JP7346853B2 - IoT device trustworthiness calculation device, IoT device trustworthiness calculation system, usage method of IoT device trustworthiness calculation device, IoT device trustworthiness calculation method, program - Google Patents

Description

The present invention relates to an IoT device trustworthiness calculation device, an IoT device trustworthiness calculation system, a method of using the IoT device trustworthiness calculation device, an IoT device trustworthiness calculation method, and a program .

The Internet of Things (IoT), in which all things are connected to the Internet, is attracting attention and is rapidly spreading. Additionally, various security measures are being taken regarding IoT devices. For example, "Security Measures for IoT Devices" (http://www.soumu.go.jp/main_content/000538611.pdf) created by the Information and Communication Network Industry Association (General Incorporated Association) provides information on the leakage, impersonation, and eavesdropping of device information. , cites guidance as a major threat to IoT devices, and explains countermeasures against it. However, these are mainly measures to prevent a malicious third party from intervening via the network, and do not mention countermeasures when there is a problem with the IoT device itself or the owner or administrator of the IoT device. .
Meanwhile, Professor Tsutomu Matsumoto of Yokohama National University and his colleagues say that the current relatively closed IoT architecture will change to an open architecture around 2030 (https://www.nedo.go. jp/content/100867110.pdf).

Open IoT means meshing of data distribution, multi-layering of services, and virtualization among various layers of IoT, regardless of domain or business owner, and moving towards the ultimate IoT that connects multiple stakeholders in a diverse manner. It shows that it is expanding. If open IoT is realized, the data users and providers will not be vertically integrated, and the data users will not be able to fully monitor the owners and managers of the IoT devices on the data provider side, as well as the installation status of the IoT devices. It is assumed that there will be cases where the quality of data transmitted from IoT devices cannot be determined.

FIG. 17 is a diagram showing an example of the current problem.
As shown in Figure 17, for example, an IoT device administrator may maliciously send false data, the IoT device may not be installed correctly, or the IoT device may not be properly calibrated and send incorrect data. , threats may arise from sources other than third parties, such as owners and managers. In particular, if the credibility of the information is damaged later, such as if the administrator commits fraud after the equipment is installed, or if the equipment is not properly maintained and the measured values show abnormal values, data users may It was difficult to grasp the credibility of the data.

Furthermore, management devices and environmental sensing systems that ensure the reliability of environmental information detected by sensor devices have been known. (For example, see Patent Document 1).
In the technology described in Patent Document 1, a sensor device transmits first environmental information and second environmental information to a management device, and the management device transmits first environmental information and second environmental information transmitted by the sensor device. 2 environment information is received. Further, the management device executes control to restrict the use of the first environmental information transmitted from the sensor device when it is determined that the environment around the sensor device has changed based on the second environmental information. .
By the way, although Patent Document 1 states that the administrator checks the environmental sensor (sensor device), the technology described in Patent Document 1 does not allow the administrator of the environmental sensor (sensor device) to check the environmental sensor (sensor device). Whether or not the environmental information detected by the sensor device is reliable is determined without considering whether the person is
Furthermore, in the technology described in Patent Document 1, attributes of the sensor device are taken into account, such as whether the sensor device is equipped with equipment that ensures the reliability of the environmental information detected by the sensor device. Whether or not the environmental information detected by the sensor device is reliable is determined without having to do so.
Therefore, depending on the technique described in Patent Document 1, there is a possibility that it may not be possible to determine with high precision whether or not the environmental information detected by the sensor device is reliable.

Japanese Patent Application Publication No. 2018-160834

In view of the above-mentioned problems, the present invention provides an IoT device credibility calculation device, an IoT device credibility calculation system, and a method of using the IoT device credibility calculation device, which can calculate the credibility of measurement data transmitted from an IoT device with high accuracy. , aims to provide a method and program for calculating trustworthiness of IoT devices .

One aspect of the present invention includes a first acquisition unit that acquires measurement data transmitted over time from an IoT device having a sensor, and acquires non-measurement information that is information other than the measurement data transmitted from the IoT device. the measurement transmitted from the IoT device based on the measurement data acquired by the first acquisition unit and the non-measurement information acquired by the non-measurement information acquisition unit; The present invention is an IoT device trustworthiness calculation device including a trustworthiness calculation unit that calculates trustworthiness of data.

In the IoT device trust calculation device according to one aspect of the present invention, the non-measurement information includes owner information that is information about the owner and/or manager of the IoT device, and attributes that are information about attributes of the IoT device. information, the non-measurement information acquisition section includes a second acquisition section that acquires the owner information, and a third acquisition section that acquires the attribute information, and the credit score calculation section includes the first acquisition section that acquires the owner information. transmitted from the IoT device based on the measurement data acquired by the acquisition unit, the owner information acquired by the second acquisition unit, and the attribute information acquired by the third acquisition unit. The reliability of the measurement data may be calculated.

