CN108667924B - Gateway equipment establishing method for providing edge computing service - Google Patents
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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Abstract
A gateway equipment establishing method for providing edge computing service belongs to the field of Internet of things technology and application. According to the characteristics of the Raspberry Pi and the characteristics of the Home Assistant system, a wireless router module, a data analysis module and an equipment management module are constructed in the ordinary Raspberry Pi, so that gateway equipment for providing edge computing service is obtained, and the functions of a router, data analysis and equipment management are realized. The wireless router module in the Raspberry Pi is used for realizing the internet access function of the IOT equipment and can communicate with a cloud server center and other equipment terminals; the data analysis module collects data generated by the IOT equipment and analyzes and processes the data before the data reach the cloud center; and the equipment management module realizes corresponding equipment control response to the sensor data. The invention not only has a narrow router, but also has the task of providing service for edge calculation, and can meet the requirement of the development of gateway equipment of the Internet of things in the future.
Description
Technical Field
The invention relates to a method for establishing gateway equipment for providing edge computing service, and belongs to the field of Internet of things technology and application.
Background
The number of global mobile terminal connections is expected to approach 180 billion by 2030, with china reaching 30 billion; the global internet of things device connection quantity is remarkably increased, and the global internet of things device connection quantity is expected to exceed 1 billion in 2030, wherein the Chinese internet of things device connection quantity exceeds 200 billion. At present, a large amount of IOT equipment is stored in a cloud service center, and the real-time interaction between a user and the cloud center needs a high-speed transmission rate, so that huge flow pressure is caused to a network in a hot spot area; and the service with high real-time performance needs low delay of end-to-end millisecond level, so that the requirement is difficult to meet by the conventional cloud computing network architecture. The proposal of the edge computing concept effectively solves the problems.
As is well known, the gateway of internet of things plays a very important role in the future era of internet of things, and becomes a link for connecting the sensing network and the traditional communication network. As a gateway device, the internet of things gateway at the present stage can only implement protocol conversion between the sensing network and the communication network, and between different types of sensing networks, but does not have functions such as data analysis and device management. However, with the development of the edge computing concept, the internet of things gateway in the future network architecture is also required to have functions of data analysis, device management and the like, so that a developer can manage each sensing node at the bottom layer through the internet of things gateway device, know relevant information of each node, and realize remote control.
The gateway equipment established by the method integrates routing, data analysis and equipment management functions, and can directly process most of tasks of the Internet of things nearby by virtue of self computing and processing capabilities, so that the workload of a cloud service center is reduced, and the accuracy and efficiency of the gateway equipment responding to different states are improved.
In summary, the gateway device plays a crucial role in edge computing, so the establishment of the gateway device is an important ring in the edge computing framework.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a gateway equipment establishing method for providing edge computing service. According to the characteristics of Raspberry Pi (Raspberry pie) itself: 1) the portable and portable electric bicycle has the advantages of portability, portability and high cost performance; 2) support for multiple programming languages; 3) multithreading and multitasking can be operated simultaneously, and 4) the system has expandability and rich network functions; 5) the gateway device also has good computing capability and all the basic functions of the PC, which are not possessed by the prior common gateway device. By combining the characteristics of the HomeAssistant system, a developer can shield the details of the communication of the platform underlying network and concentrate on the realization of program functions, so that the Raspberry Pi is constructed into a gateway device. The wireless routing module, the data analysis module and the equipment management module are established for the Raspberry Pi, so that the single independent establishment of the gateway equipment for providing the edge computing service is completed. The aggregation, optimization and screening can be completed when the data reaches the layer of gateway equipment; the acquired data is subjected to pre-analysis processing, so that the equipment directly reacts, and meanwhile, the result and the high-value data are uploaded to the cloud; in addition, unified management can be performed on the equipment accessing to the Raspberry Pi. The gateway equipment designed by the method not only has a narrow router, but also has the task of providing service for edge computing, and meets the requirement of development of gateway equipment of the internet of things in the future.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the method for establishing the gateway equipment for providing the edge computing service comprises the steps of constructing a wireless router module, a data analysis module and an equipment management module in a common Raspberry Pi to obtain the gateway equipment for providing the edge computing service, and realizing the functions of the router, the data analysis and the equipment management.
The Raspberry Pi is characterized in that a wireless router module is constructed for realizing the internet access function of the IOT equipment and can be communicated with a cloud server center and other equipment terminals, and the method comprises the following specific steps:
(1) and selecting OpenWRT as an operating system of the router, and taking charge of the management of hardware resources and a software system of the router.
