WO2019179471A1 - 一种物联网环境下的雾计算体系架构 - Google Patents

一种物联网环境下的雾计算体系架构 Download PDF

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Publication number
WO2019179471A1
WO2019179471A1 PCT/CN2019/078905 CN2019078905W WO2019179471A1 WO 2019179471 A1 WO2019179471 A1 WO 2019179471A1 CN 2019078905 W CN2019078905 W CN 2019078905W WO 2019179471 A1 WO2019179471 A1 WO 2019179471A1
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cloud
distributed
mode
communication
fog computing
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PCT/CN2019/078905
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English (en)
French (fr)
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朱洪波
郭永安
蔡艳
杨龙祥
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南京邮电大学
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Priority to JP2020537011A priority Critical patent/JP6959685B2/ja
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]

Definitions

  • the invention relates to a fog computing architecture in an Internet of Things environment, which is mainly applied to a fifth generation mobile communication system (5G), belonging to the field of Internet of Things and cloud computing.
  • 5G fifth generation mobile communication system
  • the system capacity of the fifth generation wireless communication system (5G) will increase by at least 1000 times, and the energy transmission efficiency will increase by at least 10 times.
  • the cloud computing architecture must centrally process large amounts of data in distributed base stations, which requires the architecture to have two prerequisites: high bandwidth and low latency interconnect front ends.
  • existing front-end transmissions are often constrained by capacity and time delays, and their performance in terms of spectral efficiency and energy efficiency is far less than that required by the standard.
  • Fog computing extends the traditional cloud computing model to the edge of the network, where it is easy to provide a large number of devices with storage, communication, control, configuration, measurement, and management to meet transmission needs, rather than using centralized cloud storage and channel establishment to achieve demand.
  • cooperative radio signal processing can be performed not only in a cloud computing center server, but also at distributed base stations and smart devices.
  • This real-time coordinated radio signal processing and flexible coordinated radio resource management enable the fog computing architecture to accommodate the fast and scalable front-end burden of the radio environment.
  • fog computing enables user-centric goals through device-to-device (D2D), wireless relay, distributed coordination, and adaptive collaboration between large-scale centralized collaboration.
  • D2D device-to-device
  • D2D relay mode local distributed coordination mode, global cloud transmission mode and HPN mode;
  • the distributed logical communication cloud is composed of a plurality of distributed base stations, and is cooperatively processed among a plurality of distributed base stations to release the overload of the previous link and reduce the queue and transmission waiting time;
  • the distributed logical storage cloud is composed of controlled user equipments in the access fog computing architecture; the centralized control cloud is located in the IoT monitoring node, and the global centralized communication and storage cloud and the cloud computing central server are formed;
  • the distributed logical communication cloud not only integrates the pre-RF, but also integrates the functions of local distributed cooperative radio signal processing and coordinated radio resource management technology, while the distributed logical storage cloud is responsible for local storage and caching in edge devices; centralized control of the cloud Used to act as a control platform, responsible for issuing control signaling to the lower layer cloud;
  • the global centralized communication and storage cloud is similar to the traditional cloud computing central server in cloud computing, and is responsible for transmitting and interacting data with user equipment and distributed base stations, and functions as centralized storage and centralized communication;
  • the cloud computing center server selectively supplies the received data to users in the IoT application layer according to requirements
  • the terminal layer, the network access layer, the cloud computing layer and the fog computing layer; the fog computing layer is evolved by the distributed base station into a fog computing access point, the terminal layer and the smart device in the network access layer; in the terminal layer
  • the device communicates directly with the neighboring device in the D2D mode without the help of the fog computing access point evolved by the distributed base station. If the communication distance of the two potentially paired devices exceeds the D2D distance threshold, the trigger will be based on a relay mode of a third-party device to provide communication for the two devices;
  • the Internet of Things monitoring node is used to deliver overall control signaling and provides seamless coverage with high bit rate for high speed mobile devices
  • the cloud computing layer is software defined, centralized computing and cached attributes
  • All signal processing units work together in a large physical baseband unit pool to share signaling, traffic data, and channel state information for the entire fog calculation;
  • the controlled user equipment adaptively accesses the fog computing in an infinite network, and selects four transmission modes according to the moving speed, communication distance, location, quality of service requirements, processing and caching capabilities of the user equipment: D2D relay mode, Local distributed coordination mode, global cloud transmission mode and HPN mode.
