在支持计算和通信技术的支持下，元评估有望为用户带来前所未有的服务体验。但是，元用户数量的增加对网络资源的需求量很大，尤其是用于基于图形扩展现实并需要渲染大量虚拟对象的荟萃分析服务。为了有效利用网络资源并改善体验质量（QOE），我们设计了一个注意力吸引网络资源分配方案，以实现定制的元评估服务。目的是将更多的网络资源分配给用户更感兴趣的虚拟对象。我们首先讨论与荟萃服务有关的几种关键技术，包括QOE分析，眼睛跟踪和远程渲染。然后，我们查看现有的数据集，并提出用户对象注意级别（UOAL）数据集，该数据集包含30个用户对1,000张图像中96个对象的地面意义。提供有关如何使用UOAL的教程。在UOAL的帮助下，我们提出了一种注意力感知的网络资源分配算法，该算法有两个步骤，即注意力预测和QOE最大化。特别是，我们概述了两种类型的注意力预测方法的设计，即兴趣感知和时间感知预测。通过使用预测的用户对象 - 注意值，可以最佳分配边缘设备的渲染能力等网络资源以最大化QOE。最后，我们提出了与荟萃服务有关的有前途的研究指示。

作为一个与现实世界互动的虚拟世界，元媒体封装了我们对下一代互联网的期望，同时带来了新的关键绩效指标（KPIS）。常规的超级可靠和低延迟通信（URLLC）可以满足绝大多数客观服务KPI，但是很难为用户提供个性化的荟萃服务体验。由于提高经验质量（QOE）可以被视为当务之急的KPI，因此URLLC朝向下一代URLLC（XURLLC），以支持基于图形技术的荟萃分析。通过将更多资源分配给用户更感兴趣的虚拟对象，可以实现更高的QoE。在本文中，我们研究了元服务提供商（MSP）和网络基础架构提供商（INP）之间的相互作用，以部署Metaverse Xurllc服务。提供了最佳合同设计框架。具体而言，将最大化的MSP的实用程序定义为元用户的QOE的函数，同时确保INP的激励措施。为了建模Metaverse Xurllc服务的Qoe，我们提出了一个名为Meta Immersion的新颖指标，该指标既包含了客观网络KPI和元用户的主观感觉。使用用户对象注意级别（UOAL）数据集，我们开发并验证了注意力吸引人的渲染能力分配方案以改善QOE。结果表明，与常规的URLLC相比，Xurllc平均提高了20.1％的QoE改善。当总资源有限时，QoE改进的比例较高，例如40％。

数字化和自动化方面的快速进步导致医疗保健的加速增长，从而产生了新型模型，这些模型正在创造新的渠道，以降低成本。 Metaverse是一项在数字空间中的新兴技术，在医疗保健方面具有巨大的潜力，为患者和医生带来了现实的经验。荟萃分析是多种促成技术的汇合，例如人工智能，虚拟现实，增强现实，医疗设备，机器人技术，量子计算等。通过哪些方向可以探索提供优质医疗保健治疗和服务的新方向。这些技术的合并确保了身临其境，亲密和个性化的患者护理。它还提供自适应智能解决方案，以消除医疗保健提供者和接收器之间的障碍。本文对医疗保健的荟萃分析提供了全面的综述，强调了最新技术的状态，即采用医疗保健元元的能力技术，潜在的应用程序和相关项目。还确定了用于医疗保健应用的元元改编的问题，并强调了合理的解决方案作为未来研究方向的一部分。

In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI). The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G.

In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks. Such challenges can be potentially overcome by integrating communication, computing, caching, and control (i4C) technologies. In this survey, we first give a snapshot of different aspects of the i4C, comprising background, motivation, leading technological enablers, potential applications, and use cases. Next, we describe different models of communication, computing, caching, and control (4C) to lay the foundation of the integration approach. We review current state-of-the-art research efforts related to the i4C, focusing on recent trends of both conventional and artificial intelligence (AI)-based integration approaches. We also highlight the need for intelligence in resources integration. Then, we discuss integration of sensing and communication (ISAC) and classify the integration approaches into various classes. Finally, we propose open challenges and present future research directions for beyond 5G networks, such as 6G.

Semantic communication (SemCom) and edge computing are two disruptive solutions to address emerging requirements of huge data communication, bandwidth efficiency and low latency data processing in Metaverse. However, edge computing resources are often provided by computing service providers and thus it is essential to design appealingly incentive mechanisms for the provision of limited resources. Deep learning (DL)- based auction has recently proposed as an incentive mechanism that maximizes the revenue while holding important economic properties, i.e., individual rationality and incentive compatibility. Therefore, in this work, we introduce the design of the DLbased auction for the computing resource allocation in SemComenabled Metaverse. First, we briefly introduce the fundamentals and challenges of Metaverse. Second, we present the preliminaries of SemCom and edge computing. Third, we review various incentive mechanisms for edge computing resource trading. Fourth, we present the design of the DL-based auction for edge resource allocation in SemCom-enabled Metaverse. Simulation results demonstrate that the DL-based auction improves the revenue while nearly satisfying the individual rationality and incentive compatibility constraints.