(2) And selecting a wireless network card supporting the 802.11ac standard by adopting a framework provided by OpenWRT, starting an AP functional module of a wireless network adapter of the Raspberry Pi by calling a hostapd process by the Raspberry Pi, and sharing a wired network of the wireless network adapter.
(3) And calling a DHCP network protocol, wherein the Raspberry Pi can centrally manage and dynamically allocate the IP address, so that the IOT device connected to the Raspberry Pi can dynamically obtain information such as the IP address, the Gateway address, the DNS server address and the like.
(4) To ensure that the IOT device can communicate with external networks, ipv4 forwarding is implemented herein by defining nat rules with the Linux kernel integrated iptables.
Therefore, the IOT equipment can be connected with the Raspberry Pi according to a mode of being connected with a common router, so that the internet surfing function is realized, and the IOT equipment can be communicated with a cloud server center and other equipment terminals.
The Raspberry Pi is provided with the data analysis module, so that a large amount of data generated by the IOT equipment can be collected and analyzed or processed before the data reaches the cloud center, and the data volume and the round-trip delay of the data uploaded to the cloud server center are reduced. The data analysis module comprises a data collection sub-module, a training model establishing sub-module, a model training sub-module and a data prediction analysis sub-module. Data analysis processing is deployed on a data analysis module of a Raspberry Pi, because a Tensorflow artificial intelligence learning system can automatically run a model on each platform; and supporting heterogeneous device distributed computing; and the characteristics of the most popular deep neural network model at present and the like are supported. Therefore, the Tensorflow is selected for analyzing and processing the data, and the specific steps are as follows:
1) a series of data collected by the IOT device (e.g., a temperature and humidity sensor), which the Raspberry Pi stores in some way;
2) then selecting required data by using a specific tool, reading the data and taking the data as input;
3) then, selecting and establishing a training model according to the characteristics of the data;
4) carrying out iterative training on data to be analyzed and predicted by using Tensorflow until convergence and storing the trained model;
5) and finally, calling the existing data and inputting the data into the model for data analysis and prediction.
The data collection submodule comprises: the system is mainly responsible for collecting various data and automatically storing the data in a system database;
the training model establishing submodule comprises: selecting a proper training model according to the characteristics of the collected data;
the model training submodule comprises: the method is mainly responsible for iterative training through a selected training model and storing a finally converged model;
the data prediction analysis sub-module: the method mainly brings data to be analyzed and predicted into a trained model to obtain a final result.
A device management module is constructed in the Raspberry Pi, so that corresponding device control response to sensor data is realized; a service may be invoked to view device history data; unified management of IOT equipment; the UI interface may also be customized according to user preferences. The equipment management module also comprises an equipment data management module, a data integration module, an equipment registration module, an equipment control module, an equipment tracking module, a UI self-defining module and a third-party service module. Deploying the device management function to a device management module on a Raspberry Pi, specifically comprising the following steps:
1) raspberry Pi selects a Home Assistant system as a development basis of a functional module, the HomeAssistant is a mature and complete Python-based system, and supports multifunctional highly customized setting;
2) calling SMB service and starting the network sharing function of the system;
3) designing a configuration module of a system into a container, and operating the service of the system, the service of a third party, a code written by a user and the like in the container;
4) deploying functions of sensor data collection and display, equipment control, equipment tracking, and the like into a configuration module;
5) according to the user preference, the UI self-defining module can be modified;
6) the Raspberry Pi calls a configuration module;
7) finally, a plurality of Raspberry Pi can be distributed and deployed, so that an edge computing overall framework is formed.
The data management module: the data acquisition and display device is mainly responsible for interface display and storage of collected data;
the data integration module: the system is mainly responsible for integrating and optimizing collected data, and selecting and transmitting the optimized data to a cloud service center;
the device registration module: mainly responsible for registering and registering the terminal device accessed to the gateway device;
the equipment control module is as follows: the control action of the associated equipment is automatically or manually carried out according to the data transmitted to the gateway equipment by the equipment;
the device tracking module: the method mainly comprises the steps of performing off-line/on-line management on a mobile terminal accessed to the gateway equipment and simply positioning the mobile terminal;
the UI self-defining module comprises: the user can modify the preference of the system interface;
the third-party service module: third party services supported by the system may be invoked.
The invention has the beneficial effects that:
1) and screening the data of the IOT equipment, filtering and analyzing the data at the gateway equipment, and transmitting the optimized data to the cloud center.
2) The gateway device can command and control the device by processing and analyzing a part of data.
3) By establishing the gateway device, IOT devices in the network can be managed in a unified manner, and the burden of a cloud service center is reduced.
4) The Raspberry Pi is light and cheap relative to other gateway devices, has strong computing power and high cost performance.