  • the D2D relay mode two user equipments communicate with each other through D2D or smart device-based wireless relay technology;
  • the local distributed coordination mode means that the controlled user equipment accesses the adjacent distributed base station, and the communication is in At the end, only the transmission result is fed back;
  • the global cloud transmission mode means that all local distributed cooperative radio signal processing and cooperative radio resource management technology functions are implemented centrally in distributed base stations, with high mobile speed or in a distributed logical communication cloud.
  • the controlled user equipment in the coverage hole must access the IoT monitoring node represented by the HPN mode.
  • the present invention designs a fog computing architecture under the Internet of Things environment, and its core idea is to fully utilize local radio signal processing, combine radio resource management and distributed storage capabilities in edge devices to reduce the weight of the front end. Load to achieve higher system capacity and processing rate to meet the needs of the fifth generation of wireless communication systems (5G).
  • 5G wireless communication systems
  • fog computing extends the traditional cloud computing model to the edge of the network, and collaborative radio signal processing can be performed not only in the cloud computing center server, but also at distributed base stations and smart devices.
  • fog computing can achieve user-centric goals through device-to-device (D2D), wireless relay, distributed coordination, and large-scale centralized collaboration, ultimately adapting to the needs of mobile transport development.
  • D2D device-to-device
  • FIG. 1 is a system architecture diagram of a fog computing architecture in an Internet of Things environment according to the present invention
  • FIG. 2 is a mode selection pseudo code of a fog computing architecture in an Internet of Things environment according to the present invention.
  • the proposed F-RAN leverages the convergence of cloud computing, heterogeneous networks and fog computing.
  • clouds Four types of clouds are defined: global centralized communication and storage clouds, centralized control clouds, distributed logical communication clouds, and distributed logical storage clouds.
  • the distributed logical communication cloud not only integrates the pre-radio, but also integrates local distributed cooperative radio signal processing and cooperative radio resource management technology functions, while the distributed logical storage cloud is responsible for local storage and caching in edge devices.
  • the centralized control cloud is used to act as a control platform and is responsible for issuing control signaling to the lower layer cloud.
  • the global centralized communication and storage cloud is similar to the traditional cloud computing central server in cloud computing, and is responsible for transmitting and interacting data with user equipment and distributed base stations, and functions as centralized storage and centralized communication.
  • a proposed system model for implementing a fog computing architecture as shown in FIG. It includes a terminal layer, a network access layer, a cloud computing layer, and a fog computing layer.
  • the fog computing layer is developed by the distributed computing base station into a fog computing access point, a smart layer in the terminal layer and the network access layer.
  • adjacent controlled user equipments can communicate with each other through a D2D mode or a distributed base station based relay mode.
  • device 3 and device 1 can communicate with each other with the help of device 2, which can be considered a mobile relay. If there is some data to be sent directly between device 1 and device 2, the D2D mode is used.
  • the network access layer consists of a fog computing access point evolved by a distributed base station and an IoT monitoring checkpoint. All communication devices access the IoT monitoring node to obtain all signaling related to system information, which acts as a control platform.
  • the fog computing access point evolved by the distributed base station is used to forward and process the received data.
  • the fog computing access point interfaces with the cloud computing center server baseband unit in the cloud computing layer through the front end link, and the fog computing access point accesses the cloud computing center server baseband unit through the backhaul link, and the signal on the front end link Sent to the cloud computing server for large-scale processing.
  • IoT monitoring points and limited caching in smart devices can distribute some packet services to edge devices rather than to a centralized centralized cache in a cloud computing center server.
  • the fog computing access point evolved by the distributed base station is mainly used for processing local coordinated radio signal processing and cooperative radio resource management for the accessed device, and provides interference suppression and spectrum sharing for the device operating in the D2D transmission mode, and passes through the front end.
  • the received information is compressed and forwarded to the cloud computing center server baseband unit.