The IoT device trustworthiness calculation device according to one aspect of the present invention includes a measurement data analysis unit that analyzes the measurement data acquired by the first acquisition unit, and a measurement data analysis unit that analyzes the measurement data acquired by the second acquisition unit; an owner analysis unit that analyzes the owner and/or manager of the IoT device based on the attribute information acquired by the third acquisition unit; and an analysis of the attributes of the IoT device based on the attribute information acquired by the third acquisition unit. further comprising an IoT device attribute analysis section that performs the above-mentioned evaluation based on the analysis result of the measurement data analysis section, the analysis result of the owner analysis section, and the analysis result of the IoT device attribute analysis section. The reliability of the measurement data transmitted from the IoT device may be calculated.

In the IoT device trust calculation device according to one aspect of the present invention, the measured data analysis unit may combine the measured data acquired by the first acquisition unit and past measurement data acquired in the past by the first acquisition unit. At the same time, the measurement data acquired by the first acquisition unit may be compared with data transmitted from an IoT device other than the IoT device.

In the IoT device trust calculation device according to one aspect of the present invention, the measurement data analysis unit may perform an analysis of the presence or absence of traces of human manipulation on the measurement data acquired by the first acquisition unit.

In the IoT device trust calculation device according to one aspect of the present invention, the measured data analysis unit analyzes the measured data by performing data mining processing after graphically representing the characteristics of changes in the measured data. Good too.

In the IoT device trust calculation device according to one aspect of the present invention, the owner analysis unit includes information obtained from declaration contents of the owner and/or manager of the IoT device, past history, and other databases, and Analyzing whether the owner and/or manager of the IoT device is the authorized owner and/or manager of the IoT device based on at least one of the information obtained from the investigation results. Good too.

In the IoT device trust calculation device according to one aspect of the present invention, the owner analysis unit determines whether or not the owner and/or administrator of the IoT device is transmitting information regarding the IoT device. Contents of information regarding the IoT device transmitted in the past by the owner and/or administrator of the IoT device, the management status of other IoT devices owned or managed by the owner and/or administrator of the IoT device, ownership of the IoT device whether or not the person and/or manager is appropriately disposing of the other IoT device; information regarding the credit of the owner and/or manager of the IoT device; An analysis of the owner and/or manager of the IoT device may be performed based on at least one of the information regarding the manager's management system.

In the IoT device trustworthiness calculation device according to one aspect of the present invention, the IoT device attribute analysis unit includes a manufacturer of the IoT device, a manufacturing time of the IoT device, whether calibration and/or maintenance of the IoT device has been performed, The time when the calibration and/or maintenance of the IoT device was performed, the content of the calibration and/or maintenance of the IoT device, the response of the IoT device, whether the IoT device is equipped with a secure chip, the IoT device operational status, authentication information regarding the IoT device, installation location of the IoT device, installation environment and/or measurement environment of the IoT device, difficulty level of measurement of the measurement data by the sensor of the IoT device, depending on the IoT device. An analysis of attributes of the IoT device may be performed based on at least one of a communication path of the transmitted measurement data and a level of encryption in the IoT device.

In the IoT device trust calculation device according to one aspect of the present invention, the IoT device includes a monitoring unit that monitors the power consumption of the IoT device, the temperature of the calculation unit of the IoT device, and/or the opening/closing state of the case of the IoT device. Furthermore, the third acquisition unit acquires information on the power consumption of the IoT device monitored by the monitoring unit, the temperature of the calculation unit of the IoT device, and/or the open/closed state of the case of the IoT device, and The IoT device attribute analysis unit is configured to analyze the characteristics of the IoT device based on the information on the power consumption of the IoT device, the temperature of the calculation unit of the IoT device, and/or the open/closed state of the case of the IoT device acquired by the third acquisition unit. An analysis of the attributes of the equipment may be performed.

In the IoT device trustworthiness calculation device according to one aspect of the present invention, after the trustworthiness calculation unit calculates the trustworthiness of the measurement data transmitted from the IoT device, the trustworthiness calculation unit continuously calculates the reliability of the measurement data transmitted from the IoT device. Based on the results of the analysis performed by the owner analysis unit, the results of the analysis continuously performed by the IoT device attribute analysis unit, The credibility of the measurement data transmitted from the computer may be continuously calculated.

In the IoT device trustworthiness calculation device according to one aspect of the present invention, the trustworthiness calculation unit may calculate a trust score as the trustworthiness of the measurement data transmitted from the IoT device by using artificial intelligence.

One aspect of the present invention is an IoT device trust calculation system comprising an IoT device trust calculation device, the IoT device, and a network, wherein the IoT device trust calculation device is an IoT device that owns, manages, and manufactures the IoT device. The IoT device is owned or managed by a person who has not performed any of the above, and the IoT device transmits the measurement data to the first acquisition unit of the IoT device credibility calculation device and a location other than the IoT device credibility calculation device via the network. This is an IoT device trust calculation system that is configured to automatically make calls.

