KR102012097B1 - Monitoring, analyzing and controlling platform, fog server equipped with the same and fog computing system including the fog server - Google Patents

Abstract

A monitoring, analysis and control platform, a fog server equipped with the same, and a fog computing system including the same are disclosed. The monitoring, analysis, and control platform is connected to an IoT device node, and is a monitoring framework for receiving data generated or acquired at the IoT device node, and analyzing the data generated or acquired at the IoT device node to be applied to the IoT device node. An analysis framework for generating new knowledge and a control framework for generating a control signal for the control of the IoT device node based on the knowledge generated by the analysis framework and transmits to the IoT device node.

Description

MONITORING, ANALYZING AND CONTROLLING PLATFORM, FOG SERVER EQUIPPED WITH THE SAME AND FOG COMPUTING SYSTEM INCLUDING THE FOG SERVER}

The present invention relates to a monitoring, analysis and control platform, a fog server equipped with the same, and a fog computing system including the same. More specifically, a monitoring, analysis and control platform for providing an Internet of Things (IoT) service, A fog server equipped with the same and a computing system including the same.

With the development of network technology, an Internet of Things (IoT) service has emerged that analyzes data collected by objects connected to the Internet and provides services based on them. IoT devices with sensors and actuators are interoperable and applied to a wide range of applications, increasing the number of users using IoT services.

IoT devices, on the other hand, are getting smarter, they can recognize the IoT environment themselves, and they can also receive commands from remote servers to perform specific tasks. These IoT devices can collect and generate large quantities of various kinds of data. IoT devices can send large amounts of data to cloud servers, which can analyze and process data received from IoT devices. Such cloud computing is in the spotlight for efficiently managing smarter IoT devices.

However, cloud computing is physically far from the IoT device, causing frequent data transmission delays, and it is difficult to handle all of the large capacity constantly generated from the increasing IoT devices. Therefore, fog computing has been proposed that minimizes response time of IoT devices and enables real-time processing.

Fog computing is a highly platform that provides computing, storage and networking services between end devices and cloud servers. The fog server may be located between the IoT device and the cloud server. Fog computing can connect to cloud servers through fog servers to provide physical proximity that enables users' real-time applications, location-based services, and mobility support. Accordingly, fog computing is applied to smart homes, smart offices, smart factories, and smart grids to provide various services, and related researches are being actively conducted.

For example, ParaDrop, an edge computing framework that enables developers to utilize computing resources in the user domain, has been proposed. This allows developers to design virtualized computing resources to provide services near users. Users can be provided with resource control services through dynamic installation and management policy design using developer API (Application Programming Interface).

In addition, an extended framework has been proposed through a policy management module to support the orchestration layer of fog computing. The extended framework consists of modules such as policy decision engine, policy resolver, policy store, and policy enforcer. These modules can perform data-driven decisions, user authentication and validation, and policy storage.

In addition, a mathematical framework for heterogeneous resource sharing based on service-oriented utility functions has been proposed. This can be especially maximized in fog computing, where different units can be quantified to map all numbers equally to time resources. Accordingly, service latency can be effectively reduced and energy efficiency can be increased.

In addition, an online assistant framework has been proposed that incorporates an online algorithm to find a suitable set of fog device neighbors. This can minimize delays by forming a fog network around a given fog device. This enables proper distribution of computing resources to remote cloud servers and fog networks.

As such, various researches on fog computing have been conducted, but most of them focus on specific services such as increasing user proximity, efficient policy management, and minimizing delay. In other words, the existing research on fog computing focuses on a framework for solving specific problems occurring in fog computing, and research on fog computing solutions that can expand new services or integrate various IoT devices is insufficient.

One aspect of the invention provides a monitoring, analysis and control platform that performs monitoring, analysis and control functions of an IoT device using a common API.

Another aspect of the invention provides a fog server equipped with a monitoring, analysis and control platform that performs monitoring, analysis and control functions of an IoT device using a common API.

Another aspect of the present invention is to construct a fog node consisting of a fog server and fog storage equipped with a monitoring, analysis and control platform that performs the monitoring, analysis and control functions of the IoT device using a common API, IoT device nodes Provide a fog computing system.