  • the fog computing access point not only integrates the pre-RF, but also integrates local distributed cooperative radio signal processing and simple coordinated radio resource management functions. By cooperating between multiple adjacent fog computing access points, the overload of the front-end link is released, and the queuing and transmission waiting time can be alleviated.
  • the fog computing access point degenerates into a conventional distributed base station.
  • the fog computing access point and the IoT monitoring point become active to carry high-capacity transmission communication traffic, and the D2D or relay mode can be further triggered to meet huge capacity requirements.
  • the above three form a fog computing layer by cooperating with each other.
  • the rest of the layered architecture of the fog computing architecture in an IoT environment is described below.
  • the device can communicate directly with neighboring devices in the D2D mode without the aid of a fog computing access point evolved by the distributed base station, where the IoT monitoring node is used to deliver the overall to the D2D paired device. Control signaling.
  • the D2D mode is particularly advantageous for meeting the need to transfer data at high rates and also to increase overall throughput.
  • the D2D mode is severely constrained by the communication distance and the ability of the fog to calculate an access point, and cannot provide a legacy device service that does not support the D2D mode. If the communication distance of two potentially paired devices exceeds the D2D distance threshold, a third-party device-based relay mode will be triggered to provide communication for the two devices.
  • IoT monitoring nodes are mainly used to deliver overall control signaling and provide seamless coverage with high bit rate for high speed mobile devices.
  • IoT monitoring nodes with large-scale multiple-input multiple-output (MIMO) are the key to ensuring backward compatibility between the fog computing architecture and existing wireless systems.
  • MIMO multiple-input multiple-output
  • the overall control channel overhead and cell-specific reference signals of the fog computing architecture are delivered by the IoT monitoring node, so fog calculations can reduce unnecessary handoffs and mitigate synchronization constraints.
  • the cooperative radio signal processing and cooperative radio resource management functions end in the fog computing access point, they have the same function of the cell base station, with distributed interference coordination similar to coordinated multi-point transmission and reception (CoMP) to suppress the layer Internal and inter-layer interference.
  • CoMP coordinated multi-point transmission and reception
  • the cloud computing layer is software-defined and features centralized computing and cached attributes. All signal processing units work together in a large physical baseband unit pool to share signaling, traffic data, and channel state information for the entire fog calculation. When the network load grows, operators only need to upgrade the baseband unit pool to accommodate the increased capacity.
  • the controlled user equipment in the non-computing architecture in the IoT environment is adaptively connected to the fog computing infinite network, and according to the moving speed, communication distance, location, quality of service requirements of the user equipment, processing And the cache capability to select four transmission modes: D2D relay mode, local distributed coordination mode, global cloud transmission mode and HPN mode.
  • the local distributed coordination mode means that the device accesses the adjacent distributed base station and the communication ends here.
  • the global cloud transmission mode means that all local distributed cooperative radio signal processing and cooperative radio resource management technology functions are implemented centrally in distributed base stations, and controlled user equipments with high mobile speed or coverage holes in distributed logical communication clouds must Access to the IoT monitoring node represented by the HPN mode.
  • the optimal transmission mode is selected by the accessed device under the supervision of the IoT monitoring node.
  • the speed of the device is estimated based on the pilot channel from the IoT monitoring node and the distance from the remaining different devices. If the device is in a high-speed mobile state or needs to provide real-time voice communication services, HPN mode is triggered with high priority.
  • the D2D mode is triggered if two devices communicating with each other have a slower relative moving speed and their distance is not greater than the threshold D1.
  • the relay mode based on the third device is triggered to This achieves better performance than other modes.
  • the two desired F-UEs move slowly, and their distance is greater than D2 but less than D3, or their distance is not greater than D2, but at least one device does not support D2D and relay mode, then the local distributed coordination mode is adopted. .
  • the global cloud transmission mode is triggered if the local distributed coordination mode does not provide the expected performance, or if the distance between the two desired F-UEs is greater than D3, or if the transmitted content is from a cloud server.

