Enabling Technologies for hyper-connected, data driven, and service oriented networks of the future

未来超连接、数据驱动和面向服务的网络的支持技术

• 批准号: RGPIN-2019-05477
• 负责人: Assi, Chadi
• 金额: $ 2.84万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=687328
• 关键词: Enabling; Technologies; hyper; connected; data

Advancements in technology and communication in the recent decades have always been on a constant rise, culminating into new models such as the LTE standard for 4G networks for provisioning high data rates responding to the high bandwidth needs of modern mobile applications, and the mobile cloud computing paradigm for leveraging the computing and storage power of data centers to address the computation and energy limitations of mobile devices. However, today, with the cities needing to contain the ever increasing surge in population, and technology needing to keep up with the unparalleled advancement in cutting edge services of the smart city vision, a recent research wave have been pushing for a dramatic evolution of the existing networking infrastructure and communication models, aiming to address the newly imposed smart city service requirements. Future services have heterogeneous stringent requirements, from ultra low latency and reliability, e.g. self-driving, remote surgeries, to very high bandwidth and data rates e.g. advanced gaming, VR, AR, and often require high mobility. 5G (and beyond) technologies promise to achieve this vision, e.g. a) address the high data rates requirement by densifying the cells with MIMO, improving the radio spectrum efficiency and extending it to include higher frequencies. b) address the expansion of IoT devices and huge data consumption by densifying the network and introducing UAV communication. c) address the ultra low latency communication and the tactile internet by pushing the cloud resources close to the end users and enabling various radio access technologies and hence the concepts of fog computing and multi-access edge computing, which is part of a paradigm shift from centralized to distributed services. d) address the dynamic and uncertain nature of traffic flows by softwarizing the network and its functions with the concepts of SDN and NFV, and hence facilitating traffic mobility and migration. e) address the heterogeneity of the services by allocating virtual networks with the concept of network slicing. Nevertheless, realizing the future networks still requires addressing numerous challenges. How to guarantee ultra low latency in dense areas with limited edge resources. How to minimize infrastructure costs when realizing the new communication models. How to address energy limitation for IoT and other communicating devices. How to ensure reliability of UAVs and other serving stations while maintaining low resource costs. How to efficiently manage interference resulting from the communication with the densified stations and UAVs. How to efficiently allocate spectrum and computation resources for maximizing resource utilization. How to efficiently cache data on the edge and UAVs. How to account for security threats in spite of the new architectures and communication models. Those and many other problems still need to be treated for fully realizing the future envisioned networks.

近几十年来，技术和通信的进步一直在不断发展，最终形成了新的模式，如4G网络的LTE标准，用于提供高数据速率，以满足现代移动应用的高带宽需求，以及移动云计算范式，用于利用数据中心的计算和存储能力来解决移动设备的计算和能量限制。然而，今天，随着城市需要遏制不断增长的人口激增，技术需要跟上智慧城市愿景中尖端服务的无与伦比的进步，最近的研究浪潮一直在推动现有网络基础设施和通信模式的戏剧性演变，旨在解决新施加的智慧城市服务需求。未来的服务具有异构的严格要求，从超低延迟和可靠性，例如自动驾驶，远程手术，到非常高的带宽和数据速率，例如高级游戏，VR， AR，并且通常需要高移动性。5G（及以后）技术有望实现这一愿景，例如a)通过使用MIMO致密化蜂窝来解决高数据速率要求，提高无线电频谱效率并将其扩展到更高的频率。b)通过密实化网络和引入无人机通信，解决物联网设备的扩展和庞大的数据消耗问题。C)通过推动云资源接近最终用户，实现各种无线电接入技术，从而解决超低延迟通信和触觉互联网问题，从而实现雾计算和多接入边缘计算的概念，这是从集中式服务到分布式服务的范式转变的一部分。d)利用SDN和NFV的概念，将网络及其功能软件化，以解决流量的动态性和不确定性，从而促进流量的流动和迁移。E)通过使用网络切片的概念分配虚拟网络来解决服务的异构性。然而，实现未来网络仍然需要解决许多挑战。如何在边缘资源有限的密集区域保证超低延迟。如何在实现新的通信模型时最小化基础设施成本。如何解决物联网和其他通信设备的能量限制。如何保证无人机和其他服务站的可靠性，同时保持低资源成本。如何有效管理与密集台站和无人机通信产生的干扰。如何有效地分配频谱和计算资源，使资源利用率最大化。如何在边缘和无人机上高效缓存数据。尽管有新的体系结构和通信模型，如何考虑安全威胁。为了充分实现未来设想的网络，这些问题和许多其他问题仍然需要解决。