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面向5G通信网的D2D技术综述 总被引：2，自引：0，他引：2
在探讨D2D对通信技术未来发展的导向作用基础上,明确了影响D2D系统设计的多个因素,即D2D设备发现、资源分配、缓存技术、D2D-MIMO。从而勾画出基于D2D技术的光纤前传和软件定义网络实现数据/控制分离的扁平化5G架构,提出负责接入的下层宏/小基站蜂窝网和负责管理的上层网络云的管理机制。将D2D技术、SDN技术、边缘计算和物联网技术等关键技术引入未来移动通信网络已经成为研究领域的热点,针对与之相关的、未来大规模网络的移动性、QoS和大数据特性进行了讨论。 相似文献
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Gerges M. Salama Alshimaa H. Ismail Tarek Abed Soliman Hesham F.A. Hamed Nirmeen A. El‐Bahnasawy 《International Journal of Communication Systems》2020,33(12)
In 5G cloud computing, the most notable and considered design issues are the energy efficiency and delay. The majority of the recent studies were dedicated to optimizing the delay issue by leveraging the edge computing concept, while other studies directed its efforts towards realizing a green cloud by minimizing the energy consumption in the cloud. Active queue management‐based green cloud model (AGCM) as one of the recent green cloud models reduced the delay and energy consumption while maintaining a reliable throughput. Multiaccess edge computing (MEC) was established as a model for the edge computing concept and achieved remarkable enhancement to the delay issue. In this paper, we present a handoff scenario between the two cloud models, AGCM and MEC, to acquire the potential gain of such collaboration and investigate its impact on the cloud fundamental constraints; energy consumption, delay, and throughput. We examined our proposed model with simulation showing great enhancement for the delay, energy consumption, and throughput over either model when employed separately. 相似文献
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《Digital Communications & Networks》2018,4(2):77-86
With the rapid development of mobile internet and Internet of Things applications, the conventional centralized cloud computing is encountering severe challenges, such as high latency, low Spectral Efficiency (SE), and non-adaptive machine type of communication. Motivated to solve these challenges, a new technology is driving a trend that shifts the function of centralized cloud computing to edge devices of networks. Several edge computing technologies originating from different backgrounds to decrease latency, improve SE, and support the massive machine type of communication have been emerging. This paper comprehensively presents a tutorial on three typical edge computing technologies, namely mobile edge computing, cloudlets, and fog computing. In particular, the standardization efforts, principles, architectures, and applications of these three technologies are summarized and compared. From the viewpoint of radio access network, the differences between mobile edge computing and fog computing are highlighted, and the characteristics of fog computing-based radio access network are discussed. Finally, open issues and future research directions are identified as well. 相似文献
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文中设计并实现了一种基于云计算的移动医疗信息系统,旨在实现对医疗数据的实时采集、存储和分析。该系统可以在移动设备上部署传感器采集患者的生理数据,利用云计算平台进行数据处理和分析,并结合深度学习算法对数据进行预测和分类;监控结果可通过移动应用界面直观展示,便于医生和患者及时获取信息。实验结果表明,该系统能有效帮助医疗人员掌握患者健康状况,提高诊疗的及时性和准确性。 相似文献
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《Digital Communications & Networks》2021,7(3):317-326
The advancement of the Internet of Things (IoT) brings new opportunities for collecting real-time data and deploying machine learning models. Nonetheless, an individual IoT device may not have adequate computing resources to train and deploy an entire learning model. At the same time, transmitting continuous real-time data to a central server with high computing resource incurs enormous communication costs and raises issues in data security and privacy. Federated learning, a distributed machine learning framework, is a promising solution to train machine learning models with resource-limited devices and edge servers. Yet, the majority of existing works assume an impractically synchronous parameter update manner with homogeneous IoT nodes under stable communication connections. In this paper, we develop an asynchronous federated learning scheme to improve training efficiency for heterogeneous IoT devices under unstable communication network. Particularly, we formulate an asynchronous federated learning model and develop a lightweight node selection algorithm to carry out learning tasks effectively. The proposed algorithm iteratively selects heterogeneous IoT nodes to participate in the global learning aggregation while considering their local computing resource and communication condition. Extensive experimental results demonstrate that our proposed asynchronous federated learning scheme outperforms the state-of-the-art schemes in various settings on independent and identically distributed (i.i.d.) and non-i.i.d. data distribution. 相似文献
