори розробки програмного забезпечення (SDK), які допомагають реалізовувати AR-проекти з різними функціональними можливостями. У розділі "Сфери застосування AR" обговорено широкий спектр галузей, де застосовується технологія доповненої реальності. Розглянуто її роль у маркетингу та рекламі, освіті, медицині, а також в іграх та розвагах. Підкреслено потенціал AR для покращення навчання, медичної діагностики, рекламних кампаній та ігрового досвіду. У висновках підсумовано основні висновки статті. Підкреслено значущість технології доповненої реальності для сучасного суспільства та визначено перспективи її подальшого розвитку. Вказано на необхідність подальших досліджень та розробки для вирішення викликів та оптимізації функціональності AR-систем.
... 2) Use cases and applications: Edge computing is a rapidly evolving field, and the trend is towards moving cloud functions to the network edges [289]. Edge computing benefits the advancement and implementation of 5G and beyond networks with applications such as augmented/virtual reality [290], low-complexity IoT [291], Internet of vehicles [292], and video stream analysis [293]. Delay-sensitive augmented/virtual reality applications can be migrated to edge servers to guarantee a high quality user experience with a timely response. ...
Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.
... To further compensate for the lack of bandwidth and computing capability, Qiao et al. [50] proposed a web AR service-provisioning framework with edge servers. Moreover, the collaboration among edge servers can enhance the XR system performance. ...
Edge computing has been getting a momentum with ever-increasing data at the edge of the network. In particular, huge amounts of video data and their real-time processing requirements have been increasingly hindering the traditional cloud computing approach due to high bandwidth consumption and high latency. Edge computing in essence aims to overcome this hindrance by processing most video data making use of edge servers, such as small-scale on-premises server clusters, server-grade computing resources at mobile base stations and even mobile devices like smartphones and tablets; hence, the term edge-based video analytics. However, the actual realization of such analytics requires more than the simple, collective use of edge servers. In this paper, we survey state-of-the-art works on edge-based video analytics with respect to applications, architectures, techniques, resource management, security and privacy. We provide a comprehensive and detailed review on what works, what doesn't work and why. These findings give insights and suggestions for next generation edge-based video analytics. We also identify open issues and research directions.
... In order to address cross-platform issues, it has been envisioned that web-based solutions with natural cross-platform benefits can potentially solve this issue and has become active areas of research pertaining to system design of XR solutions (Qiao, et al., 2018). For instance, research and development of XR cross platform tools has already started where the GhostBustXR cross platform tool was created at the MIT Reality Hack that can potentially work across all XR device platforms and similar platforms are anticipated to emerge as XR technologies are advancing. ...
Extended reality (XR) technologies entails a group of innovative and immersive technologies such as augmented reality (AR), virtual reality (VR), and cinematic reality (CR), which bring physical objects into digital environments and take digital objects into physical reality. Recently, these technologies have undergone rapid development and are increasingly becoming ubiquitous in various sectors including healthcare, education, manufacturing, and military, among others. As the 5G era has united high bit rate, low latency, high-energy efficiency, increased availability, and intelligent networks, various opportunities involving XR technologies has emerged, which are also facilitated by advances in artificial intelligence. These developments are expected to strengthen toward the 6G era. On the other hand, different challenges can hinder the proliferation of these technologies toward 6G. This chapter addresses how AI-assisted XR technologies provide opportunities in the 56/6G era, while at the same time presenting the key challenges that are expected to hinder diffusion of such technologies. In addition, prospective solutions to these challenges will be discussed in order to envision the needs toward 6G.
... Content processing and rendering are more complex in AR than in VR, where AR processing procedure is shown in Figure 4. Once the raw AR content, i.e., video frames, is captured by an AR device, a location tracking step determines the device's location and position according to the captured frames. Then, a mapping step establishes a virtual coordinate of the environment based on the result of the tracker, and an object recognizing step detects the objects to process in the video frames (Qiao et al., 2018;Ren et al., 2019). Based on the identified objects, the augmented data is retrieved from the local cache or network servers and attached to the frames accordingly. ...
The sixth generation (6G) networks are expected to enable immersive communications and bridge the physical and the virtual worlds. Integrating extended reality, holography, and haptics, immersive communications will revolutionize how people work, entertain, and communicate by enabling lifelike interactions. However, the unprecedented demand for data transmission rate and the stringent requirements on latency and reliability create challenges for 6G networks to support immersive communications. In this survey article, we present the prospect of immersive communications and investigate emerging solutions to the corresponding challenges for 6G. First, we introduce use cases of immersive communications, in the fields of entertainment, education, and healthcare. Second, we present the concepts of immersive communications, including extended reality, haptic communication, and holographic communication, their basic implementation procedures, and their requirements on networks in terms of transmission rate, latency, and reliability. Third, we summarize the potential solutions to addressing the challenges from the aspects of communication, computing, and networking. Finally, we discuss future research directions and conclude this study.