Monitoring, analysis and control platform according to an aspect of the present invention is connected to the IoT device node, the monitoring framework for receiving data generated or acquired at the IoT device node, the data generated or acquired at the IoT device node to analyze An analysis framework for generating new knowledge to be applied to the IoT device node and a control framework for generating a control signal for controlling the IoT device node and transmitting the control signal to the IoT device node based on the knowledge generated by the analysis framework. It includes.

On the other hand, the monitoring framework, the device connector for controlling the access of the IoT device node, receives the data generated or acquired in the IoT device node from the IoT device node, and classifies and stores the received data according to characteristics It may include a data manager and a device monitor for monitoring the status of the IoT device node.

The data manager may classify data generated or acquired by the IoT device node into user generated data, system generated data, and environment data.

In addition, the device monitor may monitor a process, an infrastructure network, and an I / O state running in the IoT device node.

The analysis framework may further include a change tracker for tracking changes in data generated or acquired by the IoT device node, a feature extractor for extracting features from the change information tracked by the change tracker, and the feature extractor. According to a feature may include an integrated analyzer for generating new knowledge to be applied to the IoT device.

The control framework may further include a trigger updater that updates an event trigger of the IoT device node based on knowledge generated by the analysis framework, a module updater that performs a software module update of the IoT device node, and the trigger updater. It may include a device controller for transmitting the event trigger updated by the IoT device node.

In addition, the device controller receives defect information of the IoT device node according to the monitoring result of the IoT device node through the monitoring framework, identifies the cause of the defect of the IoT device node, and accordingly the platform manager Notify or confirm the software module update.

In addition, a common API may be used between the IoT device node and the monitoring framework or the control framework, between the monitoring framework, the analysis framework, and the control framework.

Meanwhile, another aspect of the present invention may be a fog server equipped with a monitoring, analysis, and control platform.

Meanwhile, the fog computing system according to another aspect of the present invention is an IoT device node connecting an IoT device and an external server to generate or acquire various data, and data connected to the IoT device node and generated or acquired by the IoT device. Receive the data, classify and store data generated or acquired by the IoT device by characteristics, analyze data generated or acquired by the IoT device, and generate a control signal for controlling the IoT device according to an analysis result And a fog node for transmitting to the IoT device node.

Meanwhile, the fog node is connected to the IoT device node to receive data generated or acquired by the IoT device, and classify data generated or acquired by the IoT device into user generated data, system generated data, and environmental data. It may include a fog server to provide a storage space classified into a fog server and user-generated data, system-generated data and environmental data.

In addition, the IoT device node may include an edge gateway connected to the IoT device to provide an interface for connecting to an external server of the IoT device.

According to one aspect of the present invention described above it is possible to build a consistent interface that is easy to expand new services or to integrate various IoT devices, it is possible to ensure excellent scalability.

1 is a conceptual diagram of a fog computing system according to an embodiment of the present invention.
2 is a conceptual diagram of a fog server equipped with a monitoring, analysis, and control platform according to an embodiment of the present invention.
3 and 4 are algorithms for implementing the operation of the monitoring, analysis and control platform shown in FIG.
FIG. 5 is a diagram illustrating a data flow in the fog computing system shown in FIG. 1.
FIG. 6 is a function call graph in the fog computing system shown in FIG. 1.

Advantages and features of the present invention and methods for achieving them will be apparent with reference to the embodiments described below in detail with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but will be implemented in various forms, and only the present embodiments are intended to complete the disclosure of the present invention, and the general knowledge in the art to which the present invention pertains. It is provided to fully convey the scope of the invention to those skilled in the art, and the present invention is defined only by the scope of the claims. Like reference numerals refer to like elements throughout.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, the words "comprises" and / or "comprising" do not exclude the presence or addition of one or more other components, steps, and actions.

1 is a conceptual diagram of a fog computing system according to an embodiment of the present invention.

Referring to FIG. 1, a fog computing system 1000 according to an embodiment of the present invention may be configured with a fog node 10 and an IoT device node 20.

