KR20190056502A - Monitoring, analyzing and controlling platform, fog server equipped with the same and fog computing system including the fog server - Google Patents

Abstract

Disclosed are a monitoring, analyzing, and controlling platform, a fog server mounting the same, and a fog computing system including the same. The monitoring, analyzing, and controlling platform comprises: a monitoring framework connected with an IoT device node to receive data generated or obtained from the IoT device node; an analysis framework analyzing the data generated and obtained from the IoT device node to generate new knowledge to be applied to the IoT device node; and a control framework 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 from the analysis framework.

Description

TECHNICAL FIELD [0001] The present invention relates to a monitoring, analysis and control platform, a fog server equipped with the fog server, and a 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, and more particularly, to a monitoring, analysis and control platform for providing Internet of Things (IoT) And a fog server and a computing system including the fog server.

As the network technology developed, the internet (IoT) service, which analyzes the collected data of objects connected to the Internet and provides the services based on the collected data, has appeared. Since the IoT devices with sensors and actuators are interoperable and applied to various fields, the number of users using the IoT service is greatly increasing.

IoT devices, on the other hand, become increasingly smart, self-aware of the IoT environment, and perform certain tasks by receiving commands from remote servers. These IoT devices can collect and generate various types of data in large quantities. IoT devices can send a large amount of data to the cloud server, and the cloud server can analyze and process the data received from the IoT device. Such cloud computing is attracting attention because it can efficiently manage smart IoT devices.

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

Fog Computing is a high-level platform for computing, storage and networking services between end devices and cloud servers. The fog server can be located between the IoT device and the cloud server. According to Fog Computing, it is possible to connect to a cloud server through a fog server to provide physical proximity that makes available real-time applications, location-based services and mobility support for users. Therefore, fog computing has been applied to smart home, smart office, smart factory and smart grid, and various services are being provided.

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 and provide services near the users. Users can receive resource control service 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 for supporting the orchestration layer of fog computing. The extended framework consists of modules such as policy decision engine, policy resolver, policy repository, and policy enforcer. These modules can perform functions such as data-driven decision making, user authentication and verification, and policy storage.

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

In addition, an online secretarial framework has been proposed that combines online algorithms to find a suitable foggy device neighborhood set. This can minimize delay by forming a fog network around a given fog device. This enables proper allocation of computing resources to remote cloud servers and fog networks.

As such, various studies related to 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, existing studies related to fog computing are concentrated on a framework for solving specific problems occurring in fog computing, and there are few studies related to new service expansion or fog computing solutions capable of integrating various IoT devices.

One aspect of the present invention provides a monitoring, analysis and control platform that performs monitoring, analysis and control functions of IoT devices using common APIs.

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

Another aspect of the present invention is to provide a fog node comprising a fog server and a fog storage equipped with a monitoring, analysis and control platform for performing monitoring, analysis and control functions of an IoT device using a common API, A fog computing system.

The monitoring, analysis, and control platform according to one aspect of the present invention includes a monitoring framework that is connected to an IoT device node and receives data generated or obtained at the IoT device node, and analyzes the data generated or obtained at the IoT device node A control framework for generating a 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 knowledge generated in the analysis framework and transmitting the control signal to the IoT device node .

The monitoring framework includes a device connector for controlling access of the IoT device node, a data communication module for receiving data generated or obtained from the IoT device node from the IoT device node, classifying the received data according to characteristics, A data manager and a device monitor for monitoring the status of the IoT device node.

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

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

The analysis framework may further comprise a change tracker for tracking changes in data generated or obtained at the IoT device node, a feature extractor for extracting features from the change information tracked by the change tracker, And an integrated analyzer for generating new knowledge to be applied to the IoT device according to the characteristics.

The control framework may further include a trigger updater for updating an event trigger of the IoT device node based on knowledge generated by the analysis framework, a module updater for performing a software module update of the IoT device node, And a device controller transmitting the updated event trigger to the IoT device node.

In addition, the device controller may 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, , Or can confirm the software module update.

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

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

According to another aspect of the present invention, there is provided a fog computing system including an IoT device node connecting an IoT device and an external server, the IoT device node generating or acquiring various data, and data generated or acquired by the IoT device, Analyzes the data generated or acquired by the IoT device, generates a control signal for controlling the IoT device according to the analysis result, And a fog node for transmitting to the IoT device node.