Drawings
Fig. 1 is a flow chart of the gateway device of the present invention in the edge computing.
Fig. 2 is an overall architecture diagram of the gateway device of the present invention.
Fig. 3 is a diagram of a gateway device routing function module implementation of the present invention.
Fig. 4 is a flow chart of data analysis of the gateway device of the present invention.
Fig. 5 is a diagram of a device management module structure of the gateway device of the present invention.
Detailed description of the preferred embodiments
In the edge computing overall architecture, the gateway equipment is placed at a place close to a mobile terminal, a sensor and a PC end user, and when the terminal equipment adopts computing services of a cloud data center, data and nearest local gateway equipment are interacted, so that the problem of high delay in cloud computing is well solved through the gateway equipment. The gateway equipment has complete computing capacity and storage capacity, and can be connected with the equipment terminal in the same local area network through localized deployment. The gateway device is connected with the cloud terminal through a stable return link, and the distance between the terminal user and the computing resource can be controlled within a one-hop range, wherein the one-hop range refers to that the gateway device and the terminal user are generally connected through Wi-Fi, and the terminal user within the Wi-Fi coverage range can adopt computing and storage services provided by the gateway device.
After the gateway equipment is added at a position close to the terminal, the working process of the whole network is greatly different from the traditional working process. As shown in fig. 1, in the edge computing network framework, a terminal user requests service content from a gateway device first, and if the gateway device can independently complete the request of the terminal user, the terminal user will be directly served and a corresponding result is returned; if the gateway equipment cannot independently complete the request of the terminal user, the request is forwarded to the core network through the gateway equipment, and the cloud service center provides service for the user.
The core part of the Raspberry Pi in the edge computing network is mainly composed of two parts, namely an infrastructure and an application platform. As shown in fig. 2, the infrastructure of the gateway device is mainly composed of a physical layer and a virtual layer, and the physical layer is mainly hardware support providing storage, computation, and control functions. The virtual layer is realized by network virtualization technology, and provides virtualized computing processing, virtual storage and corresponding management functions. The application platform of the gateway device provides some middleware services and basic services, and applications running on the gateway device call these services to maintain normal operation of the applications, where these services include communication services, service registration, data analysis, device management, and the like. According to the characteristics of the Home Assistant, a developer can shield the details of the communication of the platform bottom layer network and concentrate on the realization of the program function. In addition, the gateway device can acquire information such as the number of users, user information, sensor data, device control, throughput of the gateway device, and the like through some services provided by the HomeAssistant itself. The mobile terminal, the sensor and the PC terminal device in the figure can call the service provided on the gateway device.
Under the support of the gateway device hardware infrastructure, the application platform can be divided into three major modules: the device comprises a router module, a data analysis module and a device management module.
As shown in fig. 3, in the routing module of the Raspberry Pi, first, the Raspberry Pi is connected to the network through its own LAN port, and selects the wireless network card supporting the 802.11ac standard; then, the Raspberry Pi calls a hostapd process to start an AP function module of the wireless network adapter of the Raspberry Pi, and a wired network of the wireless network adapter is shared; then, calling a DHCP network protocol to enable the Raspberry Pi to manage in a centralized way and dynamically allocate IP addresses, so that the IOT equipment connected to the Raspberry Pi can dynamically acquire information such as IP addresses, Gateway addresses, DNS server addresses and the like; to ensure that the IOT device can communicate with external networks, ipv4 forwarding is implemented herein by defining nat rules with the Linux kernel integrated iptables. Therefore, the IOT equipment can realize the internet surfing function and can be communicated with a cloud server center and other equipment terminals.
If the data generated by the IOT device is directly transmitted to the cloud service center, it brings great challenges to the communication energy consumption of the general terminal and the network bandwidth for uploading the data. For the Raspberry Pi data analysis module, as shown in fig. 4, the flow of analyzing and predicting the collected data is described. After the Raspberry Pi receives the data of the IOT equipment, selecting the needed data to store in a CSV mode; reading data in the file in the CSV format by creating a data analysis job, and storing the processed data; defining a prediction function, and selecting an establishment model; and training with Tensorflow until convergence; and inputting the selected data into the trained model for prediction to obtain a prediction result.
The equipment management module of the Raspberry Pi not only can provide rich convenient computing capability for a terminal user, but also can be accessed to a network environment anytime and anywhere by utilizing the advantages of localization and close-range deployment of computing services of the equipment management module. As shown in fig. 5, the device management module mainly has the following functions: 1) the data transmitted by the IOT equipment can be displayed, integrated, screened and the like on the Raspberry Pi, and the optimized data is transmitted to the cloud data center; 2) through the IOT device data communication, control commands can be directly made to the corresponding devices, such as: controlling the upward/downward movement of the curtain by the data of the illumination sensor; 3) the mobile equipment which is generally connected with or disconnected from the Raspberry Pi is subjected to equipment tracking, basic information of the mobile equipment is displayed, and the mobile equipment is simply positioned.
