TWI880091B - Server and method for managing internet of things - Google Patents

Abstract

A server and a method for managing Internet of Things (IoT) are provided. The method includes: receiving current data from the IoT; determining whether the IoT is in a abnormal state according to the current data; setting a cycle time to a first time interval in response to determining the IoT being in the abnormal state; and generating a data report according to the current data and outputting the data report according to the cycle time.

Description

Server and method for managing Internet of Things

The present invention relates to a server and method for managing the Internet of Things (IoT).

Currently, the Internet of Things is gradually opening up services mainly based on machine type communication (MTC) and other object-to-object (M2M) applications. In some application scenarios (such as car computers, parking lot management or electric meters, etc.), the Internet of Things may need to provide services for a large number of IoT devices. In response to the connection management needs of a large number of IoT devices, the connection management platform of the IoT plays the role of first-line customer service. In addition to providing real-time device information, connection information or traffic, the connection management platform also provides fault query services to provide users or IoT devices with real-time information such as real-time connection status or traffic. When faced with managed device failures or malicious attacks, there is often a surge in the amount of device status data. Since the capacity of the connection management platform to process data is fixed, when the amount of data exceeds the range that the connection management platform can handle, it may cause the connection management platform's response time to become longer or service delays to occur, thereby affecting the service quality of the Internet of Things.

The present invention provides a server and method for managing the Internet of Things, which can prevent a large amount of data generated by abnormal devices from having a negative impact on the computing capacity of the server.

The present invention provides a server for managing the Internet of Things, comprising a processor, a storage medium and a transceiver. The storage medium stores a plurality of modules. The processor is coupled to the storage medium and the transceiver, and accesses and executes a plurality of modules, wherein the plurality of modules comprises a buffer, an abnormality detection module, a data processing module and a data output module. The buffer receives current data from the Internet of Things through the transceiver. The abnormality detection module determines whether the Internet of Things is in an abnormal state according to the current data. The data processing module generates a data report according to the current data. The data output module outputs a data report through a transceiver according to a period, wherein the data processing module sets the period to a first time interval in response to the Internet of Things being in an abnormal state.

In one embodiment of the present invention, the above-mentioned current data includes first current data corresponding to a first IoT device, wherein the buffer receives historical data from the IoT via a transceiver, and wherein the abnormality detection module calculates a standard score of the first IoT device based on the first current data and the historical data.

In one embodiment of the present invention, the above-mentioned standard score is related to the number of disconnections of the first IoT device during a preset time period.

In one embodiment of the present invention, the above-mentioned abnormality detection module adds the first Internet of Things device to the abnormal device list stored in the storage medium in response to the absolute value of the standard score being greater than the first threshold.

In one embodiment of the present invention, the above-mentioned current data is associated with multiple IoT devices including a first IoT device, and the abnormal device list includes at least one abnormal IoT device, wherein the abnormality detection module determines that the IoT is in an abnormal state in response to the ratio of at least one abnormal IoT device to multiple IoT devices being greater than a second threshold.

In one embodiment of the present invention, the above-mentioned abnormality detection module responds to the ratio being less than or equal to the second threshold and determines that the Internet of Things is in an abnormal state, wherein the data processing module responds to the Internet of Things being in an abnormal state and sets the period to a second time interval, wherein the second time interval is less than the first time interval.

In an embodiment of the present invention, the above-mentioned abnormality detection module responds that the absolute value is less than or equal to the first threshold and the abnormal device list includes the first IoT device, and deletes the first IoT device from the abnormal device list.

In one embodiment of the present invention, the data processing module performs Gaussian filtering on the historical data to generate a distribution model, wherein the anomaly detection module calculates a standard score based on the distribution model.

The present invention provides a method for managing an Internet of Things, comprising: receiving current data from the Internet of Things; determining whether the Internet of Things is in an abnormal state based on the current data; setting a period to a first time interval in response to determining that the Internet of Things is in an abnormal state; and generating a data report based on the current data, and outputting the data report based on the period.

Based on the above, the server of the present invention can establish a distribution model of IoT devices according to the number of disconnections of IoT devices, and use the distribution model to determine whether an IoT device is abnormal, thereby establishing an abnormal device list. The server can determine whether the IoT is in an abnormal state according to the abnormal device list, and adjust the cycle of reporting data reports according to the judgment result. In this way, it is possible to avoid the large amount of abnormal data generated by the IoT from increasing the load of the IoT connection management platform, thereby improving the performance of the connection management platform.

In order to make the content of the present invention more clearly understood, the following embodiments are specifically cited as examples by which the present invention can be truly implemented. In addition, wherever possible, elements/components/steps with the same reference numerals in the drawings and embodiments represent the same or similar components.