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Abstract

本发明公开了一种基于物联网环境下的雾计算体系架构，本方案充分利用本地无线电信号处理能力，合并无线电资源管理和边缘设备中的分布式存储能力，并通过合适的传输模式选择，构成雾计算体系架构，以达到减少前端负荷的效果，从而获取更高的系统容量和处理速率。具体表现为在雾计算无线接入网络中定义了四种云：全局集中通信和存储云，集中控制云，分布式逻辑通信云和分布式逻辑存储云，分别负责通讯，存储，控制与信号处理。当用户设备接入雾计算体系物联网中，根据用户设备的移动速度，通信距离，位置，服务质量(QoS)要求，处理和高速缓存能力来选择四种传输模式：D2D和中继模式，局部分布式协调模式，全局C-RAN模式和HPN模式，从而达到获取更高效率。

Description

一种物联网环境下的雾计算体系架构 技术领域
本发明涉及一种物联网环境下雾计算体系架构，主要应用于第五代移动通信系统(5G)，属于物联网与云计算领域。
背景技术
与第四代无线通信系统(4G)相比，第五代无线通信系统(5G)的系统容量将至少增长1000倍，且能量传输效率至少增加10倍。为了实现这些目标，云计算体系架构必须在分布式基站中集中处理大量的数据，这就要求这个体系架构要拥有高宽带和低等待时间的互联前端这两个先决条件。然而现有的前端传输往往是容量和时间延迟约束的，其在频谱效率和能量效率上的表现远不及标准所需。随着基于位置的社交应用变得越来越流行，前端业务数据激增，产生了大量的冗余信息；同时为了满足峰值容量的要求，运营商需要部署大量的基站以满足峰值容量的要求，然而在业务交付量不够大时造成了严重的浪费。
发明内容
为了解决现有技术中的挑战，本发明的目的是引入雾计算体系架构的概念，通过充分利用边缘设备中的处理和存储能力，以减轻前端和分布式基站的负担。
雾计算将传统的云计算模式扩展到网络边缘，在网络便于提供大量具有的存储，通信，控制，配置，测量和管理的设备以达到传输需求，而不是利用集中式云存储和建立信道来达到需求。基于雾计算，协作无线电信号处理不仅可以在云计算中心服务器中执行，而且可以在分布式基站以及智能设备处被托管。这种实时的协同无线电信号处理和灵活的协同无线资源管理使得雾计算体系架构能够适应承担无线电环境下的快速且可缩放的前端负担。此外，雾计算可以通过设备到设备(D2D)，无线中继，分布式协调和大规模集中合作之间的自适应技术来实现以用户为中心的目标。
技术方案如下：
物联网环境下的雾计算体系架构的“四种云+应用层”式布局及四种传输模式，
即全局集中通信和存储云，集中控制云，分布式逻辑通信云，分布式逻辑存储云和物联网应用层；
D2D中继模式，局部分布式协调模式，全局云传输模式和HPN模式；
所述分布式逻辑通信云由多个分布式基站组成，通过在多个分布式基站之间协作处理，达到释放前段链路的过载，减轻排队和传输等待时间的效果；
分布式逻辑存储云由接入雾计算体系架构中的受控用户设备组成；集中控制云位于物联网监测节点中，而全局集中通信和存储云又云计算中央服务器构成；
分布式逻辑通信云不仅集成了前射频，而且集成了本地分布式协同无线电信号处理和协同无线资源管理技术功能，而分布式逻辑存储云负责在边缘设备中的本地存储和高速缓存；集中控制云用于充当控制平台的作用，负责向下层云下达控制信令；
全局集中通信和存储云类似于传统的云计算中的云计算中心服务器，负责与用户 设备和分布式基站传输和交互数据，担当集中存储和集中通讯功能；
最终云计算中心服务器根据需求，将接受到的数据有选择地提供给物联网应用层中的用户；
还包括：