... Second, even using mobile-AR devices, it is still troublesome to install a new app before design revision. With mobile edge computing and a 5G telecom network, Web-based XR 86,87 can be a promising method to bypass this step. The authors will embed the AR app in a daily social communication app, such as a WeChat Mini Program of AR. 88 It could raise the usability of AR. ...
Extended reality (XR), including augmented reality (AR), mixed reality (MR), and virtual reality (VR), can make complex information more intuitively understandable. However, it still needs to be determined which XR technology is the most suitable for reviewing performance‐based building design, during which architects check whether design strategies increase energy efficiency, indoor environment quality and so forth. It requires comprehensive design and simulation information to be vividly represented. The authors develop three XR apps (AR, MR, and VR) to interactively visualize façade fenestration geometries and indoor illuminance simulations. Then XR technologies are assessed by 120 students and young architects, from task performance and engagement level two aspects. The task performance is measured by correct rate and time consumption two indicators, while the engagement level is measured by usability and interest two indicators. Evaluation results show that compared to AR and VR, MR is the best XR technology for this aim. VR outperforms AR on three indicators except for usability. By exposing three different XR technologies' performances in aiding fenestration design, this study increases the practical value of applying XR to the building design field. Comparing three XR technologies for reviewing performance‐based building design.
... Due to diversity of hardware and operating systems, most AR applications lack cross-platform support, therefore it is necessary to investigate a lightweight, low-cost and cross-platform solution. According to the state of the art as stated in [30] [29] respectively: " Web AR: A Promising Future for Mobile Augmented Reality State of the Art, Challenges, and Insights" in 2019 and "A New Era for Web AR with Mobile Edge ...
The recent development in 5G technologies has opened a broad range of interactive and collaborative applications such as Virtual Reality / Augmented Reality (VR/AR) applications (i.e.: as online gaming and education). These immersive applications have critical requirements in terms of latency (less than 10ms), bandwidth (25Mbps to 20Gbps) and computation (3D rendering). Recently, a promising approach has been adopted that leverages Multi-access Edge Computing (MEC) technology to overcome these challenges (high latency and high-power consumption) by offloading the end user complex and heavy tasks (e.g.: rendering) at the edge. This has the benefit for reducing the application response time and keeping the power consumption of VR/AR devices very low. Furthermore, to meet the requirements of interactive and collaborative applications
(Motion-to-photon latency, high Quality of Experience (QoE), maximum number of users), Machine Learning (ML) techniques are promising techniques that will play a tremendous role for enabling a cognitive network and intelligent network orchestration system. Nevertheless, a test platform is seen as an essential means for validating and optimizing various parameters relating to resource-intensive applications. Therefore, there is a need to develop a test bed for highly interactive real-time mobile services that will allow researchers to test, evaluate and analyze their solutions. The aim of this research is to design and develop a testbed for highly interactive mobile services, specifically for VR/AR applications. The testbed consists of end user devices and edge clouds. It will provide functionalities such as edge server for Realtime services (i.e. rendering, encoding, streaming etc.).
... 2) Mobile Edge Cloud-Metaverse Architecture: Mobile edge computing (MEC), as an emerging paradigm for edge computing, has differed from traditional cloud computing in that cloud services can run as close to users as possible to reduce network latency, which is applied in some highly mobile devices, such as the Internet of vehicles, mobile termi- Fig. 5 Overview of the self-balancing federated learning-based Metaverse framework nals, wearable devices, etc., to reduce network delay [61] . For instance, Zhang et al. [53] introduced the MEC into the Metaverse, combining multiple dynamic edge nodes to help deliver services to the same user effectively solves the latency problem caused by user mobility. ...
Sixth generation (6G) enabled edge intelligence opens up a new era of Internet of everything and makes it possible to interconnect people-devices-cloud anytime, anywhere. More and more next-generation wireless network smart service applications are changing our way of life and improving our quality of life. As the hottest new form of next-generation Internet applications, Metaverse is striving to connect billions of users and create a shared world where virtual and reality merge. However, limited by resources, computing power, and sensory devices, Metaverse is still far from realizing its full vision of immersion, materialization, and interoperability. To this end, this survey aims to realize this vision through the organic integration of 6G-enabled edge artificial intelligence (AI) and Metaverse. Specifically, we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in Metaverse. Then we summarize technical challenges that these architectures face in Metaverse and the existing solutions. Furthermore, we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital data. Finally, we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.
... Mobile edge computing (MEC), as an emerging paradigm for edge computing, has differed from traditional cloud computing in that cloud services can run as close to users as possible to reduce network latency [69] . MEC is applied in some highly mobile devices, such as the Internet of Vehicles, mobile terminals, wearable devices, etc., to reduce network delay has become a new hot direction [70] . ...