One aspect of the present invention is a method of using an IoT device trust calculation device, wherein the trust of the measurement data calculated by the trust calculation unit is used for issuing a certificate of the IoT device. This is how to use the calculation device.

One aspect of the present invention is a method of using an IoT device trustworthiness calculation device, wherein the trustworthiness of the measurement data calculated by the trustworthiness calculation unit is based on a rating of the owner and/or manager of the IoT device, and/or a rating of the owner and/or manager of the IoT device. Alternatively, it is a method of using an IoT device trustworthiness calculation device that is used to rate the installation area of the IoT device.

One aspect of the present invention includes a first acquisition step in which a first acquisition unit of the IoT device credibility calculation device acquires measurement data transmitted over time from an IoT device having a sensor; and a non-measurement step of the IoT device credibility calculation device. a non-measurement information acquisition step in which the information acquisition unit acquires non-measurement information that is information other than the measurement data transmitted from the IoT device; and a trust calculation unit of the IoT device trust calculation device in the first acquisition step. an IoT device comprising: a credibility calculation step of calculating credibility of the measurement data transmitted from the IoT device based on the acquired measurement data and the non-measurement information acquired in the non-measurement information acquisition step; This is a credit rating calculation method.

One aspect of the present invention includes a first acquisition step in which a computer acquires measurement data transmitted over time from an IoT device having a sensor, and non-measurement data that is information other than the measurement data transmitted from the IoT device. A non-measurement information acquisition step of acquiring information, a non-measurement information acquisition step that transmits information from the IoT device based on the measurement data acquired in the first acquisition step, and the non-measurement information acquired in the non-measurement information acquisition step. This is a program for executing a reliability calculation step of calculating the reliability of the measured data.

In the IoT data platform according to one aspect of the present invention, the IoT data reception unit transmits the data from the IoT device while concealing information of an IoT data usage unit that uses the IoT data transmitted by the IoT data transmission unit. The IoT data is received from the IoT data ownership unit that owns the transmitted IoT data, and the IoT data transmission unit transmits the information to the IoT data usage unit while keeping the information of the IoT data ownership unit confidential. The IoT data may be transmitted.

According to the present invention, an IoT device trustworthiness calculation device, an IoT device trustworthiness calculation system, a method of using the IoT device trustworthiness calculation device, and an IoT device trustworthiness calculation capable of calculating the trustworthiness of measurement data transmitted from an IoT device with high accuracy. Methods and programs can be provided.
Further, according to the present invention, it is possible to provide an IoT data platform that can improve the convenience of using IoT data, and a method for processing IoT data using the IoT data platform.

1 is a diagram illustrating an example of a schematic configuration of an IoT device trustworthiness calculation device according to a first embodiment; FIG. It is a flow chart for explaining an example of processing performed in the IoT device trustworthiness calculation device of a 1st embodiment. FIG. 2 is a diagram illustrating an example of processing within the IoT device credibility calculation device when the measurement data analysis section shown in FIG. 1 executes data mining processing and the credibility calculation section uses artificial intelligence or the like. It is a figure showing an example of data mining processing performed by a measurement data analysis part. FIG. 2 is a diagram illustrating an example of a neural network that performs data mining processing. FIG. 2 is a diagram illustrating an example of a process executed in a trust calculation unit of the IoT device trust calculation device according to the first embodiment. FIG. 2 is a diagram illustrating an example in which the trust score calculation unit of the IoT device trust calculation device according to the first embodiment calculates a trust score by performing multivariate analysis. 2 is a flowchart illustrating an example of a process in which the trust calculation unit of the IoT device trust calculation device according to the first embodiment calculates a trust score by executing state point calculation. FIG. 3 is a diagram illustrating an example in which the trust score calculation unit of the IoT device trust calculation device according to the first embodiment calculates a trust score by executing state point calculation. FIG. 2 is a diagram showing a first application example of the IoT device trust calculation device according to the first embodiment. FIG. 7 is a diagram illustrating a second application example of the IoT device trustworthiness calculation device of the first embodiment. 12 is a flowchart illustrating an example of a process executed in the IoT device credibility calculation system of the second application example of the IoT device credibility calculation device of the first embodiment. FIG. 7 is a diagram illustrating a third application example of the IoT device trustworthiness calculation device of the first embodiment. FIG. 7 is a diagram illustrating an example of a schematic configuration of an IoT data platform according to a second embodiment. It is a flowchart for explaining an example of processing executed in the IoT data platform of the second embodiment. It is a diagram showing an example of an IoT data distribution system to which the IoT data platform of the second embodiment is applied. FIG. 2 is a diagram illustrating an example of current problems.

<First embodiment>
DESCRIPTION OF EMBODIMENTS Hereinafter, embodiments of an IoT device credibility calculation device, an IoT device credibility calculation system, a method of using the IoT device credibility calculation device, an IoT device credibility calculation method, and a program of the present invention will be described with reference to the accompanying drawings.