By establishing the three modules for the Raspberry Pi, after a deployment task is completed, a terminal user can be ensured to access a wireless network at any time and any place, analysis of data and management and control of equipment can be completed under the condition that a data request is not sent to a cloud service center, and the requirement of future development of gateway equipment of the Internet of things is met.
Claims (1)
1. A method for establishing gateway equipment for providing edge computing service is characterized in that the method for establishing gateway equipment for providing edge computing service is obtained by establishing a wireless router module, a data analysis module and an equipment management module in a common Raspberry Pi, and the router function, the data analysis function and the equipment management function are realized;
the Raspberry Pi is internally provided with a wireless router module for realizing the internet access function of the Internet of things equipment and communicating with the cloud server center and the Internet of things equipment, and the method comprises the following specific steps:
(1) selecting OpenWRT as an operating system of the wireless router module, and taking charge of management of hardware resources and a software system of the wireless router module;
(2) selecting a wireless network card supporting the 802.11ac standard by adopting a framework provided by OpenWRT, starting an AP functional module of a wireless network adapter of Raspberry Pi by calling a hostapd process by the Raspberry Pi, and sharing a wired network of the wireless network adapter;
(3) then, a DHCP network protocol is called, and the Raspberry Pi centrally manages and dynamically allocates IP addresses, so that the Internet of things equipment connected to the Raspberry Pi can dynamically acquire IP addresses, Gateway addresses and DNS server address information;
(4) the ipv4 forwarding is realized by defining nat rules by using an iptables integrated by a Linux kernel, so that the communication between the Internet of things equipment and an external network is ensured;
a data analysis module is constructed in the Raspberry Pi and is used for collecting data generated by the equipment of the Internet of things and analyzing or processing the data before the data reaches the cloud server center; the data analysis module comprises a data collection sub-module, a training model establishing sub-module, a model training sub-module and a data prediction analysis sub-module; the data collection submodule is responsible for collecting various data and automatically storing the data in a system database; the training model establishing submodule selects a proper training model according to the characteristics of the collected data; the model training submodule is responsible for carrying out iterative training by selecting a good training model and storing a finally converged model; the data prediction analysis submodule brings data to be analyzed and predicted into a trained model to obtain a final result; deploying data analysis processing to a data analysis module of a Raspberry Pi, and selecting Tensorflow to analyze and process data, wherein the concrete steps are as follows:
1) collecting data through the Internet of things equipment, and storing the data through a Raspberry Pi;
2) selecting required data, reading out the data and taking the data as input;
3) selecting and establishing a training model according to the data characteristics;
4) performing iterative training on data to be analyzed and predicted by adopting Tensorflow until convergence, and storing a trained model;
5) finally, calling the existing data and inputting the data into the model for data analysis and prediction;
a device management module is constructed in the Raspberry Pi to realize corresponding device control response to the sensor data; calling a service to check historical data of the equipment, and uniformly managing the equipment of the Internet of things; the UI interface is also customized by the user; the equipment management module also comprises an equipment data management module, a data integration module, an equipment registration module, an equipment control module, an equipment tracking module, a UI self-defining module and a third-party service module; the data management module is responsible for carrying out interface display and storage on the collected data; the data integration module is responsible for integrating and optimizing the collected data, and selecting the optimized data to transmit to the cloud service center; the equipment registration module is responsible for registering and registering the Internet of things equipment accessed into the gateway equipment; the equipment control module automatically or manually performs control actions of associated equipment according to data transmitted to the gateway equipment by the equipment; the device tracking module performs offline/online management on the Internet of things device accessed to the gateway device and simply positions the Internet of things device; the UI self-defining module is used for modifying the system interface by the user; deploying the device management function to a device management module on a Raspberry Pi, specifically comprising the following steps:
1) the Raspberry Pi selects a Home Assistant system as a development basis of a functional module;
2) calling SMB service and starting the network sharing function of the system;
3) designing a configuration module carried by the system into a container, and running the service of the system, the service of a third party and a code written by a user in the container;
4) deploying sensor data collection and display, equipment control and tracking functions of the equipment into a configuration module;
5) modifying the UI self-defining module according to the user preference;
6) the Raspberry Pi calls a configuration module;
7) and finally, carrying out distributed deployment on a plurality of Raspberry Pi, thereby forming an edge computing integral framework.
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