6G-enabled edge intelligence opens up a new era of Internet of Everything and makes it possible to interconnect people-devices-cloud anytime, anywhere. More and more next-generation wireless network smart service applications are changing our way of life and improving our quality of life. As the hottest new form of next-generation Internet applications, Metaverse is striving to connect billions of users and create a shared world where virtual and reality merge. However, limited by resources, computing power, and sensory devices, Metaverse is still far from realizing its full vision of immersion, materialization, and interoperability. To this end, this survey aims to realize this vision through the organic integration of 6G-enabled edge AI and Metaverse. Specifically, we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in Metaverse. Then we summarize technical challenges that these architectures face in Metaverse and the existing solutions. Furthermore, we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital data. Finally, we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.
... A/VR applications (Qiao et al., 2018) are delay-sensitive due to the feature of real-time interactions with users. Highly complex operations based on big data, e.g., processing images and videos, further pose challenges on the delay guarantee of A/VR. ...
Computation offloading is one of the key technologies in Mobile Edge Computing (MEC), which makes up for the deficiencies of mobile devices in terms of storage resource, computing capacity, and energy efficiency. On one hand, computation offloading of task requests not only relieves the communication pressure on the core networks but also reduces the delay caused by long-distance data transmission. On the other hand, emerging applications in 5/6G also rely on the computation offloading technology for efficient service provisioning to users. At present, the industry and academia have conducted a lot of researches on the computation offloading methods in MEC networks with a diversity of meaningful techniques and approaches. In this paper, we present a comprehensive survey of the computation offloading in MEC networks including applications, offloading objectives, and offloading approaches. Particularly, we discuss key issues on various offloading objectives, including delay minimization, energy consumption minimization, revenue maximization, and system utility maximization. The approaches to achieve these objectives mainly include mathematical solver, heuristic algorithms, Lyapunov optimization, game theory, and Markov Decision Process (MDP) and Reinforcement Learning (RL). We compare the approaches by characterizing their pros and cons as well as targeting applications.Finally, from the four aspects of subtasks dependency and online task requests, server selection, real-time environment perception, and security, we analyze the current challenges and future directions of computation offloading in MEC networks.
... The latency problem occurs when retrieving IoT data into the MR APP. Researchers tried to use Edge Computing (Qiao et al., 2018) (Elawady & Sarhan, 2020) and Fog Computing (Jesudian, 2018) to solve it. Meanwhile, cloud computing techniques can help us render and visualize design solutions in real-time. ...
Performance-based architectural design pursues building performance objectives like energy efficiency to guide design decisions. Nevertheless, these informed decisions are usually made according to performance simulation software results, which are quite effort-consuming and tedious. Meanwhile, this low feedback efficiency blocks architects’ mind flows from creating new design alternatives. Therefore, the current authors develop a digital platform by coupling Internet of Things (IoT) and Mixed Reality (MR). It streams and visualizes real-time sensory data of indoor illuminance into an MR glass HoloLens. It enables students to intuitively observe spatio-temporal illuminance fluctuation when manually modifying the physical shading device model. Therefore, they can quickly learn how design decisions influence indoor illuminances. At last, in the validation experiment with first and second year students in an architecture school, we compare this platform with two other teaching methods and platforms. Experimental results identify that the IoT-MR platform can effectively attract students and increase their learning effects.
... are now shifting from cloud datacenters to smartphones, making a case for low user-perceived delay and data privacy preservation with the support of on-device DL [52]. For example, it is reported that the DL-embedded apps on Google Play market have increased by 60% from Feb. 2020 to Apr. 2021, and those apps contribute to billions of downloads and user reviews [22,72]. ...
Deploying deep learning (DL) on mobile devices has been a notable trend in recent years. To support fast inference of on-device DL, DL libraries play a critical role as algorithms and hardware do. Unfortunately, no prior work ever dives deep into the ecosystem of modern DL libs and provides quantitative results on their performance. In this paper, we first build a comprehensive benchmark that includes 6 representative DL libs and 15 diversified DL models. We then perform extensive experiments on 10 mobile devices, which help reveal a complete landscape of the current mobile DL libs ecosystem. For example, we find that the best-performing DL lib is severely fragmented across different models and hardware, and the gap between those DL libs can be rather huge. In fact, the impacts of DL libs can overwhelm the optimizations from algorithms or hardware, e.g., model quantization and GPU/DSP-based heterogeneous computing. Finally, atop the observations, we summarize practical implications to different roles in the DL lib ecosystem.