The fog node 10 may include a fog server 11 and a fog storage 12. The fog server 11 may be connected to the IoT device node 20 to perform IoT device monitoring, analysis, and control. In the fog storage 12, data processed by the fog server 11 may be stored.

The IoT device node 20 may be divided into an area that enables an external server connection of the IoT device and an area that is built by the high-end IoT device 21 and an area that is built by the low-end IoT device 22. The high-end IoT device 21 can be connected to an external network by itself with high computing power and wide network bandwidth. However, the low-end IoT device 22 is composed of simple sensors and actuators to have poor computing power and narrow network bandwidth. Accordingly, the IoT device node 20 may include an edge gateway 23 to enable external network connection of the low-specific IoT device 22. The edge gateway 23 may be connected with the low specification IoT device 22 to provide an internal interface that enables external network connection of the low specification IoT device 22.

The fog computing system 1000 according to an embodiment of the present invention may implement an IoT service through the fog server 11 capable of monitoring, analyzing, and controlling IoT devices. In particular, the fog computing system 1000 according to an embodiment of the present invention is an IoT service by the monitoring, analysis and control platform 100 that builds the fog server 11, and expands a new service according to a user's request. And it can provide an interface that is easy to integrate various IoT devices. This will be described in detail with reference to FIG. 2.

2 is a conceptual diagram of a fog server equipped with a monitoring, analysis, and control platform according to an embodiment of the present invention.

Referring to FIG. 2, the monitoring, analysis and control platform 100 according to an embodiment of the present invention may build a fog server 11. The monitoring, analysis, and control platform 100 may include a monitoring framework 110, an analysis framework 120, and a control framework 130. Here, the D2F API may be used between the monitoring, analysis and control platform 100 and the IoT device node 20. In addition, the F2F API may be used between the frameworks included in the monitoring, analysis, and control platform 100. In addition, a D2D API may be used between the IoT device nodes 20. In addition, although not shown in FIG. 2, a MaaS API, an AaaS API, and a CaaS API may be used between the modules of the monitoring framework 110, the analysis framework 120, and the control framework 130, respectively. A summary of the API used in such a monitoring, analysis and control platform 100 is shown in Table 1 below.

At this time, D2F API, F2F API, D2D API, MaaS API, AaaS API and CaaS API are all common APIs. Accordingly, the monitoring, analysis and control platform 100 may use a common API without additional work even if the IoT device node 20 is added as well as a new framework, thereby ensuring excellent scalability.

Hereinafter, the monitoring framework 110, the analysis framework 120, and the control framework 130 constituting the monitoring, analysis, and control platform 100 will be described in detail.

The monitoring framework 110 may be connected to the IoT device node 20 to receive data generated or acquired by the IoT device node 20. The monitoring framework 110 may be composed of three modules: the device connector 111, the data manager 112, and the device monitor 113.

The device connector 111 may control access to the fog server 11 of the IoT device node 20. The device connector 111 may perform authentication of the IoT device node 20 requesting the fog server 11. The device connector 111 may connect the authenticated IoT device node 20.

The data manager 112 may periodically receive data generated or acquired by the IoT device node 20 from the IoT device node 20 authenticated by the device connector 111. The data manager 112 may classify the received data according to characteristics and store the received data in the fog storage 12. The data manager 112 may classify and store data generated or acquired by the IoT device node 20 into user generated data, system generated data, and environmental data. Here, the user generated data is personal data generated by the user through the IoT device, and may include, for example, a picture, a video, a schedule, and the like. The system generated data may be configuration files, event triggers, and sensing data generated by the IoT device itself. The environmental data is environment-related data in which the fog server 11 and the IoT device are located, and may include temperature, humidity, precipitation, and the like. Environmental data can be obtained directly from IoT devices or from servers such as the National Weather Server or Met Office.

The device monitor 113 may periodically monitor the state of the IoT device node 20. The device monitor 113 may monitor whether an unexpected fault occurs in the IoT device node 20. The device monitor 113 may monitor the processes, infrastructure networks, and I / O statuses running on the IoT device node 20.