Unmanned aerial vehicle (UAV) swarms are considered as a promising technique for next-generation communication networks due to their flexibility, mobility, low cost, and the ability to collaboratively and autonomously provide services. Distributed learning (DL) enables UAV swarms to intelligently provide communication services, multi-directional remote surveillance, and target tracking. In this survey, we first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning, and present a comprehensive overview of their applications for UAV swarms, such as trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite communications. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such us reconfigurable intelligent surface (RIS), virtual reality (VR), semantic communications, and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL enabled UAV swarms. In summary, this survey provides a comprehensive survey of various DL applications for UAV swarms in extensive scenarios.

In the Metaverse, the physical space and the virtual space co-exist, and interact simultaneously. While the physical space is virtually enhanced with information, the virtual space is continuously refreshed with real-time, real-world information. To allow users to process and manipulate information seamlessly between the real and digital spaces, novel technologies must be developed. These include smart interfaces, new augmented realities, efficient storage and data management and dissemination techniques. In this paper, we first discuss some promising co-space applications. These applications offer opportunities that neither of the spaces can realize on its own. We then discuss challenges. Finally, we discuss and envision what are likely to be required from the database and system perspectives.

使用人工智能（AI）赋予无线网络中数据量的前所未有的数据量激增，为提供无处不在的数据驱动智能服务而开辟了新的视野。通过集中收集数据集和培训模型来实现传统的云彩中心学习（ML）基础的服务。然而，这种传统的训练技术包括两个挑战：（i）由于数据通信增加而导致的高通信和能源成本，（ii）通过允许不受信任的各方利用这些信息来威胁数据隐私。最近，鉴于这些限制，一种新兴的新兴技术，包括联合学习（FL），以使ML带到无线网络的边缘。通过以分布式方式培训全局模型，可以通过FL Server策划的全局模型来提取数据孤岛的好处。 FL利用分散的数据集和参与客户的计算资源，在不影响数据隐私的情况下开发广义ML模型。在本文中，我们介绍了对FL的基本面和能够实现技术的全面调查。此外，提出了一个广泛的研究，详细说明了无线网络中的流体的各种应用，并突出了他们的挑战和局限性。进一步探索了FL的疗效，其新兴的前瞻性超出了第五代（B5G）和第六代（6G）通信系统。本调查的目的是在关键的无线技术中概述了流动的技术，这些技术将作为建立对该主题的坚定了解的基础。最后，我们向未来的研究方向提供前进的道路。

联邦学习（FL）变得流行，并在训练大型机器学习（ML）模型的情况下表现出很大的潜力，而不会使所有者的原始数据曝光。在FL中，数据所有者可以根据其本地数据培训ML模型，并且仅将模型更新发送到模型更新，而不是原始数据到模型所有者进行聚合。为了提高模型准确性和培训完成时间的学习绩效，招募足够的参与者至关重要。同时，数据所有者是理性的，可能不愿意由于资源消耗而参与协作学习过程。为了解决这些问题，最近有各种作品旨在激励数据业主贡献其资源。在本文中，我们为文献中提出的经济和游戏理论方法提供了全面的审查，以设计刺激数据业主参加流程培训过程的各种计划。特别是，我们首先在激励机制设计中常用的佛罗里达州的基础和背景，经济理论。然后，我们审查博弈理论和经济方法应用于FL的激励机制的应用。最后，我们突出了一些开放的问题和未来关于FL激励机制设计的研究方向。

随着物联网（IoT）和5G/6G无线通信的进步，近年来，移动计算的范式已经显着发展，从集中式移动云计算到分布式雾计算和移动边缘计算（MEC）。 MEC将计算密集型任务推向网络的边缘，并将资源尽可能接近端点，以解决有关存储空间，资源优化，计算性能和效率方面的移动设备缺点。与云计算相比，作为分布式和更紧密的基础架构，MEC与其他新兴技术的收敛性，包括元元，6G无线通信，人工智能（AI）和区块链，也解决了网络资源分配的问题，更多的网络负载，更多的网络负载，以及延迟要求。因此，本文研究了用于满足现代应用程序严格要求的计算范例。提供了MEC在移动增强现实（MAR）中的应用程序方案。此外，这项调查提出了基于MEC的元元的动机，并将MEC的应用介绍给了元元。特别强调上述一组技术融合，例如6G具有MEC范式，通过区块链加强MEC等。

Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. A dedicated venue for collecting and summarizing the latest advances of EVA is highly desired by the community. Besides, the basic concepts of EVA (e.g., definition, architectures, etc.) are ambiguous and neglected by these surveys due to the rapid development of this domain. A thorough clarification is needed to facilitate a consensus on these concepts. To fill in these gaps, we conduct a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. The EVA system and its enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.