... Web AR is mostly targeted hand held devices such as mobile phones and tablets and has its own limitations compared with device-based and app-based AR such as limited computation, visualization capacity, lack of web AR standardization between different web browsers and limited hardware capabilities of mobile phones [112,113]. Web AR applications developed can be classified into two categories which are location-based and non-location/vision-based. Location-based AR is usually designed for large-scale outdoor applications and non-location/vision based is usually for small-scale indoor application such as tabletop applications. Location-based AR uses GNSS to track the location of the AR model based on Point of Interest (POI) which can be presented in a standardized method [114]. ...
Subsurface utilities are important assets that need to be perceived during any construction activities. Positioning and visualizing the subsurface utilities before the construction work starts has significant benefits for the effective management of construction projects. Augmented Reality (AR) is a promising technology for the visualization of subsurface utilities. The aim of this paper is to provide a comprehensive review of the state-of-the-art in AR visualization of subsurface utilities, including existing AR visualization methods, categorization of the methods and their drawbacks, comprehensive discussion on the challenges, research gaps and potential solutions. The paper begins with an introduction of current practice of locating subsurface utilities and an overview of different reality technologies including AR. We propose a taxonomy of AR visualization methods including X-Ray view, transparent view, shadow view, topo view, image rendering and cross-section view. We provide a comparison of existing methods in terms of quality of depth perception, occlusion of real world, complexity of visualization and parallax effect followed by a discussion of the drawbacks in these methods. Poor depth perception, parallax effect caused by the user movement, poor positional accuracy in Global Navigation Satellite System (GNSS) deprived or indoor areas and unavailability of accurate location information for generating virtual models are identified as main challenges and topics of future research in effective AR visu-alization of subsurface utilities.
... Moreover, web-based AR (web AR) is an approach that overcomes the cross-platform and extensive provisioning limitations that are inherent with device-based and app-based AR applications. MEC is a patent deployment option for web AR that is envisioning to achieve 1 ms latency with 5G integration [105]. infected ME App would manipulate the MEP and MEPM of the MEC servers to allocate more inessential resources for the particular application resulting service interruption of the MEC Hosts. ...
The future of mobile and internet technologies are manifesting advancements beyond the existing scope of science. The concepts of
automated driving, augmented-reality, and machine-type-communication are quite sophisticated; and requires an elevation of the current mobile infrastructure for launching. The 5G mobile technology serves as the solution; though lacks a proximate networking
infrastructure to satisfy the service guarantees. Multi-Access Edge Computing envisage such an edge computing platform. In this
survey, we are revealing security vulnerabilities of key 5G based use cases deployed in the MEC context. Probable security flows of
each case are specified, while countermeasures are proposed for mitigating them.
... Edge computing is a recent trend in networking that shifts the burden of expensive computation from the user devices, which might have limited CPU/memory/battery resources, towards nodes with available compute capabilities deployed for this purpose by the telecom operator in the vicinity [1], as opposed to Mobile Cloud Computing (MCC), which relies on services running in remote data centers in the cloud. Being close to where the data are generated and consumed, edge computing enables application with strict latency requirements [2], [3]. To maximize the Return on Investment (ROI), it is expected that densely populated areas will be the initial target of telecom operators to deploy edge nodes, thus unlocking a huge potential for the adoption and growth of smart city applications [4]. ...
Deployment of computing infrastructures at the edge of the network will drive a revolution in integrated solutions for smart mobility in the cities of the future, thanks to the promises of reduced latency and outbound traffic. The adoption of serverless computing will help realising this vision since it simplifies management while at the same time providing the application developers with a neat and clean Function-as-a- Service (FaaS) programming model. Today FaaS relies on HTTP, but QUIC is emerging fast as a replacement because it is more robust to packet losses and it allows connection roaming: both these advantages are especially important for mobile scenarios. In this paper we report the results of a preliminary evaluation of QUIC when used instead of HTTP within a framework for decentralized dispatching of FaaS function invocations, which shows that this direction is promising and deserves to be delved further in the future.
... Cross-platform Supportable VR System: The current application-based VR solutions require installing the specific app in advance and lack cross-platform support (i.e., a VR activity of one IoT application cannot be used in other applications directly). [123] envisioned that web-based solutions with natural cross-platform advantages is attracting more and more attention and is opening up a new research field of VR system design. ...
Virtual Reality (VR) has shown great potential to revolutionize the market by providing users immersive experiences with freedom of movement. Compared to traditional video streaming, VR is with ultra high-definition and dynamically changes with users' head and eye movements, which poses significant challenges for the realization of such potential. In this paper, we provide a detailed and systematic survey of enabling technologies of virtual reality and its applications in Internet of Things (IoT). We identify major challenges of virtual reality on system design, view prediction, computation, streaming, and quality of experience evaluation. We discuss each of them by extensively surveying and reviewing related papers in the recent years. We also introduce several use cases of VR for IoT. Last, issues and future research directions are also identified and discussed.
