随着人工智能(AI)的积极发展,基于深神经网络(DNN)的智能应用会改变人们的生活方式和生产效率。但是,从网络边缘生成的大量计算和数据成为主要的瓶颈,传统的基于云的计算模式无法满足实时处理任务的要求。为了解决上述问题,通过将AI模型训练和推理功能嵌入网络边缘,Edge Intelligence(EI)成为AI领域的尖端方向。此外,云,边缘和终端设备之间的协作DNN推断提供了一种有希望的方法来增强EI。然而,目前,以EI为导向的协作DNN推断仍处于早期阶段,缺乏对现有研究工作的系统分类和讨论。因此,我们已经对有关以EI为导向的协作DNN推断的最新研究进行了全面调查。在本文中,我们首先回顾了EI的背景和动机。然后,我们为EI分类了四个典型的DNN推理范例,并分析其特征和关键技术。最后,我们总结了协作DNN推断的当前挑战,讨论未来的发展趋势并提供未来的研究方向。
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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.
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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.
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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.
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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.
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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。
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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。
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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.
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智能物联网环境(iiote)由可以协作执行半自动的IOT应用的异构装置,其示例包括高度自动化的制造单元或自主交互收获机器。能量效率是这种边缘环境中的关键,因为它们通常基于由无线和电池运行设备组成的基础设施,例如电子拖拉机,无人机,自动引导车辆(AGV)S和机器人。总能源消耗从多种技术技术汲取贡献,使得能够实现边缘计算和通信,分布式学习以及分布式分区和智能合同。本文提供了本技术的最先进的概述,并说明了它们的功能和性能,特别关注资源,延迟,隐私和能源消耗之间的权衡。最后,本文提供了一种在节能IIOTE和路线图中集成这些能力技术的愿景,以解决开放的研究挑战
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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。
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受到深入学习的巨大成功通过云计算和边缘芯片的快速发展的影响,人工智能研究(AI)的研究已经转移到计算范例,即云计算和边缘计算。近年来,我们目睹了在云服务器上开发更高级的AI模型,以超越传统的深度学习模型,以造成模型创新(例如,变压器,净化家庭),训练数据爆炸和飙升的计算能力。但是,边缘计算,尤其是边缘和云协同计算,仍然在其初期阶段,因为由于资源受限的IOT场景,因此由于部署了非常有限的算法而导致其成功。在本调查中,我们对云和边缘AI进行系统审查。具体而言,我们是第一个设置云和边缘建模的协作学习机制,通过彻底的审查使能够实现这种机制的架构。我们还讨论了一些正在进行的先进EDGE AI主题的潜在和实践经验,包括预先训练模型,图形神经网络和加强学习。最后,我们讨论了这一领域的有希望的方向和挑战。
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With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computational paradigms is considered a challenge. In general, resource allocation approaches are divided into two methods, including auction-based methods(goal, increase profits for service providers-increase user satisfaction and usability) and optimization-based methods(energy, cost, network exploitation, Runtime, reduction of time delay). In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture,machine-to-machine communication(M2M),Train-to-Train(T2T) communication network, Peer-to-Peer(P2P) network. Since deep learning methods based on artificial intelligence are used as the most important methods in resource allocation problems; Therefore, in this paper, resource allocation approaches based on deep learning are also used in the mentioned computational environments such as deep reinforcement learning, Q-learning technique, reinforcement learning, online learning, and also Classical learning methods such as Bayesian learning, Cummins clustering, Markov decision process.
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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.
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In recent years, deep learning (DL) models have demonstrated remarkable achievements on non-trivial tasks such as speech recognition and natural language understanding. One of the significant contributors to its success is the proliferation of end devices that acted as a catalyst to provide data for data-hungry DL models. However, computing DL training and inference is the main challenge. Usually, central cloud servers are used for the computation, but it opens up other significant challenges, such as high latency, increased communication costs, and privacy concerns. To mitigate these drawbacks, considerable efforts have been made to push the processing of DL models to edge servers. Moreover, the confluence point of DL and edge has given rise to edge intelligence (EI). This survey paper focuses primarily on the fifth level of EI, called all in-edge level, where DL training and inference (deployment) are performed solely by edge servers. All in-edge is suitable when the end devices have low computing resources, e.g., Internet-of-Things, and other requirements such as latency and communication cost are important in mission-critical applications, e.g., health care. Firstly, this paper presents all in-edge computing architectures, including centralized, decentralized, and distributed. Secondly, this paper presents enabling technologies, such as model parallelism and split learning, which facilitate DL training and deployment at edge servers. Thirdly, model adaptation techniques based on model compression and conditional computation are described because the standard cloud-based DL deployment cannot be directly applied to all in-edge due to its limited computational resources. Fourthly, this paper discusses eleven key performance metrics to evaluate the performance of DL at all in-edge efficiently. Finally, several open research challenges in the area of all in-edge are presented.