FIG. 1 is a diagram illustrating an example of a schematic configuration of an IoT device trust calculation device 1 according to a first embodiment. Specifically, FIG. 1(A) shows an example of a schematic configuration of the IoT device credibility calculation device 1 of the first embodiment, and FIG. 1(B) shows the IoT device credibility calculation device 1 of the first embodiment and the IoT device credibility calculation device 1 of the first embodiment. An example of the relationship with device D is shown.
In the example shown in FIG. 1, the IoT device reliability calculation device 1 calculates the reliability of data measured by the sensor D1 of the IoT device D. The IoT device D includes a sensor D1, a monitoring section D2, a checking section D3, and a storage section D4.
The sensor D1 measures data such as weather data such as water level, ground, temperature, humidity, wind speed, and atmospheric pressure. The monitoring unit D2 monitors, for example, the power consumption of the IoT device D, the temperature of the calculation unit of the IoT device D, the opening/closing state of the case of the IoT device D, and the like. The checking unit D3 checks whether the IoT device D has been modified in terms of software and/or hardware, the history of the IoT device D, the log data of the IoT device D, and the like. The storage unit D4 stores owner information that is information about the owner and/or manager of the IoT device D, and attribute information that is information about the attributes of the IoT device D. Owners and/or managers of IoT device D include not only individuals but also organizations. The attributes of the IoT device D include, for example, the degree of secure capability of the IoT device D (secure level), whether the device (IoT device D) is authenticated, and the like.
In the example shown in FIG. 1, the IoT device D includes a monitoring section D2, a checking section D3, and a storage section D4, but in other examples, the IoT device D includes a monitoring section D2, a checking section D3, and a storage section D4. It is not necessary to include at least one of D4.

In the example shown in FIG. 1, the IoT device trust calculation device 1 includes a first acquisition unit 11, a second acquisition unit 12, a third acquisition unit 13, a measurement data analysis unit 14, an owner analysis unit 15, It includes an IoT device attribute analysis section 16, a credibility calculation section 17, and a storage section 18.
The first acquisition unit 11 acquires measurement data of the sensor D1 transmitted from the IoT device D over time.
The second acquisition unit 12 acquires the above-mentioned owner information from the IoT device D. Specifically, the second acquisition unit 12 acquires the owner information stored in the storage unit D4 of the IoT device D. The second acquisition unit 12 functions as a non-measurement information acquisition unit that acquires non-measurement information that is information other than the measurement data transmitted from the IoT device D.
The second acquisition unit 12 includes, for example, the content of declarations by the owner and/or manager of the IoT device D, past history, information obtained from other databases (not shown), information obtained from survey results, etc. is obtained as the owner information. The second acquisition unit 12 also determines whether or not the owner and/or administrator of the IoT device D has transmitted information regarding the IoT device D, for example, whether the owner and/or administrator of the IoT device D has The content of the information regarding the transmitted IoT device D, the management status of other IoT devices (not shown) owned or managed by the owner and/or administrator of the IoT device D, the owner and/or the administrator of the IoT device D Whether the administrator is appropriately disposing of other IoT devices, information regarding the credit of the owner and/or administrator of IoT device D, and the management system of the owner and/or administrator of IoT device D Obtain information about the owner as owner information.

The third acquisition unit 13 acquires the above-mentioned attribute information from the IoT device D. Specifically, the third acquisition unit 13 acquires the attribute information stored in the storage unit D4 of the IoT device D. The third acquisition unit 13 also functions as a non-measurement information acquisition unit that acquires non-measurement information that is information other than the measurement data transmitted from the IoT device D.
The third acquisition unit 13 includes, for example, the manufacturer of the IoT device D, the manufacturing date of the IoT device D, whether the calibration and/or maintenance of the IoT device D has been performed, and whether the calibration and/or maintenance of the IoT device D has been performed. content of calibration and/or maintenance of IoT device D, response of IoT device, whether IoT device D is equipped with a secure chip (not shown), operating status of IoT device, authentication regarding IoT device D. Information, installation location of IoT device D, installation environment and/or measurement environment of IoT device D, degree of difficulty in measuring data by sensor D1 of IoT device D, communication route of measurement data transmitted by IoT device D, IoT device The level of encryption in D is acquired as attribute information.
In addition, the third acquisition unit 13 collects, for example, the power consumption of the IoT device D monitored by the monitoring unit D2 of the IoT device D, the temperature of the calculation unit of the IoT device D, information on the opening/closing state of the case of the IoT device D, etc. , obtained as attribute information.
In addition, the third acquisition unit 13 also determines, for example, whether the IoT device D checked by the check unit D3 of the IoT device D has been modified in terms of software and/or hardware, the history of the IoT device D, and the history of the IoT device D. Obtain log data etc. as attribute information.
That is, the attribute information acquired by the third acquisition unit 13 includes information obtained by a sensor (detection information) and information that can be obtained without the need to provide a sensor (non-detection information).