FIG1 is a schematic diagram of data transmitted in the Internet of Things 10 collected by a server 100 according to an embodiment of the present invention. The server 100 can be communicatively connected to the Internet of Things 10 and the database 20, and can collect data from the Internet of Things 10 (or from a remote authentication dial in user service (RADIUS) server in the Internet of Things 10). The server 100 can store the data collected from the Internet of Things 10 in the database 20. When the connection management platform responsible for managing the Internet of Things 10 wants to obtain information of the Internet of Things 10, the connection management platform can access the database 20 to obtain the information of the Internet of Things 10. However, if an abnormality occurs in the IoT 10 and a large amount of data is generated, the data stored in the database 20 may increase significantly, thereby increasing the load of the connection management platform. To avoid the above situation, the server 100 can reduce the amount of data transmitted to the database 20 when an abnormality is detected in the IoT 10.

FIG2 is a schematic diagram of a server 100 for managing the Internet of Things 10 according to an embodiment of the present invention. The server 100 may include a processor 110, a storage medium 120, and a transceiver 130. The processor 110 is, for example, a central processing unit (CPU), or other programmable general-purpose or special-purpose micro control unit (MCU), microprocessor, digital signal processor (DSP), programmable controller, application specific integrated circuit (ASIC), graphics processing unit (GPU), image signal processor (ISP), image processing unit (IPU), arithmetic logic unit (ALU), complex programmable logic device (CPLD), field programmable gate array (FPGA), or other similar components or combinations of the above components. The processor 110 may be coupled to the storage medium 120 and the transceiver 130 , and access and execute a plurality of modules and various applications stored in the storage medium 120 .

The storage medium 120 is, for example, any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk drive (HDD), solid state drive (SSD) or similar components or a combination of the above components, and is used to store multiple modules or various applications that can be executed by the processor 110. In this embodiment, the storage medium 120 can store multiple modules including a data processing module 121, an abnormality detection module 122, a data output module 123, and a buffer 124, and their functions will be described later. In one embodiment, the buffer 124 may include a message queue (MQ) database.

The transceiver 130 transmits and receives signals wirelessly or wiredly. The transceiver 130 may also perform operations such as low noise amplification, impedance matching, frequency mixing, up or down frequency conversion, filtering, amplification, and the like.

FIG3 is a flow chart of establishing a distribution model for IoT devices according to an embodiment of the present invention. The process of FIG3 can be implemented by the server 100 shown in FIG1. In step S301, the buffer 124 can receive historical data from the IoT 10 through the transceiver 130 and store the historical data, wherein the historical data can include N connection records corresponding to N IoT devices (N is a positive integer), wherein the connection record can record disconnection events occurring in the IoT devices. In one embodiment, the RADIUS server in the IoT 10 can record the disconnection event corresponding to the specific IoT device when the specific IoT device is not detected, thereby generating a connection record of the specific IoT device.

The buffer 124 may receive a comma-separated values (CSV) file from the RADIUS server based on a secure file transfer protocol (SFTP), and obtain the historical data from the CSV file.

In step S302, the data processing module 121 may perform Gaussian filtering on the historical data to generate a distribution model. Specifically, the data processing module 121 may count the number of disconnections of the IoT device during a preset time period (e.g., 1 hour) for each IoT device based on the historical data. Since the historical data is associated with N IoT devices, the data processing module 121 may count N disconnection times based on the historical data. After obtaining the N disconnection times, the abnormality detection module 122 may generate a distribution associated with the disconnection times based on the N disconnection times. Generally speaking, the distribution generated by the N disconnection times may present a normal distribution.

After obtaining the distribution related to the number of disconnections, the anomaly detection module 122 may perform Gaussian filtering on the distribution to filter out outliers outside a standard deviation, thereby generating a distribution model, wherein the distribution model may include a mean value and a standard deviation of the number of disconnections.

FIG4 is a flow chart for adjusting the reporting period of IoT data according to an embodiment of the present invention. The process of FIG4 can be implemented by the server 100 shown in FIG1. In step S401, the buffer 124 can receive current data from the IoT 10 through the transceiver 130 and store the current data, wherein the current data can be associated with multiple IoT devices. Taking the first IoT device among multiple IoT devices as an example, the current data can include the first current data corresponding to the first IoT device. The first current data can record a disconnection event occurring in the first IoT device. The buffer 124 can receive a CSV file from a RADIUS server in the IoT 10 based on SFTP and obtain the current data from the CSV file.

The generation time of the current data may be later than the generation time of the historical data. For example, if the historical data records the disconnection events that occurred in the IoT 10 in the past 90 days, the current data may record the disconnection events that occurred in the IoT 10 in the past day.