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In recent years, applying Internet of Things (IoT) applications has significantly increased to facilitate and improve quality of human life activities in various fields such as healthcare, education, industry, economics, etc. The energy aware cloud-edge computing paradigm has developed as a hybrid computing solution to provide IoT applications using available cloud service providers and fog nodes for the smart devices and mobile applications. Since the IoT applications are developed in the form of several IoT services with various quality of service (QoS) metrics which can deploy on the cloud-edge providers with different resource capabilities, finding an efficient placement solution as one of challenging topics to be measured for IoT applications. The service placement issue arranges IoT applications on the cloud-edge providers with various capabilities of atomic services though sufficient different QoS factors to support service level agreement (SLA) contracts. This paper presents a technical analysis on the cloud-edge service placement approaches in IoT systems. The key point of this technical analysis is to identify substantial studies in the service placement approaches which need additional consideration to progress more efficient and effective placement strategies in IoT environments. In addition, a side-by-side taxonomy is proposed to categorize the relevant studies on cloud-edge service placement approaches and algorithms. A statistical and technical analysis of reviewed existing approaches is provided, and evaluation factors and attributes are discussed. Finally, open issues and forthcoming challenges of service placement approaches are presented. 相似文献
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在介绍物联网与云计算定义的基础上,对其特有的业务形态进行了分析与总结,对LTE宽带无线技术如何更好地服务于物联网与云计算领域进行了探讨,并对现在的标准现状进行了分析。通过对物联网、云计算业务数据形态的分析,对未来由物联网带来的无线通信技术发展进行了展望。 相似文献
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文中深入分析了移动办公环境的特点和挑战,探讨了企业办公自动化机制在此背景下的转变和优化。研究从技术架构、管理模式、安全策略和用户体验等多个维度,构建了适应移动办公需求的自动化机制框架。通过案例分析和实证研究,验证了该机制在提升工作效率、促进协作沟通、增强信息安全等方面的积极作用。同时,文中也指出了在实施过程中可能面临的挑战和解决策略。该研究不仅为企业在移动办公环境下优化办公自动化系统提供了理论指导和实践参考,也为未来办公模式的发展趋势提供了新的思路。 相似文献
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机器类通信(Machine-Type Communication,MTC)在无线通信领域有着十分广阔的应用.与之前的3G/4G网络中以人类为中心的通信方式不同,随着5G时代的到来和\"物联网\"概念的引入,任何人和任何事物在任何时间和任何地点都能够获取和分享信息的愿景将慢慢实现,而机器类通信的快速发展尤为重要.因此,阐述机... 相似文献
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文中针对 5G 通信网络面临的信息安全挑战,提出了一种创新的基于人工智能的安全防护方法 AI-5GNSPM。 该方法巧妙融合深度学习、联邦学习和边缘计算技术,构建了一个智能、高效、隐私保护的安全防御体系。此外,还搭建了包含 20 个边缘节点的 5G 网络仿真环境,利用 KDD Cup 99 数据集对方法进行了全面评估。实验结果表明,AI-5GNSPM 在准确率、精确率、召回率和 F1 值等指标上均取得了优异表现,且能显著降低通信开销和处理时延。 相似文献
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随着手机图书馆成为图书馆服务的新阵地,越来越多的图书馆的传统服务在手机图书馆上体现出新的生命力。依赖于云计算技术的手机图书馆克服了手机作为服务终端所存在的服务瓶颈,成为手机图书馆的最佳搭档。本文探讨了手机云计算以及其优势,以及在云计算环境下,手机图书馆的服务整合理念和模式。 相似文献
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基于ETSI提出的MEC系统移动用户应用实例智能迁移机制,提出了一种增强的基于差异化服务的应用实例智能迁移机制。根据需要迁移的用户数量和业务数量定义了 4 种状态消息同步模式,使得运营商可以对具有不同服务性能优先级请求的用户和业务的处理具有更大的灵活性,为实现差异化服务和计费提供了更多的选择。 相似文献
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截至2002年4月底,意大利的移动通信用户数已超过5200万,普及率高达88%,用户规模仅次于德国,是欧洲第二大移动通信市场。目前,意大利共有4家GSM移动运营商:TIM,OmnitelVodafone,Wind和Blu,分别占有市场份额的46.7%、34.4%、15.3%和3.6%。意大利的运营商早在1998年就开始了移动互联网业务的市场培育工作,现在,移动互联网业务收入在意大利各运营商的总业务收入中已经占到10%～15%的比例。意大利的3G牌照是通过拍卖的方式颁发,OmnitelVodafone,TIM,Wind,IPSE2000,Wind,和H3G五家运营商获得了3G执照。ReportofStudyTouro 相似文献
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文中针对现有的互联网会议系统的缺陷,采用C/S与B/S的混合结构构建视频通信系统,并利用Eclipse整合式开发工具进行测试与调整,设计与实现了一个基于安卓系统的视频会议系统,并对其进行了优化调整,同时对现有版本的功能模块作出了相应更新。针对客户特定业务场景,还加入了无人机视频巡视功能,通过手机5G网络与传统会议管理系统对接,可以有效地提高客户使用体验感,并在一定程度上增加他们的主动投入程度,促进更有效的对话环境,使原本枯燥的线上讨论过程转变得生动活泼。 相似文献
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