The analysis framework 120 may generate new knowledge to be applied to the IoT device node 20 by analyzing data generated or acquired by the IoT device node 20. The analysis framework 120 may be composed of three modules: a change tracker 121, a feature extractor 122, and an integrated analyzer 123. In this case, the feature extractor 122 and the integrated analyzer 123 may use a learning engine module.

The change tracker 121 may periodically track a change in data generated or acquired by the IoT device node 20.

The feature extractor 122 may extract the feature by analyzing the change information of the IoT device node 20 tracked by the change tracker 121. The feature extractor 122 may extract the feature by applying the change information to the learning engine.

The integrated analyzer 123 may analyze the feature information extracted by the feature extractor 122 to generate new knowledge to be applied to the IoT device node 20. The integrated analyzer 123 may apply the feature information to the learning engine to generate new knowledge to be applied to the IoT device node 20.

The control framework 130 generates a control signal for controlling the IoT device node 20 based on the analysis result of the data generated or acquired by the IoT device node 20 and transmits the control signal to the IoT device node 20. Can be. The control framework 130 may be composed of three modules: a trigger updater 131, a module updater 132, and a device controller 133.

When new knowledge is generated by the integrated analyzer 123, the trigger updater 131 may update an event trigger for controlling the IoT device node 20 based on the newly generated knowledge. The trigger updater 131 may update event variables and parameters to be applied to the IoT device node 20 according to new knowledge generated by the integration analyzer 123.

The module updater 132 may periodically check for software module updates of the IoT device node 20. The module updater 132 may periodically check the update module provided from the vendor of the IoT device. The module updater 132 may perform module update of the IoT device node 20 when the update module provided from the vendor is confirmed. Alternatively, the module updater 132 may perform module update of the IoT device node 20 when a defect occurs due to the monitoring of the IoT device node 20 in the device monitor 113.

The device controller 133 may transmit the event trigger updated by the trigger updater 131 to the IoT device node 20. The device controller 133 may transmit the event variables and parameters updated by the trigger updater 131 to the IoT device node 20. In this case, the IoT device node 20 may execute a command according to event variables and parameters received from the device controller 133.

In addition, when a defect occurs due to the monitoring of the IoT device node 20 in the device monitor 113, the device controller 133 may check the cause. The device controller 133 may determine whether the defect of the IoT device node 20 is due to the internal design and implementation of the monitoring, analysis and control platform 100, or according to a software module provided by the vendor. If the device controller 133 determines that the cause of the defect of the IoT device node 20 is due to the internal design and implementation of the monitoring, analysis and control platform 100, the device controller 133 corresponds to an administrator of the monitoring, analysis and control platform 100. You can tell the facts. If the device controller 133 determines that the cause of the defect of the IoT device node 20 is due to the software module provided by the vendor, the device controller 133 may update the software module through the module updater 132 or notify the vendor of the fact. Can be.

3 and 4, the operation process of the monitoring, analysis and control platform 100 shown in FIG. 2 will be described in detail.

3 and 4 are algorithms for implementing the operation of the monitoring, analysis and control platform shown in FIG.

Referring to FIG. 3, the monitoring framework 110 may identify an operation process of connecting to the IoT device node 20 and storing data received from the IoT device node 20.

Specifically, the device connector 111 may check the IoT device node 20 currently connected using the checkConnection () API. The device connector 111 may call the accessControl () API to authenticate the IoT device node 20. Here, the IoT device node 20 may request a connection with the fog server 11 using the requestConnection () API. When authentication is completed by the device connector 111, the IoT device node 20 may use the send () API to send a data manager 112. ) Can be sent.

The data manager 112 may receive data generated or acquired by the IoT device node 20 using the receive () API. When the data manager 112 receives data from the IoT device node 20, the data manager 112 may periodically call the classify () API to classify the received data according to characteristics. In this case, the data manager 112 may classify the data generated or acquired by the IoT device node 20 into user generated data, system generated data, and environmental data.

Referring to FIG. 4, the monitoring framework 110 monitors the IoT device node 20 and checks an operation process of controlling the IoT device node 20 in the control framework 130 according to the monitoring result.