In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.

为了满足下一代无线通信网络的极其异构要求，研究界越来越依赖于使用机器学习解决方案进行实时决策和无线电资源管理。传统的机器学习采用完全集中的架构，其中整个培训数据在一个节点上收集，即云服务器，显着提高了通信开销，并提高了严重的隐私问题。迄今为止，最近提出了作为联合学习（FL）称为联合学习的分布式机器学习范式。在FL中，每个参与边缘设备通过使用自己的培训数据列举其本地模型。然后，通过无线信道，本地训练模型的权重或参数被发送到中央ps，聚合它们并更新全局模型。一方面，FL对优化无线通信网络的资源起着重要作用，另一方面，无线通信对于FL至关重要。因此，FL和无线通信之间存在“双向”关系。虽然FL是一个新兴的概念，但许多出版物已经在FL的领域发表了发布及其对下一代无线网络的应用。尽管如此，我们注意到没有任何作品突出了FL和无线通信之间的双向关系。因此，本调查纸的目的是通过提供关于FL和无线通信之间的相互依存性的及时和全面的讨论来弥合文学中的这种差距。

随着数据生成越来越多地在没有连接连接的设备上进行，因此与机器学习（ML）相关的流量将在无线网络中无处不在。许多研究表明，传统的无线协议高效或不可持续以支持ML，这创造了对新的无线通信方法的需求。在这项调查中，我们对最先进的无线方法进行了详尽的审查，这些方法是专门设计用于支持分布式数据集的ML服务的。当前，文献中有两个明确的主题，模拟的无线计算和针对ML优化的数字无线电资源管理。这项调查对这些方法进行了全面的介绍，回顾了最重要的作品，突出了开放问题并讨论了应用程序方案。

The Metaverse can be considered the extension of the present-day web, which integrates the physical and virtual worlds, delivering hyper-realistic user experiences. The inception of the Metaverse brings forth many ecosystem services such as content creation, social entertainment, in-world value transfer, intelligent traffic, healthcare. These services are compute-intensive and require computation offloading onto a Metaverse edge computing server (MECS). Existing Metaverse edge computing approaches do not efficiently and effectively handle resource allocation to ensure a fluid, seamless and hyper-realistic Metaverse experience required for Metaverse ecosystem services. Therefore, we introduce a new Metaverse-compatible, Unified, User and Task (UUT) centered artificial intelligence (AI)- based mobile edge computing (MEC) paradigm, which serves as a concept upon which future AI control algorithms could be built to develop a more user and task-focused MEC.

The Metaverse is deemed the next evolution of the Internet and has received much attention recently. Metaverse applications via mobile augmented reality (MAR) require rapid and accurate object detection to mix digital data with the real world. As mobile devices evolve, they become more potent in computing. Hence, their computational resources can be leveraged to train machine learning models. In light of the increasing concerns of user privacy and data security, federated learning (FL) has become a promising distributed learning framework for privacy-preserving analytics. In this article, FL and MAR are brought together in the Metaverse. We discuss the necessity and rationality of the combination of FL and MAR. The prospective technologies that power FL and MAR in the Metaverse are also identified. In addition, existing challenges that prevent the fulfilment of FL and MAR in the Metaverse and several application scenarios are presented. Finally, two case studies of Metaverse FL-MAR systems are demonstrated.

MetaVerse，巨大的虚拟物理网络空间，为艺术家带来了前所未有的机会，将我们的身体环境的每个角落与数字创造力混合。本文对计算艺术进行了全面的调查，其中七个关键主题与成权相关，描述了混合虚拟物理现实中的新颖艺术品。主题首先涵盖了MetaVerse的建筑元素，例如虚拟场景和字符，听觉，文本元素。接下来，已经反映了诸如沉浸式艺术，机器人艺术和其他用户以其他用户的方法提供了沉浸式艺术，机器人艺术和其他用户中心的若干非凡类型的新颖创作。最后，我们提出了几项研究议程：民主化的计算艺术，数字隐私和搬迁艺术家的安全性，为数字艺术品，技术挑战等等的所有权认可。该调查还担任艺术家和搬迁技术人员的介绍材料，以开始在超现实主义网络空间领域创造。

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.