... The Web AR concept relies on web technologies to run an AR experience in a browser application, supposedly making it compatible with every device with enough computing capabilities [30]. There are different classifications regarding the browser's role in the computational tasks [31], each one with their limitations, as presented in Table 1. The pure front-end approach includes a JavaScript engine at the users' side that performs all the algorithms and calculations. ...
Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a virtual layer on top of real-time images. The great potential of unmanned aerial vehicles (UAVs) for carrying out routine and professional tasks has encouraged their use in the creation of several services, such as package delivery or industrial maintenance. Unfortunately, drone piloting is difficult to learn and requires specific training. Since regular training is performed with virtual simulations, we decided to propose a multiplatform cloud-hosted solution based in Web AR for drone training and usability testing. This solution defines a configurable trajectory through virtual elements represented over barcode markers placed on a real environment. The main goal is to provide an inclusive and accessible training solution which could be used by anyone who wants to learn how to pilot or test research related to UAV control. For this paper, we reviewed drones, AR, and human–drone interaction (HDI) to propose an architecture and implement a prototype, which was built using a Raspberry Pi 3, a camera, and barcode markers. The validation was conducted using several test scenarios. The results show that a real-time AR experience for drone pilot training and usability testing is achievable through web technologies. Some of the advantages of this approach, compared to traditional methods, are its high availability by using the web and other ubiquitous devices; the minimization of technophobia related to crashes; and the development of cost-effective alternatives to train pilots and make the testing phase easier for drone researchers and developers through trendy technologies.
... The Web AR concept relies on web technologies to run an AR experience in a browser application, supposedly making it compatible with every device with enough computing capabilities [30]. There are different classifications regarding the browser's role in the computational tasks [31], each one with their limitations, as presented in Table 1. The pure front-end approach includes a JavaScript engine at the users' side that performs all the algorithms and calculations. ...
Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a virtual layer on top of real-time images. The great potential of unmanned aerial vehicles (UAVs) for carrying out routine and professional tasks has encouraged their use in the creation of several services, such as package delivery or industrial maintenance. Unfortunately, drone piloting is difficult to learn and requires specific training. Since regular training is performed with virtual simulations, we decided to propose a multiplatform cloud-hosted solution based in Web AR for drone training and usability testing. This solution defines a configurable trajectory through virtual elements represented over barcode markers placed on a real environment. The main goal is to provide an inclusive and accessible training solution which could be used by anyone who wants to learn how to pilot or test research related to UAV control. For this paper, we reviewed drones, AR, and human–drone interaction (HDI) to propose an architecture and implement a prototype, which was built using a Raspberry Pi 3, a camera, and barcode markers. The validation was conducted using several test scenarios. The results show that a real-time AR experience for drone pilot training and usability testing is achievable through web technologies. Some of the advantages of this approach, compared to traditional methods, are its high availability by using the web and other ubiquitous devices; the minimization of technophobia related to crashes; and the development of cost-effective alternatives to train pilots and make the testing phase easier for drone researchers and developers through trendy technologies.
... Multi-access Edge Computing (MEC) brings the computations closer to the end devices, minimizing the latency of cloud offloading and facilitating the latency-critical applications [42]- [45]. Mobile edge computing offers the processing capabilities at the nearest base stations, provides a promising approach to offload MAR computations via the radio network [46]. The envisioned future of MAR puts strict requirements on wireless connectivity, which can be realizable¨ ...
The Augmented Reality (AR) technology enhances the human perception of the world by combining the real environment with the virtual space. With the explosive growth of powerful, less expensive mobile devices, and the emergence of sophisticated communication infrastructure, Mobile Augmented Reality (MAR) applications are gaining increased popularity. MAR allows users to run AR applications on mobile devices with greater mobility and at a lower cost. The emerging 5G communication technologies act as critical enablers for future MAR applications to achieve ultra-low latency and extremely high data rates while Multi-access Edge Computing (MEC) brings enhanced computational power closer to the users to complement MAR. This paper extensively discusses the landscape of MAR through the past and its future prospects with respect to the 5G systems and complementary technology MEC. The paper especially provides an informative analysis of the network formation of current and future MAR systems in terms of cloud, edge, localized, and hybrid architectural options. The paper discusses key application areas for MAR and their future with the advent of 5G technologies. The paper also discusses the requirements and limitations of MAR technical aspects such as communication, mobility management, energy management, service offloading and migration, security, and privacy and analyzes the role of 5G technologies.