In step S402, the anomaly detection module 122 may calculate the standard score (z-score) of the first IoT device based on the first current data. Specifically, the data processing module 121 may count the number of disconnections of the first IoT device during a preset time period (e.g., 1 hour) based on the first current data. After obtaining the number of disconnections of the first IoT device, the anomaly detection module 122 may calculate the standard score of the first IoT device according to equation (1), where is the standard score, is the number of disconnections of the first IoT device during a preset time period, is the mean value of the distribution model, and is the standard deviation of the distribution model. …(1)

In one embodiment, the data processing module 121 can extract the first current data corresponding to the first IoT device from the current data according to the Internet protocol (IP) address or device identification code of the first IoT device. For example, in the first current data, the source device IP address of each data packet can be equal to the IP address of the first IoT device.

In step S403, the abnormality detection module 122 may determine the absolute value of the standard score corresponding to the first IoT device (ie: ) is greater than the first threshold. If the absolute value of the standard score is greater than the first threshold, it means that the first IoT device may be abnormal. Accordingly, the server 100 can execute step S404. If the absolute value of the standard score is less than or equal to the first threshold, it means that the first IoT device should be in a normal state. Accordingly, the server 100 can execute step S407.

In one embodiment, if the distribution calculated in step S302 is a normal distribution, the anomaly detection module 122 may set the first threshold to 2.

In step S404, the abnormality detection module 122 can determine whether the abnormal device list exists. If the abnormality detection module 122 finds the abnormal device list in the storage medium 120, the process proceeds to step S406. If the abnormality detection module 122 does not find the abnormal device list in the storage medium 120, the process proceeds to step S405.

In step S405, the abnormality detection module 122 may create an abnormal device list and store the abnormal device list in the storage medium 120.

In step S406, the abnormality detection module 122 may add the first IoT device to the abnormal device list.

In step S407, the abnormality detection module 122 can determine whether the first IoT device is included in the abnormal device list. If the first IoT device is included in the abnormal device list, the process proceeds to step S408. If the first IoT device is not included in the abnormal device list, the process proceeds to step S409.

Since the abnormality detection module 122 has determined in step S403 that the first IoT device is in a normal state, in step S408, the abnormality detection module 122 may delete the first IoT device from the abnormal device list.

In step S409, the abnormality detection module 122 can determine whether the Internet of Things 10 is in an abnormal state according to the current data. If the Internet of Things 10 is in an abnormal state, the process proceeds to step S410. If the Internet of Things 10 is in a non-abnormal state, the process proceeds to step S411.

Specifically, if the current data is associated with M IoT devices (i.e., there are a total of M IoT devices communicating in the IoT 10), and the abnormal device list includes K IoT devices, the abnormal detection module 122 can calculate the ratio R according to equation (2), where M is a positive integer, and K is a positive integer less than or equal to M. The abnormal detection module 122 can determine that the IoT 10 is in an abnormal state in response to the ratio R being greater than the second threshold, and can determine that the IoT 10 is in a non-abnormal state in response to the ratio R being less than or equal to the second threshold. …(2)

In one embodiment, the abnormality detection module 122 may set the second threshold to 0.5. That is, if more than half of the IoT devices in the IoT 10 are in the abnormal device list, the abnormality detection module 122 may determine that the IoT 10 is in an abnormal state.

In step S410, the data processing module 121 may set the cycle to the first time interval. In step S411, the data processing module 121 may set the cycle to the first time interval, wherein the second time interval is less than the first time interval. The above cycle may be a cycle for the server 100 to transmit the data of the Internet of Things 10 to the database 20. Specifically, the data processing module 121 may generate a data report of the Internet of Things 10 based on the current data stored in the buffer 124, wherein the data report may include relevant information of the traffic in the Internet of Things 10. The data output module 123 may output the data report to the database 20 through the transceiver 130 according to the cycle, so that the connection management platform can use it as a basis for managing the Internet of Things 10.

In other words, if the IoT 10 is in an abnormal state, the data processing module 121 can extend the cycle of reporting data to the database 20. If the IoT 10 is in an abnormal state, the data processing module 121 can shorten the cycle of reporting data to the database 20. In this way, when the IoT 10 causes an increase in traffic due to an abnormality, the server 100 can avoid increasing the load of the connection management platform by shortening the cycle of reporting data to the connection management platform, so that the management function of the connection management platform for the IoT 10 can operate normally.

In one embodiment, the data processing module 121 may clear the current data stored in the buffer 124 in response to the data output module 124 outputting the data report to the database 20.