终端层，网络接入层，云计算层和雾计算层；雾计算层由分布式基站演化为的雾计算接入点，终端层和网络接入层中的智能设备制定的；在终端层中，设备在D2D模式中直接与相邻设备进行通信，而无需由分布式基站演化为的雾计算接入点的帮助，如果两个潜在配对的设备的通信距离超过D2D距离阈值，则将触发基于第三方设备的中继模式，以为这两个设备提供通信；
在网络接入层中，存在两种类型的边缘通信实体：物联网监测节点和雾计算接入点。物联网监测节点用于传递整体控制信令，并为高速移动的设备提供具有基本比特率的无缝覆盖；云计算层是软件定义的，集中式计算和缓存的属性；
所有信号处理单元在大型物理基带单元池中一起工作，以共享整个雾计算的信令，业务数据以及信道状态信息；
受控用户设备自适应地接入雾计算无限网路中，并且根据用户设备的移动速度，通信距离，位置，服务质量要求，处理和高速缓存能力来选择四种传输模式：D2D中继模式，局部分布式协调模式，全局云传输模式和HPN模式。
在D2D中继模式中，两个用户设备通过D2D或基于智能设备的无线中继技术彼此进行通信；本地分布式协调模式意味着受控用户设备接入相邻的分布式基站中，并且通信在此结束，仅将传输结果反馈上传；全局云传输模式意味着所有本地分布式协同无线电信号处理和协同无线资源管理技术功能在分布式基站中集中实现，具有高移动速度或在分布式逻辑通信云的覆盖空洞中的受控用户设备必须访问由HPN模式表示的物联网监测节点。
有益效果：本发明设计了一种物联网环境下的雾计算体系架构，其核心思想是充分利用本地无线电信号处理，合并无线电资源管理和边缘设备中的分布式存储能力，以达到减少前端的重负荷，从而获取更高的系统容量和处理速率，满足第五代无线通信系统(5G)的需求。从移动应用的角度来看，如果传输发生在本地或者相同的内容存储在相邻的分布式基站中，则用户设备不必连接到云计算中心服务器基带单元来下载。同时，雾计算将传统的云计算模式扩展到网络边缘，协作无线电信号处理不仅可以在云计算中心服务器中执行，而且可以在分布式基站以及智能设备处被托管。此外，雾计算可以通过设备到设备(D2D)，无线中继，分布式协调和大规模集中合作之间的自适应技术来实现以用户为中心的目标，最终适应移动传输发展的需要。
附图说明
图1为本发明一种物联网环境下的雾计算体系架构的系统架构图；
图2为本发明一种物联网环境下的雾计算体系架构的模式选择伪代码。
具体实施方式
以下结合附图和具体实施例对本发明作进一步详细说明。
拟议的F-RAN充分利用云计算，异构网络和雾计算的融合。定义了四种云：全局集 中通信和存储云，集中控制云，分布式逻辑通信云和分布式逻辑存储云。分布式逻辑通信云不仅集成了前射频，而且集成了本地分布式协同无线电信号处理和协同无线资源管理技术功能，而分布式逻辑存储云负责在边缘设备中的本地存储和高速缓存。集中控制云用于充当控制平台的作用，负责向下层云下达控制信令。全局集中通信和存储云类似于传统的云计算中的云计算中心服务器，负责与用户设备和分布式基站传输和交互数据，担当集中存储和集中通讯功能。
如图1中所示的所提出的用于实现雾计算体系架构的系统模型。其包括终端层，网络接入层，云计算层和雾计算层。
雾计算层是由分布式基站演化为的雾计算接入点，终端层和网络接入层中的智能设备制定的。
在终端层中，相邻的受控用户设备可以通过D2D模式或基于分布式基站的中继模式相互通信。例如，设备3和设备1可以在设备2的帮助下彼此通信，其中设备2可以被认为是移动中继。如果存在要在设备1和设备2之间直接发送的一些数据，则使用D2D模式。
网络接入层由分布式基站演化为的雾计算接入点和物联网监测检点组成。所有通讯设备接入物联网监测节点以获得与系统信息相关的所有信令，这些信令充当控制平台的功能。此外，由分布式基站演化为的雾计算接入点用于转发和处理接收的数据。雾计算接入点通过前端链路与云计算层中的云计算中心服务器基带单元接口，而雾计算接入点通过回程链路接入云计算中心服务器基带单元，并将前端链路上的信号发送至云计算服务器中被大规模处理。因为大量的协同无线电信号处理和协同无线资源管理功能被转移到由分布式基站演化为的雾计算接入点和智能设备，前端和云计算中心服务器基带单元的负担池减轻。此外，物联网监测点和智能设备中的有限高速缓存可以将一些分组服务分配到边缘设备中，而不是在云计算中心服务器总集中式缓存。