Efficient image retrieval and recognition are pivotal for optimal mobile web vision services. Traditional web-based solutions offer limited accuracy, high overhead, and struggle with vast image volumes. Transferring images for real-time cloud recognition demands stable communication, and large-scale concurrent requests strain computational and network resources. This paper introduces a distributed recognition approach, leveraging cloud-edge-device collaboration through edge computing’s low latency and high bandwidth. We present a lightweight image saliency detection model tailored for mobile web, enhancing initial image feature extraction. Additionally, we introduce an edge-based, deep learning-driven method to amplify image retrieval speed and precision. We incorporate a location and popularity-based caching system to alleviate strains on cloud resources and network bandwidth during extensive image requests. Our real-world tests validate our approach: our saliency detection model outpaces the benchmark by reducing the model size by up to 94%, making it suitable for mobile web deployment. The proposed method improves retrieval accuracy by 40% over cloud-based counterparts and cuts response latency by over 60%.
Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network, together with target values for the next generation of wireless systems, and discuss protocol, integration, and implementation issues. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.
This work, built on the Unity3D development platform, presents a way for merging feature extraction technology and Web3D technology into advertising design in order to effectively address the issues of poor efficiency and distortion in the field. Using the candidate text layout generating technique of visual salience, we first built the vector function set based on the three main colours, and then we produced the visual communication partition model of advertising design. Second, the number of feature parameters of the shape advertising design is obtained via the establishment of coding coefficient constraint features and the use of an upgraded neural network technique to extract local feature parameter information about the product. Finally, the product design model is brought to life using Web3D technology to boost advertising design's productivity and accuracy. Experiments show that not only does this method result in a high rate of correct product identification, but it also offers a fresh viewpoint on the visual communication of product advertising design by merging the two disciplines.
In the real world, we enjoy showing off our art collections through images and films to express our personalities and interact with others in the neighborhood. However, there aren’t many methods in the NFT community outside updating the profile image for us to show others how much we love our collections. Adding any NFT to your camera is quite simple with this project. This merely needs an OpenSea connection to access the associated material because of the compatibility with web3. And whether it is 3D or 2D, a superb AR experience will be created based on the NFT. The NFT can be used as a background if it’s a JPEG. However, if the 3D model is set up, you may control it by moving your body.
In this work, the Unity3D development platform and Web3D technology are integrated into the teaching method of advertising design to get rid of the issues due to lack of communication and efficacy through design of digital advertisement. Based on this, an approach for ingenious product design from nature is proposed, with an emphasis on attaining a functional interaction of aesthetic intent and geometric features and investigating the relationships amongst natural systems and designers in product design from nature (PDN). The ponderings and research findings for the methodologies associated with the proposed approach have been presented. This methodology is thought to significantly bring down the delivery time of ground-breaking design and development of products, both economically and technologically. The findings in comprehensive experiments demonstrates that interactive virtual technology can significantly enhance the efficacy and interaction of the whole system in the process of digital advertising design.
The emergence of edge computing and 5G networks has fueled the growth of mobile Web AR. Although efforts have been made to improve the edge system efficiency for Web AR applications, efficient edge-assisted mobile Web AR services remain technically challenging. This paper presents EARNet, a distributed edge system orchestration approach for mobile Web AR in 5G networks. The design of EARNet makes three novel contributions. First, EARNet manages the edge network dynamics with respect to user mobility and their Web AR service requests by employing landmarks and grid index based edge node localization mechanisms. Second, EARNet takes into account both request serving performance and offloading cost in managing workload balance and quality of service and leverages dynamic hash and max heap mechanisms for efficient Web AR service lookup and AR computations. Third, EARNet designs the service migration schemes by optimizing several performance factors, such as message efficiency, scheduling latency, request density and locality of mobile users and edge nodes, and accuracy of Web AR services after migration. Experimental evaluations are conducted using the real base station deployment data in the Melbourne Central Business District (CBD) area. The results shows the effectiveness of the EARNet edge orchestration approach compared to several baseline approaches.
This paper presents a collaborative virtual 3D object modeling system for mobile augmented reality (AR) streaming services over 5G networks. The objective of the proposed system is to provide AR streaming services with low latency and high virtual object quality by effectively leveraging mobile edge cloud (MEC) and device-to-device (D2D) communication. MEC is utilized to perform computation-intensive 3D object modeling, such as virtual object slicing and mesh simplification, with abundant computing and storage resources. D2D communication is also utilized to reduce transmission delay by sharing virtual objects of interest among adjacent users. To achieve this goal, we propose a part-segment quality selection algorithm that can control the quality of each part segment and the transmission source by considering the users network condition. In the proposed system, all elements in the AR cloud, MEC, and mobile device are technically integrated to work together seamlessly. Finally, the proposed system is fully implemented using well-known open-source frameworks, including WebGL and AR.js. The system was then examined in real wireless network environments. The experimental results demonstrate the performance of the proposed system. The proposed system outperforms conventional systems in terms of service latency and visual 3D object quality.