Meanwhile, the fog node is connected to the IoT device node, receives data generated or acquired by the IoT device, and classifies data generated or acquired by the IoT device into user generated data, system generated data, and environment data And fog storage for providing storage space classified into user generated data, system generated data, and environmental data.

The IoT device node may include an edge gateway connected to the IoT device and providing an interface for connection to an external server of the IoT device.

According to one aspect of the present invention, it is possible to construct a consistent interface that facilitates expansion of a new service or integration of various IoT devices, and ensures 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.
Figures 3 and 4 are algorithms for implementing the operation of the monitoring, analysis and control platform shown in Figure 2.
FIG. 5 is a view showing 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.

BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention and the manner of achieving them will become apparent with reference to the embodiments described in detail below with reference to the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Is provided to fully convey the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

The terminology used herein is for the purpose of illustrating embodiments and is not intended to be limiting of the present invention. In the present specification, the singular form includes plural forms unless otherwise specified in the specification. It is noted that the terms " comprises " and / or " comprising ", as used herein, do not exclude the presence or addition of one or more other elements, steps and operations.

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 exemplary embodiment of the present invention may include a fog node 10 and an IoT device node 20 region.

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. The fog storage 12 may store data processed by the fog server 11.

The IoT device node 20 is an area for connecting an external server of the IoT device and can be divided into a region constructed by the high-growth IoT device 21 and a region constructed by the low-level IoT device 22. [ The high-output IoT device 21 has high computing power and wide network bandwidth, and can be connected to an external network by itself. However, in the case of the low-level IoT device 22, it is composed of a simple sensor and an actuator, and has a poor computing power and a narrow network bandwidth. Accordingly, the IoT device node 20 may include an edge gateway 23 that enables the external IoT device 22 to connect to the external network. The edge gateway 23 may be connected to the low-level IoT device 22 to provide an internal interface that enables connection of the low-level IoT device 22 to the external network.

The fog computing system 1000 according to an embodiment of the present invention can implement the 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 exemplary embodiment of the present invention may be implemented in an IoT service by the monitoring, analysis, and control platform 100 that builds the fog server 11, And an interface that facilitates the integration of various IoT devices. This will be described in detail with reference to FIG.

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 construct the 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 can be used between the monitoring, analysis and control platform 100 and the IoT device node 20. [ Further, the F2F API can be used between the frameworks included in the monitoring, analysis and control platform 100. [ Also, the D2D API can be used between the IoT device nodes 20. [ 2, the MaaS API, the AaaS API, and the CaaS API can be used between the respective modules of the monitoring framework 110, the analysis framework 120, and the control framework 130, respectively. The API used in the monitoring, analysis and control platform 100 is summarized in Table 1 below.

At this time, the D2F API, the F2F API, the D2D API, the MaaS API, the AaaS API, and the CaaS API are all common APIs. Accordingly, the monitoring, analysis, and control platform 100 can use the 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 coupled to the IoT device node 20 to receive data generated or obtained at the IoT device node 20. [ The monitoring framework 110 may be composed of three modules: a device connector 111, a data manager 112, and a device monitor 113.

The device connector 111 can control the access of the IoT device node 20 to the fog server 11. [ The device connector 111 can 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 obtained 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 the characteristics and store the classified data in the fog storage 12. The data manager 112 may classify and store data generated or obtained by the IoT device node 20 into user-generated data, system-generated data, and environment data. Here, the user-created data may be personal data generated by the user through the IoT device, for example, a photograph, a moving image, a schedule, or 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 environmental data related to the fog server 11 and the IoT device, and may include temperature, humidity, precipitation, and the like. Environmental data can be obtained directly from the IoT device or from a server such as National Weather Server or Met Office.

The device monitor 113 may periodically monitor the status 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 running on the IoT device node 20, the infrastructure network, and the I / O status.

The analysis framework 120 may analyze data generated or obtained at the IoT device node 20 to generate new knowledge to be applied to 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 integration analyzer 123. At this time, the feature extractor 122 and the integration analyzer 123 may use a learning engine module.

The change tracker 121 may periodically track changes in data generated or obtained at 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 can extract the feature by applying the change information to the learning engine.

The integration 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 integration 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 obtained by the IoT device node 20 and transmits the control signal to the IoT device node 20 . The control framework 130 may include three modules: a trigger updater 131, a module updater 132, and a device controller 133.