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未来的互联网涉及几种新兴技术,例如5G和5G网络,车辆网络,无人机(UAV)网络和物联网(IOT)。此外,未来的互联网变得异质并分散了许多相关网络实体。每个实体可能需要做出本地决定,以在动态和不确定的网络环境下改善网络性能。最近使用标准学习算法,例如单药强化学习(RL)或深入强化学习(DRL),以使每个网络实体作为代理人通过与未知环境进行互动来自适应地学习最佳决策策略。但是,这种算法未能对网络实体之间的合作或竞争进行建模,而只是将其他实体视为可能导致非平稳性问题的环境的一部分。多机构增强学习(MARL)允许每个网络实体不仅观察环境,还可以观察其他实体的政策来学习其最佳政策。结果,MAL可以显着提高网络实体的学习效率,并且最近已用于解决新兴网络中的各种问题。在本文中,我们因此回顾了MAL在新兴网络中的应用。特别是,我们提供了MARL的教程,以及对MARL在下一代互联网中的应用进行全面调查。特别是,我们首先介绍单代机Agent RL和MARL。然后,我们回顾了MAL在未来互联网中解决新兴问题的许多应用程序。这些问题包括网络访问,传输电源控制,计算卸载,内容缓存,数据包路由,无人机网络的轨迹设计以及网络安全问题。
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在过去的十年中,水下事物的互联网(IOUT)在环境监测和勘探,国防应用等应用程序中取得了迅速的动力。传统的IOUT系统使用机器学习(ML)方法,这些方法满足了可靠性,效率和及时性的需求。但是,对进行的各种研究的广泛审查突出了IOUT框架中数据隐私和安全性的重要性,这是实现任务关键应用程序中预期结果的主要因素。联邦学习(FL)是一个有安全的,分散的框架,是机器学习的最新发展,它将有助于满足IOUT中常规ML方法所面临的挑战。本文概述了FL在IOUT中的各种应用,其挑战,开放问题并指示未来研究前景的方向。
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近年来,物联网设备的数量越来越快,这导致了用于管理,存储,分析和从不同物联网设备的原始数据做出决定的具有挑战性的任务,尤其是对于延时敏感的应用程序。在车辆网络(VANET)环境中,由于常见的拓扑变化,车辆的动态性质使当前的开放研究发出更具挑战性,这可能导致车辆之间断开连接。为此,已经在5G基础设施上计算了云和雾化的背景下提出了许多研究工作。另一方面,有多种研究提案旨在延长车辆之间的连接时间。已经定义了车辆社交网络(VSN)以减少车辆之间的连接时间的负担。本调查纸首先提供了关于雾,云和相关范例,如5G和SDN的必要背景信息和定义。然后,它将读者介绍给车辆社交网络,不同的指标和VSN和在线社交网络之间的主要差异。最后,本调查调查了在展示不同架构的VANET背景下的相关工作,以解决雾计算中的不同问题。此外,它提供了不同方法的分类,并在雾和云的上下文中讨论所需的指标,并将其与车辆社交网络进行比较。与VSN和雾计算领域的新研究挑战和趋势一起讨论了相关相关工程的比较。
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自从37年和64年前构思了移动通信和人工智能以来,这是一个令人兴奋的旅程。虽然这两个领域独立地演变而来的通信和计算产业,但是快速收敛的5G和深度学习开始显着改变核心通信基础设施,网络管理和垂直应用。本文首先概述了早期移动通信和人工智能的个人路线图,当AI和移动通信开始汇聚时,集中在3G到5G中审查时代。关于电信人工智能,本文进一步详细介绍了移动通信生态系统中人工智能的进展。然后,该文件总结了电信生态系统中AI的分类以及各种国际电信标准化机构指定的进化路径。本文预测了电信人工智能的前瞻性路线图。符合3GPP和ITU-R的时间表5G&6G,本文进一步探讨了3GPP和奥兰路线之后的网络智能,经验和意图驱动的网络管理和操作,网络AI信令系统,智能中办事处的BSS,智能化由BSS和OSS融合驱动的客户体验管理和政策控制,从SLA到ELA的Evolution,以及垂直智能专用网络。本文的愿景结束了AI将重塑未来B5G或6G景观,我们需要枢转我们的研发,标准化和生态系统,以充分承担前所未有的机会。
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通信技术和互联网的最新进展与人工智能(AI)启用了智能医疗保健。传统上,由于现代医疗保健网络的高性性和日益增长的数据隐私问题,AI技术需要集中式数据收集和处理,这可能在现实的医疗环境中可能是不可行的。作为一个新兴的分布式协作AI范例,通过协调多个客户(例如,医院)来执行AI培训而不共享原始数据,对智能医疗保健特别有吸引力。因此,我们对智能医疗保健的使用提供了全面的调查。首先,我们在智能医疗保健中展示了近期进程,动机和使用FL的要求。然后讨论了近期智能医疗保健的FL设计,从资源感知FL,安全和隐私感知到激励FL和个性化FL。随后,我们对关键医疗领域的FL新兴应用提供了最先进的综述,包括健康数据管理,远程健康监测,医学成像和Covid-19检测。分析了几个最近基于智能医疗保健项目,并突出了从调查中学到的关键经验教训。最后,我们讨论了智能医疗保健未来研究的有趣研究挑战和可能的指示。
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深度学习(DL)模型在许多应用领域中取得了卓越的性能,包括愿景,语言,医疗,商业广告,娱乐等。随着快速的发展,DL应用和潜在的服务硬件都表现出强大的缩放趋势,即例如,模型缩放和计算缩放,例如,最近的预先训练模型,具有数百亿次参数,具有〜TB级存储器消耗,以及提供数百个TFLOPS的最新GPU加速器。在扩大趋势,新的问题和挑战中出现了DL推理服务系统,这逐渐朝着大型深度学习服务系统(LDS)趋势。该调查旨在总结和分类大规模深度学习服务系统的新兴挑战和优化机会。通过提供新的分类法,总结计算范例,并详细说明最近的技术进步,我们希望这项调查能够在新的优化视角下阐明,并激励小说在大型深度学习系统优化中的作品。
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