Internet of Things (IoT) as a backbone of future customer value enables ubiquitously available digital services. However, providing smart digital services in an IoT ecosystem that billions of devices are connected to the network, needs high processing power and high capacity as well as low latency communications. In this regard, the emergence of MultiAccess Edge Computing (MEC) technology offers cloud computing capabilities to the network edge to meet IoT-based application requirements by providing real-time, high-bandwidth, low-latency access to the network resources. In this chapter, the most important topics related to IoT integrated with MEC have been presented. After introduction, the role of MEC in providing IoT services by using real-time analysis, caching and computing mechanisms are explained. By considering the importance of the integration in service delivery and platform in the next-generation networks (e.g. 5G), the MEC API section is presented. It discusses about the interaction of devices, third-parties and service providers with MEC platform through API as a common language. Then, the mobility management in IoT ecosystem related to service delivery and QoS using MEC has been studied. Finally, after presenting a benchmark for deployed IoT use cases by famous operators, challenges and future direction have been surveyed.
Deep learning shows great promise in providing more intelligence to the cross-platform web, but insufficient infrastructure, heavy models, and intensive computation limit the use of deep learning with low-performing web inference libraries. This paper proposes DeepAdapter, an integrated cloud-edge-device framework that ties the edge, the remote cloud, with the device by cross-platform web technology for adaptive deep learning services towards lower processing latency, lower mobile energy, and higher system throughput. First, we provide a context-aware pruning algorithm that incorporates the latency, the network condition, and the device's computing capability to fit the cross-platform web's resource constraints better. Second, DeepAdapter provides a model cache update mechanism improving the model request hit rate for cross-platform web users. Third, DeepAdapter provides an online matcher for dispatching appropriate models to service requestors and employs a collaborative mechanism to ensure accuracy. Last, DeepScheduler in DeepAdapter plays a significant role in scheduling high concurrent requests among various edge centers by designing the reward prediction model that improves the DRL training. Extensive experiments show that DeepAdapter can decrease average latency by 1.33x, reduce average mobile energy by 1.4x, and improve system throughput by 2.1x with considerable accuracy.
Web-based Deep Neural Networks (DNNs) enhance the ability of object recognition and has attracted considerable attention in mobile Web AR and other services. However, neither performing the DNN inference on mobile Web browsers locally nor offloading computations to the cloud can strike a balance between accuracy and efficiency; generally, rude methods are often accompanied by unsatisfactory accuracy. Collaborative approaches seem to fill this gap by coordinating the distributed hierarchical computing resources, especially in the 5G era, but it still faces challenges in the current solutions, such as the lack of (1) full use of 5G resources for the one point DNN computation partitioning schemes; (2) fine-grained branching mechanism; (3) efficient partitioning method; and (4) multi-objective optimization. To this end, we present the fine-grained elastic computation partitioning mechanism for distributed DNN in 5G networks. First, we elaborate two collaborative scenarios. Second, we study the DNN branching mechanism at layer granularity. Next, we propose a DNN computation partitioning algorithm based on deep reinforcement learning. Finally, we develop a mobile Web AR application as a proof of concept. The experiments were conducted in an actually deployed 5G trial network, and the results show the superiority of this collaborative approach.
Virtual Reality (VR) has shown great potential to revolutionize the market by providing users immersive experiences with freedom of movement. Compared to traditional video streaming, VR is with ultra high-definition and dynamically changes with users’ head and eye movements, which poses significant challenges for the realization of such potential. In this paper, we provide a detailed and systematic survey of enabling technologies of virtual reality and its applications in Internet of Things (IoT). We identify major challenges of virtual reality on system design, view prediction, computation, streaming, and quality of experience evaluation. We discuss each of them by extensively surveying and reviewing related papers in the recent years. We also introduce several use cases of VR for IoT. Last, issues and future research directions are also identified and discussed.
Multi-user mobile Augmented Reality (AR) has been successfully used in various fields as a novel visual interaction technology. But current mainstream wearable device-based and app-based solutions are still facing cross-platform, real-time communication, and intensive computing requirements. Mobile Web technology is envisioned to be a promising supporting technology for cross-platform application of mobile AR especially in 5G networks, which provide pervasive communication and computing resources thereby forming a formidable framework for the practical application of multi-user mobile Web AR. However, the problem of how to use these new techniques properly to achieve efficient communication and computing collaboration is obviously paramount in order for multi-user mobile Web AR to be realized in 5G networks. In this article, we propose the first edge-assisted multi-user collaborative framework for mobile Web AR. Firstly, we propose a heuristic mechanism for efficient communication planning, which allows multi-user interaction synchronization to be achieved. Secondly, we introduce a motion-aware key frame selection mechanism to optimize the computational efficiency of the edge system, and simultaneously alleviate the initialization problem by collaborating with nearby mobile devices using the Device-to-Device communication technique. Experiments are conducted in a real-world 5G network, and the results demonstrate the superiority of our proposed collaborative framework.