FIG5 is a flow chart of a method for managing an Internet of Things according to an embodiment of the present invention, wherein the method can be implemented by the server 100 shown in FIG1. In step S501, current data from the Internet of Things is received. In step S502, whether the Internet of Things is in an abnormal state is determined based on the current data. In step S503, in response to determining that the Internet of Things is in an abnormal state, the period is set to a first time interval. In step S504, a data report is generated based on the current data, and the data report is output according to the period.

In summary, the server of the present invention can analyze the connection information of IOT devices and establish a distribution model of IOT devices through automatic learning. The server can determine whether the IOT device is in an abnormal connection state based on the real-time connection data and distribution model of the IOT device. Once the server finds that the IOT device is in an abnormal connection state, the server can automatically adjust the reporting cycle of the data report. Accordingly, the present invention can prevent the computing power of the connection management platform of the Internet of Things from being affected by the abnormal surge in data volume, so as to provide higher quality communication services for users of the Internet of Things.

10: Internet of Things 100: Server 110: Processor 120: Storage Media 121: Data Processing Module 122: Abnormal Detection Module 123: Data Output Module 124: Buffer 130: Transceiver 20: Database S301, S302, S401, S402, S403, S404, S405, S406, S407, S408, S409, S410, S411, S501, S502, S503, S504: Steps

FIG. 1 is a schematic diagram of data transmitted in the Internet of Things collected by a server according to an embodiment of the present invention. FIG. 2 is a schematic diagram of a server for managing the Internet of Things according to an embodiment of the present invention. FIG. 3 is a flowchart of establishing a distribution model for an Internet of Things device according to an embodiment of the present invention. FIG. 4 is a flowchart of adjusting the reporting period of Internet of Things data according to an embodiment of the present invention. FIG. 5 is a flowchart of a method for managing the Internet of Things according to an embodiment of the present invention.

S501, S502, S503, S504: Steps

Claims (5)

A server for managing an Internet of Things, comprising: a transceiver; a storage medium storing a plurality of modules; and a processor coupled to the storage medium and the transceiver, and accessing and executing the plurality of modules, wherein the plurality of modules comprises: a buffer receiving historical data and current data from the Internet of Things through the transceiver, wherein the current data comprises first current data corresponding to a first Internet of Things device; an abnormality detection module calculating a standard score of the first Internet of Things device based on the first current data and the historical data and determining whether the Internet of Things is in an abnormal state; a data processing module generating a data report based on the current data; and A data output module outputs the data report through the transceiver according to a period, wherein the data processing module sets the period to a first time interval in response to the Internet of Things being in the abnormal state, wherein The abnormal detection module adds the first Internet of Things device to the abnormal device list stored in the storage medium in response to the absolute value of the standard score being greater than a first threshold, wherein the current data is associated with a plurality of Internet of Things devices including the first Internet of Things device, and the abnormal device list includes at least one abnormal Internet of Things device, wherein The abnormality detection module judges that the Internet of Things is in the abnormal state in response to the ratio of the at least one abnormal Internet of Things device to the multiple Internet of Things devices being greater than a second threshold, and judges that the Internet of Things is in a non-abnormal state in response to the ratio being less than or equal to the second threshold, wherein The data processing module sets the cycle to a second time interval in response to the Internet of Things being in the non-abnormal state, wherein the second time interval is less than the first time interval. A server as described in claim 1, wherein the standard score is associated with the number of times the first IoT device is disconnected during a preset time period. A server as described in claim 1, wherein the abnormality detection module deletes the first IoT device from the abnormal device list in response to the absolute value being less than or equal to the first threshold and the abnormal device list including the first IoT device. A server as described in claim 1, wherein the data processing module performs Gaussian filtering on the historical data to generate a distribution model, and wherein the anomaly detection module calculates the standard score based on the distribution model. A method for managing an Internet of Things, comprising: Receiving historical data and current data from the Internet of Things, wherein the current data includes first current data corresponding to a first Internet of Things device, wherein the current data is associated with a plurality of Internet of Things devices including the first Internet of Things device; Calculating a standard score of the first Internet of Things device based on the first current data and the historical data and determining whether the Internet of Things is in an abnormal state, comprising: In response to an absolute value of the standard score being greater than a first threshold, adding the first Internet of Things device to an abnormal device list, wherein the abnormal device list includes at least one abnormal Internet of Things device; and In response to the ratio of the at least one abnormal IoT device to the plurality of IoT devices being greater than a second threshold, the IoT is judged to be in the abnormal state, and in response to the ratio being less than or equal to the second threshold, the IoT is judged to be in a non-abnormal state; In response to the judgment that the IoT is in the abnormal state, the period is set to a first time interval; In response to the judgment that the IoT is in the non-abnormal state, the period is set to a second time interval, wherein the second time interval is less than the first time interval; and A data report is generated based on the current data, and the data report is output based on the period.

Images