为了在边缘设备中引入雾化计算，传统的RRH通过配备一定的缓存，CRSP和CRRM能力，演化为基于雾计算的接入点。由分布式基站演化为的雾计算接入点主要用于为接入的设备处理本地协同无线电信号处理和协同无线资源管理，为以D2D传输模式运行的设备提供干扰抑制和频谱共享，并通过前端将接收到的信息压缩转发到云计算中心服务器基带单元。雾计算接入点不仅集成了前射频，而且集成了本地分布式协同无线电信号处理和简单的协同无线资源管理功能。通过在多个相邻的雾计算接入点之间协作处理，从而释放前端链路的过载，同时可以减轻排队和传输等待时间。当所有协同无线电信号处理和协同无线资源管理功能被移动到云计算中心服务器基带单元时，雾计算接入点退化为传统的分布式基站。
当业务负载低时，一些空闲的分布式基站演化为的雾计算接入点落入睡眠模式。当一特殊区域中业务负载变得巨大时，雾计算接入点和物联网监测点变得活跃以承载高容量的传输通讯业务，并且可以进一步触发D2D或中继模式以满足巨大的容量要求。
以上三者通过相互合作构成雾计算层。下面介绍一种物联网环境下的雾计算体系架构的分层架构中的其余部分。
在终端层中，设备可以在D2D模式中直接与相邻设备进行通信，而无需由分布式基站演化为的雾计算接入点的帮助，其中物联网监测节点用于向D2D配对的设备递送整体控制信令。通过与雾计算接入点连接的设备重复使用相同的无线资源，D2D模式特别有利于满 足高速率传输数据的需求，并且还能够提高总吞吐量。然而，D2D模式受通信距离和雾计算接入点的能力严重制约，并且不能提供不支持D2D模式的传统设备服务。如果两个潜在配对的设备的通信距离超过D2D距离阈值，则将触发基于第三方设备的中继模式，以为这两个设备提供通信。
在网络接入层中，存在两种类型的边缘通信实体：物联网监测节点和雾计算接入点。物联网监测节点主要用于传递整体控制信令，并为高速移动的设备提供具有基本比特率的无缝覆盖。具有大规模多输入多输出(MIMO)的物联网监测节点是保证雾计算体系架构与现有无线系统的向后兼容性的关键。雾计算体系架构的整体控制信道开销和小区特定参考信号由物联网监测节点递送，因此雾计算可以减少不必要的切换并减轻同步约束。如果协同无线电信号处理和协同无线资源管理功能在雾计算接入点中结束，则它们具有小区基站的相同功能，其中采用类似于协调多点发送和接收(CoMP)的分布式干扰协调来抑制层内和层间干扰。
云计算层是软件定义的，其特征在于集中式计算和缓存的属性。所有信号处理单元在大型物理基带单元池中一起工作，以共享整个雾计算的信令，业务数据以及信道状态信息。当网络负载增长时，运营商只需要升级基带单元池以适应增加的容量。
如图2所示。本方案提出了一种自适应模式选择，充分利用这四种模式。
所述的一种物联网环境下的无计算体系架构中的受控用户设备自适应地接入雾计算无限网路中，并且根据用户设备的移动速度，通信距离，位置，服务质量要求，处理和高速缓存能力来选择四种传输模式：D2D中继模式，局部分布式协调模式，全局云传输模式和HPN模式。
在D2D和中继模式中，两个设备经由D2D或基于第三方设备的无线中继技术彼此进行通信。本地分布式协调模式意味着设备接入相邻的分布式基站，并且通信在此结束。全局云传输模式意味着所有本地分布式协同无线电信号处理和协同无线资源管理技术功能在分布式基站中集中实现，具有高移动速度或在分布式逻辑通信云的覆盖空洞中的受控用户设备必须访问由HPN模式表示的物联网监测节点。