Mobile Edge Computing is an emerging technology that provides cloud and IT services within the close proximity of mobile subscribers. Traditional telecom network operators perform traffic control flow (forwarding and filtering of packets), but in Mobile Edge Computing, cloud servers are also deployed in each base station. Therefore, network operator has a great responsibility in serving mobile subscribers. Mobile Edge Computing platform reduces network latency by enabling computation and storage capacity at the edge network. It also
enables application developers and content providers to serve context-aware services (such as collaborative computing) by using real time radio access network information. Mobile and Internet of Things devices perform computation offloading for compute intensive applications, such as image processing, mobile
gaming, to leverage the Mobile Edge Computing services. In this paper, some of the promising real time Mobile Edge Computing application scenarios are discussed. Later on, a state-of-the-art research efforts on Mobile Edge Computing domain is presented. The paper also presents taxonomy of Mobile Edge Computing,
describing key attributes. Finally, open research challenges in successful deployment of Mobile Edge Computing are identified and discussed.
This survey summarizes almost 50 years of research and development in the field of Augmented Reality (AR). From early research in the 1960's until widespread availability by the 2010's there has been steady progress towards the goal of being able to seamlessly combine real and virtual worlds. We provide an overview of the common definitions of AR, and show how AR fits into taxonomies of other related technologies. A history of important milestones in Augmented Reality is followed by sections on the key enabling technologies of tracking, display and input devices. We also review design guidelines and provide some examples of successful AR applications. Finally, we conclude with a summary of directions for future work and a review of some of the areas that are currently being researched.
Recently Augmented Reality (AR) - overlaying digital content on a real world view - has seen a sharp increase in popularity due to availability of commercial SDKs and capability of display devices such as tablets, phones, and smart glasses. Typically the AR will be developed by a proprietary SDK and requires user to download a native app. Each AR experience requires a separate app.
Mobile Edge Computing (MEC) is an emerging paradigm that provides computing, storage, and networking resources within the edge of the mobile Radio Access Network (RAN). MEC servers are deployed on generic computing platform within the RAN and allow for delay-sensitive and context-aware applications to be executed in close proximity to the end users. This approach alleviates the backhaul and core network and is crucial for enabling low-latency, high-bandwidth, and agile mobile services. This article envisages a real-time, context-aware collaboration framework that lies at the edge of the RAN, constituted of MEC servers and mobile devices, and that amalgamates the heterogeneous resources at the edge. Specifically, we introduce and study three strong use cases ranging from mobile-edge orchestration, collaborative caching and processing and multi-layer interference cancellation. We demonstrate the promising benefits of these approaches in facilitating the evolution to 5G networks. Finally, we discuss the key technical challenges and open-research issues that need to be addressed in order to make an efficient integration of MEC into 5G ecosystem.
The success of the Internet of Things and rich cloud services have helped create the need for edge computing, in which data processing occurs in part at the network edge, rather than completely in the cloud. Edge computing could address concerns such as latency, mobile devices' limited battery life, bandwidth costs, security, and privacy.
Mobile crowdsourced sensing (MCS) is a new paradigm which takes advantage of pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive mechanisms are necessary to attract more user participation. Most of existing mechanisms apply only for the offline scenario where all users' information are known a priori. On the contrary, we focus on a more realistic scenario where users arrive one by one online in a random order. Based on the online auction model, we investigate the problem that users submit their private types to the crowdsourcer when arrive, and the crowdsourcer aims at selecting a subset of users before a specified deadline for maximizing the value of services (assumed to be a non-negative monotone submodular function) provided by selected users under a budget constraint. We design two online mechanisms, OMZ and OMG, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty and constant competitiveness under the zero arrival-departure interval case and a more general case, respectively. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.
A common vision of Augmented Reality (AR) is that of a person immersed in a diverse collection of virtual information, superimposed on their view of the world around them. If such a vision is to become reality, an ecosystem for AR must be created that satisfies at least these properties: multiple sources (or channels of interactive information) must be able to be simultaneously displayed and interacted with, channels must be isolated from each other (for security and stability), channel authors must have the flexibility to design the content and interactivity of their channel, and the application must fluidly integrate with the ever-growing cloud of systems and services that define our digital lives. In this paper, we present the design and implementation of the Argon AR Web Browser and describe our vision of an AR application environment that leverages the WWW ecosystem. We also describe KARML, our extension to KML (the spatial markup language for Google Earth and Maps), that supports the functionality required for mobile AR. We combine KARML with the full range of standard web technologies to create a standards-based web browser for mobile AR. KARML lets users develop 2D and 3D content using existing web technologies and facilitates easy deployment from standard web servers. We highlight a number of projects that have used Argon and point out the ways in which our web-based architecture has made previously impractical AR concepts possible.