动态复制是一种广泛的多拷贝路由方法，用于有效地应对移动机会主义网络中的间歇性连接。根据它，节点基于禁用后者的适合将消息传送到目的地的实用程序值，将消息副本转发到遇到的节点。该方法的普及源于其灵活性，在不需要特殊定制的情况下有效地在网络中有效地运行。尽管如此，其缺点是产生大量副本的趋势，这些副本消耗有限的资源，如能量和存储。为了解决问题，我们使观察到网络节点可以根据其实用程序值分组到描绘不同递送能力的集群中。我们利用此查找来转换基本转发策略，该策略是使用越来越多的效用的节点移动数据包，并实际上通过增加传递能力的集群来转发。新策略在Synernery中使用基本动态复制算法，可完全可配置，因此它可以与几乎任何实用程序功能一起使用。我们还扩展了我们的方法，同时使用两个实用程序功能，该功能在展示社会特征的移动网络中特别有效。通过在广泛的现实生活网络中进行实验，我们经验证明我们的方法在通过不同的连接特性下减少网络中的副本数量的整体数量，而没有同时阻碍输送效率。

为移动无线网络设计有效的路由策略是具有挑战性的，因为需要无缝将路由行为调整为空间多样化和时间变化的网络条件。在这项工作中，我们使用深层增强学习（DEEPRL）来学习此类网络的可扩展和可概括的单复制路由策略。我们做出以下贡献：i）我们设计了一个奖励功能，使DeepRL代理能够明确权衡竞争的网络目标，例如最大程度地减少延迟与每个数据包的传输数量； ii）我们提出了一组新型的关系邻域，路径和上下文功能，以独立于特定的网络拓扑表征移动无线网络和模型设备移动性； iii）我们使用一种灵活的培训方法，使我们可以将所有数据包和设备的数据组合到单个离线集中式培训设置中，以训练单个DEEPRL代理。为了评估通用性和可扩展性，我们在一个移动网络方案上训练我们的DEEPRL代理，然后在其他移动方案上进行测试，从而改变了设备和变速箱范围的数量。我们的结果表明，我们学到的单拷贝路由策略在延迟方面优于所有其他策略，即使在未经培训的情况下，即使在DeepRL代理的情况下也是如此。

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。

近年来，在平衡（超级）图分配算法的设计和评估中取得了重大进展。我们调查了过去十年的实用算法的趋势，用于平衡（超级）图形分区以及未来的研究方向。我们的工作是对先前有关该主题的调查的更新。特别是，该调查还通过涵盖了超图形分区和流算法来扩展先前的调查，并额外关注并行算法。

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 paper addresses state estimation for clock synchronization in the presence of factors affecting the quality of synchronization. Examples are temperature variations and delay asymmetry. These working conditions make synchronization a challenging problem in many wireless environments, such as Wireless Sensor Networks or WiFi. Dynamic state estimation is investigated as it is essential to overcome non-stationary noises. The two-way timing message exchange synchronization protocol has been taken as a reference. No a-priori assumptions are made on the stochastic environments and no temperature measurement is executed. The algorithms are unequivocally specified offline, without the need of tuning some parameters in dependence of the working conditions. The presented approach reveals to be robust to a large set of temperature variations, different delay distributions and levels of asymmetry in the transmission path.

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。

算法配置（AC）与对参数化算法最合适的参数配置的自动搜索有关。目前，文献中提出了各种各样的交流问题变体和方法。现有评论没有考虑到AC问题的所有衍生物，也没有提供完整的分类计划。为此，我们引入分类法以分别描述配置方法的交流问题和特征。我们回顾了分类法的镜头中现有的AC文献，概述相关的配置方法的设计选择，对比方法和问题变体相互对立，并描述行业中的AC状态。最后，我们的评论为研究人员和从业人员提供了AC领域的未来研究方向。

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.

Ongoing risks from climate change have impacted the livelihood of global nomadic communities, and are likely to lead to increased migratory movements in coming years. As a result, mobility considerations are becoming increasingly important in energy systems planning, particularly to achieve energy access in developing countries. Advanced Plug and Play control strategies have been recently developed with such a decentralized framework in mind, more easily allowing for the interconnection of nomadic communities, both to each other and to the main grid. In light of the above, the design and planning strategy of a mobile multi-energy supply system for a nomadic community is investigated in this work. Motivated by the scale and dimensionality of the associated uncertainties, impacting all major design and decision variables over the 30-year planning horizon, Deep Reinforcement Learning (DRL) is implemented for the design and planning problem tackled. DRL based solutions are benchmarked against several rigid baseline design options to compare expected performance under uncertainty. The results on a case study for ger communities in Mongolia suggest that mobile nomadic energy systems can be both technically and economically feasible, particularly when considering flexibility, although the degree of spatial dispersion among households is an important limiting factor. Key economic, sustainability and resilience indicators such as Cost, Equivalent Emissions and Total Unmet Load are measured, suggesting potential improvements compared to available baselines of up to 25%, 67% and 76%, respectively. Finally, the decomposition of values of flexibility and plug and play operation is presented using a variation of real options theory, with important implications for both nomadic communities and policymakers focused on enabling their energy access.

This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for empirical data in mathematical models, implement them in software, and perform simulations reflecting experiments. This path is demonstrated with respect to four key aspects of synaptic signaling: the connectivity of brain networks, synaptic transmission, synaptic plasticity, and the heterogeneity across synapses. Each step and aspect of the modeling and simulation workflow comes with its own challenges and pitfalls, which are highlighted and addressed in detail.