In the example shown in FIG. 1, the IoT device trust calculation device 1 includes both the second acquisition unit 12 and the third acquisition unit 13 as non-measurement information acquisition units; The device 1 may include only one of the second acquisition section 12 and the third acquisition section 13 as the non-measurement information acquisition section.
In yet another example, the above-mentioned owner information may be treated as included in the attribute information. In this example, the IoT device trust calculation device 1 includes the third acquisition unit 13 as a non-measurement information acquisition unit (that is, it does not include the second acquisition unit 12), and the third acquisition unit 13 includes: Owner information included in the attribute information is also acquired.

In the example shown in FIG. 1, the measured data analysis unit 14 analyzes the measured data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11. The measurement data analysis unit 14 uses, for example, the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 and the past measurement data (sensor of the IoT device D) acquired in the past by the first acquisition unit 11. A comparison with data measured in the past by D1 is performed. In addition, the measurement data analysis unit 14 also uses, for example, the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11, and data transmitted from an IoT device other than the IoT device D (an IoT device other than the IoT device D). data measured by the equipment's sensors). Furthermore, the measured data analysis unit 14 analyzes the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 to determine whether there are traces of human manipulation. In addition, the measurement data analysis unit 14 may perform data mining processing after graphically representing the characteristics of changes in the measurement data of the sensor D1 of the IoT device D, as will be described later. Perform analysis of measurement data.

The owner analysis unit 15 performs an analysis of the owner and/or manager of the IoT device D based on the owner information of the IoT device D acquired from the IoT device D by the second acquisition unit 12.
The owner analysis unit 15 analyzes the IoT based on, for example, the declaration contents of the owner and/or manager of the IoT device D, past history, information obtained from other databases, information obtained from investigation results, etc. Analyze whether the owner and/or manager of device D is the authorized owner and/or manager of IoT device D.
The owner analysis unit 15 also determines, for example, whether the owner and/or manager of the IoT device D has transmitted information regarding the IoT device D, and whether the owner and/or manager of the IoT device D has The content of the information regarding the transmitted IoT device D, the management status of other IoT devices owned or managed by the owner and/or administrator of the IoT device D, the information provided by the owner and/or administrator of the IoT device D Based on whether the disposal process of IoT devices is being carried out appropriately, information regarding the credit of the owner and/or manager of IoT device D, information regarding the management system of the owner and/or manager of IoT device D, etc. Then, an analysis of the owner and/or manager of IoT device D is performed.

The IoT device attribute analysis unit 16 analyzes the attributes of the IoT device D based on the attribute information (information regarding the attributes of the IoT device D) acquired by the third acquisition unit 13. The IoT device attribute analysis unit 16 determines, for example, the manufacturer of the IoT device D, the manufacturing date of the IoT device D, whether the calibration and/or maintenance of the IoT device D has been performed, and whether the calibration and/or maintenance of the IoT device D has been performed. content of the calibration and/or maintenance of the IoT device D, the response of the IoT device, whether the IoT device D is equipped with a secure chip (not shown), the operating status of the IoT device, and the details regarding the IoT device D. Authentication information, installation location of IoT device D, installation environment and/or measurement environment of IoT device D, level of difficulty in measuring data by sensor D1 of IoT device D, communication route of measurement data transmitted by IoT device D, IoT An analysis of the attributes of IoT device D is performed based on the level of encryption in device D and the like.
In addition, the IoT device attribute analysis unit 16 analyzes information based on, for example, the power consumption of the IoT device D acquired by the third acquisition unit 13, the temperature of the calculation unit of the IoT device D, the opening/closing state of the case of the IoT device D, etc. Then, an analysis of the attributes of IoT device D is performed.
The IoT device attribute analysis unit 16 also determines, for example, whether the IoT device D acquired by the third acquisition unit 13 has been modified in terms of software and/or hardware, the history of the IoT device D, the log of the IoT device D, etc. An analysis of the attributes of IoT device D is performed based on the data and the like.

The credibility calculation unit 17 calculates the measurement data of the sensor D1 transmitted from the IoT device D based on the analysis result of the measurement data analysis unit 14, the analysis result of the owner analysis unit 15, and the analysis result of the IoT device attribute analysis unit 16. Calculate the credit score of In other words, the trust calculation unit 17 uses the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 and the owner information (the owner of the IoT device D and/or The reliability of the measurement data of the sensor D1 transmitted from the IoT device D is calculated based on the attribute information (information regarding the attributes of the IoT device D) acquired by the third acquisition unit 13.
In an example in which the IoT device trust calculation device 1 includes only one of the second acquisition unit 12 and the third acquisition unit 13 as the non-measurement information acquisition unit, the trust calculation unit 17 is the non-measurement information acquisition unit that is acquired by the first acquisition unit 11. The reliability of the measurement data of the sensor D1 transmitted from the IoT device D is calculated based on the measurement data of the sensor D1 of the IoT device D and the non-measurement information acquired by the non-measurement information acquisition unit.