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

The module updater 132 may periodically verify the software module update of the IoT device node 20. [ The module updater 132 may periodically check the update module provided by the vendor of the IoT device. The module updater 132 can perform the module update of the IoT device node 20 when the update module provided from the vendor is confirmed. Alternatively, the module updater 132 may update the module 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 can 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 can execute an instruction according to the event variable and the parameter received from the device controller 133. [

Further, the device controller 133 can confirm the cause of the defect when the defect monitor according to the monitoring of the IoT device node 20 in the device monitor 113 occurs. The device controller 133 can verify 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 to a software module provided by the vendor. The device controller 133 may determine 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, I can tell the truth. If the device controller 133 determines that the defect of the IoT device node 20 is caused by the software module provided by the vendor, the device controller 133 updates the software module via the module updater 132 or informs the vendor of the fact .

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

Figures 3 and 4 are algorithms for implementing the operation of the monitoring, analysis and control platform shown in Figure 2.

Referring to FIG. 3, the monitoring framework 110 can confirm the connection process with the IoT device node 20 and the operation process of storing the data received from the IoT device node 20. FIG.

Specifically, the device connector 111 can check the currently connected IoT device node 20 using the checkConnection () API. The device connector 111 may invoke the accessControl () API to authenticate the IoT device node 20. Here, the IoT device node 20 can request a connection with the fog server 11 using the requestConnection () API. When the authentication is completed by the device connector 111, the IoT device node 20 transmits the data to the data manager 112 ). ≪ / RTI >

The data manager 112 may receive data generated or obtained by the IoT device node 20 using the receive () API. When receiving data from the IoT device node 20, the data manager 112 periodically calls the classify () API to classify the received data according to the characteristics. At this time, the data manager 112 may classify data generated or acquired by the IoT device node 20 into user-generated data, system-generated data, and environment data.

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

Specifically, the device monitor 113 can monitor the IoT device node 20 by periodically calling a monitor API to check whether an unexpected fault has occurred in the IoT device node 20. If the device monitor 113 finds a defect in the IoT device node 20, the device monitor 113 can transmit the corresponding defect information to the device controller 133.

Upon receiving the defect information of the IoT device node 20 from the device monitor 113, the device controller 133 calls the checkFault () API to check the cause of the defect. If the device controller 133 determines that the defect of the IoT device node 20 is caused by the internal design or process of the monitoring, analysis and control platform 100, the device controller 133 monitors the defect information, To the manager of the server. The device controller 133 may send the defect information to the module updater 132 if it is determined that the defect of the IoT device node 20 is caused by the software module of the IoT device node 20 provided by the vendor.

The module updater 132 can perform module update of the IoT device node 20 by confirming whether there is an update of the software module of the IoT device node 20. [ However, the module updater 132 may send defect information to the vendor if it is determined that there is no update of the software module.

In addition, the module updater 132 may periodically use the updateModule () API to check whether an update to a portion of the software module of the IoT device node 20 or an update of the software module is provided by the vendor. Here, it is common to stop the operation of the entire process at the time of software update in the IoT device node 20. [ The module updater 132 divides the update module of a large unit into small modules such as an execution state, a hardware configuration, and an operation system, thereby performing a module update that does not affect the current process of the IoT device node 20 .

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

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

Referring to FIG. 5, the data flow between the IoT device node 20 and the Fog node 10 can be confirmed. Here, the fog node 10 includes a fog server 11 and a fog server 11, which are equipped with a monitoring, analysis and control platform 100 including a monitoring framework 110, an analysis framework 120 and a control framework 130, And a storage 12. The IoT device node 20 may be implemented as a high-end IoT device 21 or an edge gateway 23, and the edge gateway 23 may be connected to the low-level IoT device 22. [

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

The data manager 112 may classify the 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 the data stored in the fog storage 12 to extract data change information. The feature extractor 122 may periodically analyze the data change information to extract feature information. The integration analyzer 123 can generate new knowledge by learning the 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 the 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 variables and parameters updated by the trigger updater 131 to the IoT device node 20. [ The module updater 132 may periodically update the module of the IoT device node 20. [

The IoT device node 20 can execute an instruction according to an event variable and a parameter received from the device controller 133. [

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

As described above, the fog computing system 1000 according to the embodiment of the present invention can construct a consistent interface through the fog server 11 on which the monitoring, analysis and control platform 100 is installed. That is, even if a new IoT device node 20 is added or a framework is added to the monitoring, analysis, and control platform 100, the fog computing system 1000 according to an exemplary embodiment of the present invention can share Since the API can be used, excellent scalability can be guaranteed.