We review clustering as an analysis tool and the underlying concepts from an introductory perspective. What is clustering and how can clusterings be realised programmatically? How can data be represented and prepared for a clustering task? And how can clustering results be validated? Connectivity-based versus prototype-based approaches are reflected in the context of several popular methods: single-linkage, spectral embedding, k-means, and Gaussian mixtures are discussed as well as the density-based protocols (H)DBSCAN, Jarvis-Patrick, CommonNN, and density-peaks.

蒙特卡洛树搜索（MCT）是设计游戏机器人或解决顺序决策问题的强大方法。该方法依赖于平衡探索和开发的智能树搜索。MCT以模拟的形式进行随机抽样，并存储动作的统计数据，以在每个随后的迭代中做出更有教育的选择。然而，该方法已成为组合游戏的最新技术，但是，在更复杂的游戏（例如那些具有较高的分支因素或实时系列的游戏）以及各种实用领域（例如，运输，日程安排或安全性）有效的MCT应用程序通常需要其与问题有关的修改或与其他技术集成。这种特定领域的修改和混合方法是本调查的主要重点。最后一项主要的MCT调查已于2012年发布。自发布以来出现的贡献特别感兴趣。

Monte Carlo Tree Search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarise the results from the key game and non-game domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.

Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e.g., Uber, Airbnb). Over the years, crowdsourcing has morphed from providing a platform where workers and tasks can be matched up manually into one which leverages data-driven algorithmic management approaches powered by artificial intelligence (AI) to achieve increasingly sophisticated optimization objectives. In this paper, we provide a survey presenting a unique systematic overview on how AI can empower crowdsourcing - which we refer to as AI-Empowered Crowdsourcing(AIEC). We propose a taxonomy which divides algorithmic crowdsourcing into three major areas: 1) task delegation, 2) motivating workers, and 3) quality control, focusing on the major objectives which need to be accomplished. We discuss the limitations and insights, and curate the challenges of doing research in each of these areas to highlight promising future research directions.

许多复杂网络的结构包括其拓扑顶部的边缘方向性和权重。可以无缝考虑这些属性组合的网络分析是可取的。在本文中，我们研究了两个重要的这样的网络分析技术，即中心和聚类。采用信息流基于集群的模型，该模型本身就是在计算中心的信息定理措施时构建。我们的主要捐款包括马尔可夫熵中心的广义模型，灵活地调整节点度，边缘权重和方向的重要性，具有闭合形式的渐近分析。它导致一种新颖的两级图形聚类算法。中心分析有助于推理我们对给定图形的方法的适用性，并确定探索当地社区结构的“查询”节点，从而导致群集聚类机制。熵中心计算由我们的聚类算法摊销，使其计算得高效：与使用马尔可夫熵中心为聚类的先前方法相比，我们的实验表明了多个速度的速度。我们的聚类算法自然地继承了适应边缘方向性的灵活性，以及​​边缘权重和节点度之间的不同解释和相互作用。总的来说，本文不仅具有显着的理论和概念贡献，还转化为实际相关性的文物，产生新的，有效和可扩展的中心计算和图形聚类算法，其有效通过广泛的基准测试进行了验证。

Distributed deep learning (DDL) systems strongly depend on network performance. Current electronic packet switched (EPS) network architectures and technologies suffer from variable diameter topologies, low-bisection bandwidth and over-subscription affecting completion time of communication and collective operations. We introduce a near-exascale, full-bisection bandwidth, all-to-all, single-hop, all-optical network architecture with nanosecond reconfiguration called RAMP, which supports large-scale distributed and parallel computing systems (12.8~Tbps per node for up to 65,536 nodes). For the first time, a custom RAMP-x MPI strategy and a network transcoder is proposed to run MPI collective operations across the optical circuit switched (OCS) network in a schedule-less and contention-less manner. RAMP achieves 7.6-171$\times$ speed-up in completion time across all MPI operations compared to realistic EPS and OCS counterparts. It can also deliver a 1.3-16$\times$ and 7.8-58$\times$ reduction in Megatron and DLRM training time respectively} while offering 42-53$\times$ and 3.3-12.4$\times$ improvement in energy consumption and cost respectively.

由能够连接和交换消息的越来越多的移动设备而激励，我们提出了一种旨在模拟和分析网络中节点移动性的方法。我们注意到文献中的许多现有解决方案依赖于直接在节点联系人图表上计算的拓扑测量，旨在捕获节点在有利于原型设计，设计和部署移动网络的连接和移动模式方面的重要性。但是，每个措施都具有其特异性，并且无法概括最终随时间变化的节点重要性概念。与以前的方法不同，我们的方法基于节点嵌入方法，该方法模型和推出在保留其空间和时间特征的同时在移动性和连接模式中对节点的重要性。我们专注于基于一丝小组会议的案例研究。结果表明，我们的方法提供了提取不同移动性和连接模式的丰富表示，这可能有助于移动网络中的各种应用和服务。

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.

聚类算法的全面基准是困难的两个关键因素：（i）〜这种无监督的学习方法的独特数学定义和（ii）〜某些聚类算法采用的生成模型或群集标准之间的依赖性的依赖性内部集群验证。因此，对严格基准测试的最佳做法没有达成共识，以及是否有可能在给定申请的背景之外。在这里，我们认为合成数据集必须继续在群集算法的评估中发挥重要作用，但这需要构建适当地涵盖影响聚类算法性能的各种属性集的基准。通过我们的框架，我们展示了重要的角色进化算法，以支持灵活的这种基准，允许简单的修改和扩展。我们说明了我们框架的两种可能用途：（i）〜基准数据的演变与一组手派生属性和（ii）〜生成梳理给定对算法之间的性能差异的数据集。我们的作品对设计集群基准的设计具有足够挑战广泛算法的集群基准，并进一步了解特定方法的优势和弱点。