Specifically, the device monitor 113 may periodically monitor the IoT device node 20 by calling the monitor () API so that the IoT device node 20 may determine whether an unexpected defect has occurred. When the device monitor 113 finds a defect in the IoT device node 20, the device monitor 113 may transmit the corresponding defect information to the device controller 133.

When the device controller 133 receives the fault information of the IoT device node 20 from the device monitor 113, the device controller 133 may call the checkFault () API to check the cause of the corresponding fault. If the device controller 133 determines that a defect of the IoT device node 20 is caused by an internal design or process of the monitoring, analysis and control platform 100, the device controller 133 monitors, analyzes, and controls the corresponding defect information. Can be sent to the administrator. The device controller 133 may send defect information to the module updater 132 if it is determined that the defect of the IoT device node 20 is caused by a software module of the IoT device node 20 provided by the vendor.

The module updater 132 may check whether there is an update of the software module of the IoT device node 20 and perform a module update of the IoT device node 20. However, if the module updater 132 determines that an update of the software module does not exist, the module updater 132 may transmit defect information to the vendor.

In addition, the module updater 132 may periodically check whether the update of the software module of the IoT device node 20 or the update of the software module is provided by the vendor using the updateModule () API. Here, it is common to stop the operation of the entire process when updating the software at the IoT device node 20. Accordingly, the module updater 132 divides a large update module into smaller modules such as an execution state, a hardware configuration, an operation system, and the like, so as to perform a module update that does not affect the ongoing process of the IoT device node 20. Can be.

Hereinafter, a data flow in the fog computing system 1000 illustrated in FIG. 1 will be described with reference to FIGS. 5 and 6.

5 is a diagram illustrating a data flow in the fog computing system illustrated in FIG. 1, and FIG. 6 is a function call graph of the fog computing system illustrated in FIG. 1.

Referring to FIG. 5, the data flow between the IoT device node 20 and the fog node 10 may be checked. Here, the fog node 10 includes a fog server 11 and a fog mounted with a monitoring, analysis and control platform 100 including a monitoring framework 110, an analysis framework 120, and a control framework 130. Storage 12. The IoT device node 20 may be implemented as a high specification IoT device 21 or an edge gateway 23, which may be connected to the low specification IoT device 22.

The IoT device node 20 may transmit data generated or acquired by the high-end IoT device 21 or the edge gateway 23 to the data manager 112.

The data manager 112 may classify data received from the IoT device node 20 into user data, system data, and environment data and store the data in the fog storage 12.

The change tracker 121 may periodically analyze data stored in the fog storage 12 to extract data change information. The feature extractor 122 may extract the feature information by periodically analyzing the data change information. The integrated analyzer 123 may generate new knowledge by learning change information and feature information periodically extracted by the change tracker 121 and the feature extractor 122 through the learning engine.

The trigger updater 131 may update event variables and parameters for controlling the IoT device node 20 based on the new knowledge generated by the integration analyzer 123. The device controller 133 may transmit the event variable and the parameter updated by the trigger updater 131 to the IoT device node 20. The module updater 132 may periodically perform module update of the IoT device node 20.

The IoT device node 20 may execute a command according to event variables and parameters received from the device controller 133.

Referring to FIG. 6, the API used between the monitoring framework 110, the analysis framework 120, and the control framework 130 included in the IoT device node 20 and the monitoring, analysis, and control platform 100; The APIs used between the monitoring framework 110 internal modules, the APIs used between the analysis framework 120 internal modules, and the APIs used between the control framework 130 internal modules are all common.

As such, the fog computing system 1000 according to an embodiment of the present invention may establish a consistent interface through the fog server 11 equipped with the monitoring, analysis, and control platform 100. That is, the fog computing system 1000 according to an embodiment of the present invention is common without any additional work, even if a new IoT device node 20 is added or a framework is added to the monitoring, analysis, and control platform 100. The API can be used, which ensures excellent scalability.

Such a method of implementing a fog computing system may be embodied in the form of program instructions that may be executed by various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program instructions, data files, data structures, etc. alone or in combination.

The program instructions recorded on the computer-readable recording medium are those specially designed and configured for the present invention, and may be known and available to those skilled in the computer software arts.

Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical recording media such as CD-ROMs, DVDs, and magneto-optical media such as floptical disks. media), and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like.

Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform the process according to the invention, and vice versa.

Although embodiments of the present invention have been described above with reference to the accompanying drawings, those skilled in the art to which the present invention pertains may implement the present invention in other specific forms without changing the technical spirit or essential features thereof. I can understand that. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive.

Monitoring, Analyzing, and Controlling as a Service (MACaaS) platform that monitors, analyzes, and controls various IoT devices according to the present invention can easily expand new services according to additional needs of IoT devices or users, and It can provide a consistent interface to integrate IoT devices. In addition, when new IoT devices or frameworks are added to the MACaaS platform, common APIs can be reused without additional work for new IoT devices or frameworks. This can provide superior performance and scalability without knowing the details of the internal structure of the MACaaS platform.

Therefore, if the fog computing deployment such as smart home, smart office, smart factory, smart medical care, smart grid, etc. can be applied to all smart spaces, the platform according to the present invention can provide services with agility and innovation. In addition, it is expected to improve the competitiveness of the SW industry in the 4th industrial base as well as securing related source technologies.

1000: fog computing system
10: fog node
11: fog server
12: fog storage
20: IoT Device Node
21: high-end IoT devices
22: Low Spec IoT Device
23: edge gateway

Claims (12)

A monitoring framework connected to an IoT device node to receive data generated or acquired by the IoT device node;
An analysis framework for analyzing data generated or acquired by the IoT device node to generate new knowledge to be applied to the IoT device node; And
A control framework for generating a control signal for controlling the IoT device node based on the knowledge generated by the analysis framework and transmitting the control signal to the IoT device node;
A monitoring, analysis and control platform, wherein a common API is used between the IoT device node and the monitoring framework or the control framework, and between the monitoring framework, the analysis framework, and the control framework. The method of claim 1,
The monitoring framework,
A device connector for controlling access of the IoT device node;
A data manager configured to receive data generated or acquired by the IoT device node from the IoT device node, and classify and store the received data according to characteristics; And
A monitoring, analysis and control platform comprising a device monitor for monitoring the status of the IoT device node. The method of claim 2,
The data manager,
A monitoring, analysis, and control platform for classifying and storing data generated or acquired by the IoT device node into user generated data, system generated data, and environmental data. The method of claim 2,
The device monitor,
A monitoring, analysis, and control platform for monitoring processes, infrastructure networks, and I / O conditions running on the IoT device nodes. The method of claim 1,
The analysis framework,
A change tracker for tracking changes in data generated or acquired by the IoT device node;
A feature extractor for extracting features from change information tracked by the change tracker; And
And an integrated analyzer for generating new knowledge to be applied to the IoT device according to the features extracted by the feature extractor. The method of claim 1,
The control framework,
A trigger updater for updating an event trigger of the IoT device node based on knowledge generated by the analysis framework;
A module updater that performs a software module update of the IoT device node; And
And a device controller for sending an event trigger updated by the trigger updater to the IoT device node. The method of claim 6,
The device controller,
Receive defect information of the IoT device node according to the monitoring result of the IoT device node through the monitoring framework, identify the cause of the defect of the IoT device node and notify the platform administrator according to the result, or the software module Monitoring, analysis and control platform to check for updates. delete A fog server equipped with a monitoring, analysis and control platform according to any one of claims 1 to 7. An IoT device node connecting an IoT device to an external server for generating or obtaining various data; And
Connected to the IoT device node, receives data generated or acquired by the IoT device, classifies and stores data generated or acquired by the IoT device by characteristics, and analyzes data generated or acquired by the IoT device; And a fog node generating a control signal for controlling the IoT device and transmitting the generated control signal to the IoT device node according to an analysis result.
The IoT device node,
And an edge gateway connected to the IoT device to provide an interface for connecting an external server of the IoT device. The method of claim 10,
The fog node,
A fog server connected to the IoT device node to receive data generated or acquired by the IoT device, and classify data generated or acquired by the IoT device into user generated data, system generated data, and environmental data; And
A fog computing system comprising fog storage providing storage space classified into user generated data, system generated data, and environmental data. delete

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