除了所有设备周期性地监听所传送的控制信令之外，在物联网监测节点的监管下由接入的设备选择最佳传输模式。为了确定每个设备的最优传输模式，首先根据来自物联网监测节点的导频信道预估设备的移动速度和与其余不同设备的距离。如果设备处于高速移动状态或需要提供实时语音通信服务，则以高优先级触发HPN模式。如果彼此通信的两个设备具有较慢的相对移动速度并且它们的距离不大于阈值D1，则触发D2D模式。否则，如果它们的距离大于D1但小于D2，并且存在一个相邻的设备可以作为用于这两个设备的基于第三设备的中继通讯，则基于第三设备的中继模式被触发，以此来实现比其他模式更好的性能。此外，如果两个期望的F-UE移动缓慢，以及它们的距离大于D2但小于D3，或它们的距离不大于D2，但是至少一个设备不支持D2D和中继模式，则采用本地分布式协调模式。如果本地分布式协调模式不能提供预期的性能，或者两个期望的F-UE之间的距离大于D3，或者所传输的内容来自云服务器，则触发全局云传输模式。

Claims (2)

  1. 一种物联网环境下的雾计算体系架构，其特征在于，包括：物联网环境下的雾计算体系架构的“四种云+应用层”式布局及四种传输模式，
    即全局集中通信和存储云，集中控制云，分布式逻辑通信云，分布式逻辑存储云和物联网应用层；
    D2D中继模式，局部分布式协调模式，全局云传输模式和HPN模式；
    所述分布式逻辑通信云由多个分布式基站组成，通过在多个分布式基站之间协作处理，达到释放前段链路的过载，减轻排队和传输等待时间的效果；
    分布式逻辑存储云由接入雾计算体系架构中的受控用户设备组成；集中控制云位于物联网监测节点中，而全局集中通信和存储云又云计算中央服务器构成；
    分布式逻辑通信云不仅集成了前射频，而且集成了本地分布式协同无线电信号处理和协同无线资源管理技术功能，而分布式逻辑存储云负责在边缘设备中的本地存储和高速缓存；集中控制云用于充当控制平台的作用，负责向下层云下达控制信令；
    全局集中通信和存储云类似于传统的云计算中的云计算中心服务器，负责与用户设备和分布式基站传输和交互数据，担当集中存储和集中通讯功能；
    最终云计算中心服务器根据需求，将接受到的数据有选择地提供给物联网应用层中的用户；
    还包括：
    终端层，网络接入层，云计算层和雾计算层；雾计算层由分布式基站演化为的雾计算接入点，终端层和网络接入层中的智能设备制定的；在终端层中，设备在D2D模式中直接与相邻设备进行通信，而无需由分布式基站演化为的雾计算接入点的帮助，如果两个潜在配对的设备的通信距离超过D2D距离阈值，则将触发基于第三方设备的中继模式，以为这两个设备提供通信；
    在网络接入层中，存在两种类型的边缘通信实体：物联网监测节点和雾计算接入点。物联网监测节点用于传递整体控制信令，并为高速移动的设备提供具有基本比特率的无缝覆盖；云计算层是软件定义的，集中式计算和缓存的属性；
    所有信号处理单元在大型物理基带单元池中一起工作，以共享整个雾计算的信令，业务数据以及信道状态信息；
    受控用户设备自适应地接入雾计算无限网路中，并且根据用户设备的移动速度，通信距离，位置，服务质量要求，处理和高速缓存能力来选择四种传输模式：D2D中继模式，局部分布式协调模式，全局云传输模式和HPN模式。
  2. 如权利要求1所述的雾计算体系架构，其特征在于，在D2D中继模式中，两个用户设备通过D2D或基于智能设备的无线中继技术彼此进行通信；本地分布式协调模式意味着受控用户设备接入相邻的分布式基站中，并且通信在此结束，仅将传输结果反馈上传；全局云传输模式意味着所有本地分布式协同无线电信号处理和协同无线资源管理技术功能在分布式基站中集中实现，具有高移动速度或在分布式逻辑通信云的覆盖空洞中的受控用户设备必须访问由HPN模式表示的物联网监测节点。
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