Such an implementation method of the fog computing system may be implemented in the form of program instructions that can be executed through various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program commands, data files, data structures, and the like, alone or in combination.

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

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 and DVDs, 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 machine language code such as those generated by a compiler, as well as 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 for performing the processing according to the present invention, and vice versa.

While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, You will understand. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive.

The new service can be easily extended according to the additional requirements of the IoT device or the user through a service (Monitoring, Analyzing, and Controlling a Service (MACaaS)) platform that monitors, analyzes and controls various IoT devices according to the present invention. It can provide a consistent interface to integrate IoT devices. In addition, new IoT devices or frameworks can be added to the MACaaS platform to reuse common APIs without additional work on new IoT devices or frameworks. This provides excellent performance and scalability without knowing the details of the internal structure of the MACaaS platform.

Therefore, if the platform according to the present invention is applied to all smart spaces such as a smart home, a smart office, a smart factory, a smart factory, a smart grid, etc., where the IoT service can be maximized, fog computing can be provided. And it is expected that it will be able to improve the competitiveness of the national SW industry based on the fourth industry along with securing the related technology.

1000: Fog Computing System
10: Fog Node
11: Fog server
12: Fog Storage
20: IoT device node
21: High-performance IoT devices
22: Low-profile IoT device
23: Edge Gateway

Claims (12)

A monitoring framework coupled to the IoT device node to receive data generated or obtained at the IoT device node;
An analysis framework for analyzing data generated or obtained at the IoT device node to generate new knowledge to be applied to the IoT device node; And
And a control framework for generating a control signal for controlling the IoT device node based on knowledge generated by the analysis framework and transmitting the control signal to the IoT device node. The method according to claim 1,
The monitoring framework includes:
A device connector for controlling access of the IoT device node;
A data manager for receiving data generated or obtained by the IoT device node from the IoT device node, and classifying and storing the received data according to characteristics; And
And a device monitor for monitoring the status of the IoT device node. 3. The method of claim 2,
The data manager comprising:
Analysis, and control platform for classifying and storing data generated or obtained by the IoT device node into user-generated data, system-generated data, and environment data. 3. The method of claim 2,
The device monitor comprising:
A monitoring, analysis and control platform for monitoring processes running on the IoT device node, infrastructure network and I / O status. The method according to claim 1,
Wherein the analysis framework comprises:
A change tracker for tracking changes in data generated or obtained at the IoT device node;
A feature extractor for extracting features from the 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 according to claim 1,
The control framework includes:
A trigger updater for updating an event trigger of the IoT device node based on knowledge generated by the analysis framework;
A module updater for performing a software module update of the IoT device node; And
And a device controller that sends an event trigger updated by the trigger updater to the IoT device node. The method according to claim 6,
Wherein the device controller comprises:
Receiving defect information of the IoT device node according to a monitoring result of the IoT device node through the monitoring framework, confirming the cause of the defect of the IoT device node, informing the platform manager according to the result, Monitoring, analysis and control platform to check for updates. The method according to claim 1,
A monitoring, analysis and control platform in which a common API is used between the monitoring framework, the analysis framework and the control framework between the IoT device node and the monitoring framework or the control framework. 9. A fog server equipped with a monitoring, analysis and control platform according to any one of claims 1-8. An IoT device node connecting an IoT device for generating or acquiring various data and an external server; And
Receiving data generated or acquired by the IoT device, and classifying and storing data generated or obtained by the IoT device according to characteristics, and analyzing data generated or acquired by the IoT device And a fog node for generating a control signal for controlling the IoT device according to an analysis result and transmitting the control signal to the IoT device node. 11. The method of claim 10,
The fog node comprises:
A fog server connected to the IoT device node for receiving data generated or obtained by the IoT device and classifying data generated or acquired by the IoT device into user generated data, system generated data, and environment data; And
And fog storage for providing storage space classified into user-created data, system-generated data, and environmental data. 11. The method of claim 10,
The IoT device node comprising:
And an edge gateway connected to the IoT device to provide an interface for connection to an external server of the IoT device.

Images