In the example shown in FIG. 1, the reliability calculation unit 17 calculates a numerical value or rank as the reliability of the measurement data of the sensor D1 transmitted from the IoT device D.
Even after calculating the credibility of the measurement data of the sensor D1 transmitted from the IoT device D, the credibility calculation unit 17 uses the results of the analysis continuously performed by the measurement data analysis unit 14 and the owner analysis unit 15. The reliability of the measurement data of the sensor D1 transmitted from the IoT device D is continuously evaluated based on the results of the continuously executed analysis and the results of the analysis continuously executed by the IoT device attribute analysis unit 16. calculate.
In the example shown in FIG. 1, the monitoring and/or checking of the IoT device D has been explained as part of the function of the IoT device D, but this is not limited to the monitoring and/or checking of the IoT device D by other devices. The check may be performed and the information may be input into the IoT device trust calculation device.
In addition, although the owner information of IoT device D and the attribute data of the IoT device are directly acquired from the IoT device, this is not the case. Separately stored data may also be acquired from the second acquisition unit and the third acquisition unit. good.

In the example shown in FIG. 1, the credibility calculation unit 17 uses artificial intelligence to calculate a credibility score as the credibility of the measurement data of the sensor D1 transmitted from the IoT device D.
In another example, the credibility calculation unit 17 may calculate a credibility score as the credibility of the measurement data of the sensor D1 transmitted from the IoT device D by performing multivariate analysis to be described later.
In still another example, the trustworthiness calculation unit 17 may calculate a trust score as the trustworthiness of the measurement data of the sensor D1 transmitted from the IoT device D by executing state point calculation to be described later.

In the example shown in FIG. 1, the storage unit 18 stores measurement data acquired by the first acquisition unit 11, owner information acquired by the second acquisition unit 12, and attribute information acquired by the third acquisition unit 13. , the analysis result of the measurement data analysis unit 14 , the analysis result of the owner analysis unit 15 , the analysis result of the IoT device attribute analysis unit 16 , and the measurement data of the sensor D1 of the IoT device D calculated by the trustworthiness calculation unit 17 Memorize the credit score.

FIG. 2 is a flowchart for explaining an example of a process executed in the IoT device trust calculation device 1 of the first embodiment.
In the example shown in FIG. 2, in step S11, the first acquisition unit 11 acquires measurement data of the sensor D1 transmitted from the IoT device D over time.
Further, in step S12, the second acquisition unit 12 acquires owner information, which is information about the owner and/or administrator of the IoT device D, from the IoT device D.
Further, in step S13, the third acquisition unit 13 acquires attribute information, which is information regarding the attributes of the IoT device D, from the IoT device D.

Next, in step S14, the measured data analysis unit 14 analyzes the measured data of the sensor D1 of the IoT device D acquired in step S11.
In addition, in step S15, the owner analysis unit 15 determines the ownership of the IoT device D based on the owner information of the IoT device D (information on the owner and/or manager of the IoT device D) acquired in step S12. Perform user and/or administrator analysis.
Further, in step S16, the IoT device attribute analysis unit 16 executes an analysis of the attributes of the IoT device D based on the attribute information (information regarding the attributes of the IoT device D) acquired in step S13.

Next, in step S17, the credibility calculation unit 17 calculates the measurement data of the sensor D1 transmitted from the IoT device D based on the results of the analysis in step S14, the results of the analysis in step S15, and the results of the analysis in step S16. Calculate credit score.
That is, in step S17, the credibility calculation unit 17 calculates the measurement data of the sensor D1 of the IoT device D obtained in step S11, the owner information obtained in step S12, and the attribute information obtained in step S13. Based on this, the reliability of the measurement data of the sensor D1 transmitted from the IoT device D is calculated.
Note that steps S11 to S17 do not need to be performed in this order, and may be replaced as appropriate.

As described above, in the IoT device reliability calculation device 1 of the first embodiment, when calculating the reliability of the measurement data of the sensor D1 transmitted from the IoT device D, the measurement data of the sensor D1 of the IoT device D is taken into consideration. In addition, information about the owner and/or manager of the IoT device D and information about the attributes of the IoT device D are also taken into consideration.
Therefore, in the IoT device trust calculation device 1 of the first embodiment, the sensor D1 transmitted from the IoT device D is It is possible to calculate the reliability of the measurement data of the sensor D1 transmitted from the IoT device D with higher accuracy than when the reliability of the measurement data of the IoT device D is calculated.
Specifically, in the technology described in Patent Document 1, since the behavior of the first sensor is stopped by the second sensor, the information of the first sensor cannot be used, whereas the IoT of the first embodiment The device reliability calculation device 1 can appropriately determine the use of data according to the reliability of the measurement data of the sensor D1 calculated by the reliability calculation unit 17.

Furthermore, in the IoT device reliability calculation device 1 of the first embodiment, when calculating the reliability of the measurement data of the sensor D1 transmitted from the IoT device D, an analysis of the measurement data of the sensor D1 of the IoT device D is performed. In addition, an analysis of the owner and/or manager of the IoT device D and an analysis of the attributes of the IoT device D are also performed.
Therefore, in the IoT device trust calculation device 1 of the first embodiment, the analysis of the owner and/or administrator of the IoT device D and the analysis of the attributes of the IoT device D are not performed, and the data is transmitted from the IoT device D. The reliability of the measurement data of the sensor D1 transmitted from the IoT device D can be calculated with higher accuracy than when the reliability of the measurement data of the sensor D1 transmitted from the IoT device D is calculated.

In addition, in the IoT device reliability calculation device 1 of the first embodiment, even after the reliability calculation unit 17 calculates the reliability of the measurement data of the sensor D1 transmitted from the IoT device D, the measurement data analysis unit 14 continuously IoT device D The reliability of the measurement data of the sensor D1 transmitted from the sensor D1 is continuously calculated.
Therefore, in the IoT device trust calculation device 1 of the first embodiment, if any of the analysis results of the measurement data analysis section 14, the owner analysis section 15, and the IoT device attribute analysis section 16 change; Even if there is, the reliability of the measurement data of the sensor D1 transmitted from the IoT device D can be calculated by reflecting the change.

Next, points to be considered when applying the IoT device trust calculation device 1 of the first embodiment described above will be explained.
In order for the user of the measurement data of the sensor D1 of the IoT device D to determine the degree of trust in the measurement data of the sensor D1 transmitted from the IoT device D, it is necessary to use a method other than an attack by a malicious third party via a network etc. (1) Confirmation of whether IoT device D itself is trustworthy, (2) Confirmation of whether the person who manages or owns IoT device D is trustworthy, and (3) Confirmation that IoT device D is correct. It is necessary to confirm and judge whether or not it is installed.
For these matters, it is not enough to check the degree of trust of IoT device D once. For example, regarding (1) above, the degree of trust in IoT device D may change due to deterioration of IoT device D, inadequate maintenance of IoT device D, or the like. There is also a possibility that the degree of trust in IoT device D may change due to modification of IoT device D. Regarding (2) above, even if there are no problems at first, the administrator of IoT device D may have a defective management system or the administrator of IoT device D may succumb to financial temptations, and the It cannot be denied that false information may be transmitted from IoT device D for various reasons, such as malicious guidance to data users. Regarding (3) above, even if the sensor D1 itself of the IoT device D is normal, changes in the installation environment of the sensor D1 (for example, if the sensor D1 is a temperature sensor, a situation where the sensor D1 is not exposed to direct sunlight) It is also conceivable that the IoT device D may transmit measurement data of the sensor D1 that is not correct due to a change in the situation (such as a situation where the sensor D1 is exposed to direct sunlight). Therefore, it is desirable to repeatedly confirm the degree of trust.

FIG. 3 is a diagram illustrating an example of processing within the IoT device credibility calculation device 1 when the measurement data analysis unit 14 shown in FIG. 1 executes data mining processing and the credibility calculation unit 17 uses artificial intelligence or the like.
In the example shown in FIG. 3, "IoT device data" (measurement data of sensor D1 transmitted over time from IoT device D) and "owner/manager information" (the owner and/or administrator information) and “device information” (information regarding attributes (attribute information) of IoT device D) are input to the IoT device trust calculation device 1.
“IoT device data” is, for example, time-series data distributed from IoT device D. The "owner/manager information" is, for example, registration information, management system, past data distribution information, etc., and corresponds to positive points described later. The "device information" includes, for example, registration information, the safety level of the communication path, the manufacturer, the date of manufacture, and the status of calibration.
"State analysis by data mining etc." is executed by the measured data analysis section 14 (see FIG. 1). Further, "credit score calculation by AI (artificial intelligence) etc." and "credit score judgment" are executed by the credit score calculation unit 17 (see FIG. 1).

In the example shown in FIG. 3, the reliability of the measurement data of the sensor D1 transmitted from the IoT device D (the analysis result of the measurement data analysis unit 14) and the information of the person or organization that owns and/or manages the IoT device D are calculated. , and information regarding the IoT device D itself, a trust score (degree of trustworthiness) is calculated using AI or the like. The reliability of the measurement data of the sensor D1 transmitted from the IoT device D can be obtained by performing processing such as data mining.

The information on the owner and/or manager of IoT device D is obtained from the declaration contents of the owner and/or manager of IoT device D, past history, other databases, survey results, etc. Are you the legal owner and/or manager of D? Does it transmit information regarding IoT device D that it owns and/or manages? Is the disposal process of IoT device D being carried out appropriately? This includes the content of information transmitted in the past, the management status of other IoT devices owned and/or managed, qualifications, credit, management systems, etc.
In other words, the owner analysis unit 15 (see FIG. 1) analyzes the declaration contents of the owner and/or manager of IoT device D, past history, information obtained from other databases, information obtained from investigation results, etc. Based on this, it is analyzed whether the owner and/or manager of IoT device D is the authorized owner and/or manager of IoT device D.
In addition, the owner analysis unit 15 determines whether the owner and/or administrator of IoT device D has transmitted information regarding IoT device D in the past. The content of information regarding IoT device D, the management status of other IoT devices owned or managed by the owner and/or administrator of IoT device D, the information provided by the owner and/or administrator of IoT device D to other IoT devices Based on information such as whether the disposal process of Perform an analysis of the owner and/or manager of IoT device D.

Information on IoT device D includes the manufacturer of IoT device D, manufacturing date, whether and when IoT device D is calibrated, response of IoT device D, presence or absence of a secure chip, operating status, various authentication information, installation location, and installation. This information includes the environment, the difficulty of the data measured by sensor D1 of IoT device D, the communication route, the level of encryption, etc. This information includes the model of IoT device D, the type of device used, and the data transmitted through the communication line. It can be obtained from reactions etc.
In other words, the IoT device attribute analysis unit 16 (see FIG. 1) determines the manufacturer of the IoT device D, the manufacturing date of the IoT device D, whether the calibration and/or maintenance of the IoT device D has been performed, the calibration and maintenance of the IoT device D, and the like. /Or when maintenance was performed, content of calibration and/or maintenance of IoT device D, response of IoT device, whether IoT device D is equipped with a secure chip, operating status of IoT device, information regarding IoT device D Authentication information, installation location of IoT device D, installation environment and/or measurement environment of IoT device D, difficulty level of measuring data by sensor D1 of IoT device D, communication of measurement data of sensor D1 transmitted by IoT device D An analysis of the attributes of the IoT device D is performed based on the route, the level of encryption in the IoT device D, and the like.

Information on the measurement data of the sensor D1 of the IoT device D includes data including past data transmitted from the target IoT device D, comparison with data transmitted from other IoT devices, and the like.
In other words, the measurement data analysis unit 14 (see FIG. 1) uses the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 (see FIG. 1) and the measurement data of the sensor D1 acquired by the first acquisition unit 11 in the past. Executes comparison with past measurement data.
Furthermore, the measured data analysis unit 14 compares the measured data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 with data transmitted from an IoT device other than the IoT device D.

Note that it is not necessary to use all of this information, and a comprehensive judgment is made based on one or a combination of multiple pieces of information. Decisions can be made by humans, but since the number of IoT devices installed is enormous, it is desirable to make them entirely or partially electronically, and AI such as machine learning is used for electronic decisions. You may. In particular, statistical analysis of past data, history of the owner and/or manager of IoT device D, etc. is important, as well as analysis based on information only at the time of investigation.
In addition, information on the basis can be obtained mainly by using communication systems such as the Internet or other lines, information from IoT device manufacturers, information recorded in other databases, information from the owner of IoT device D, etc. /Or registered information by an administrator, confirmation by an investigator, etc.
Alternatively, part or all of this information may be registered in the IoT device D and referenced through a communication line. In that case, it is desirable to store the information in a securely protected area such as a chip so that it cannot be tampered with, and to refer to it through encrypted communication.
In addition, it is desirable that the writing of this information requires authority according to the content of the information. Further, although the information written to the chip can be added, it cannot be erased, and it is desirable to also store information such as the date, person, organization, etc. when the data was written.
Furthermore, it is also possible to add a function to the IoT device D itself to monitor its own power consumption, or to add a function to self-check whether the IoT device D itself has been modified in terms of software or hardware. Often, that information may also be stored in a secure area and made available for reference.

In analyzing the measurement data of sensor D1 of IoT device D, has the content of the data been rewritten? Has IoT device D been modified? Has there been any change in the measurement environment? It is important to judge the following. Key points in these judgments include the presence or absence of traces of human processing or manipulation, and changes in the power consumption of IoT device D. Although these judgments may be made by humans, in order to support a large number of IoT devices, it is better to make some or all of the judgments by electronic processing. For data analysis and classification, it is desirable to perform electronic processing such as data mining as necessary.
In this way, by processing the time-series output data from the IoT device D, the probability that an abnormality and an abnormal state are occurring is determined.

Regarding people, organizations, devices, etc., the results of interviews and on-site confirmations may also be used as material for making decisions, if necessary. For the IoT device D, which requires a more accurate understanding of the reliability of the measurement data of the sensor D1, on-site interviews and confirmations, where it is difficult to falsify the data, are effective.
On the other hand, interviews and on-site inspections require manpower, so they are not suitable for checking a large number of IoT devices. Therefore, depending on the case, methods such as reducing the frequency of unannounced inspections or conducting remote checks using cameras or the like may be taken.
In this specification, the degree of trust in the measurement data of the sensor D1 and the reliability are used in the same meaning, and the degree to which the measurement data of the sensor D1 transmitted by the target IoT device D is correct information is the probability. It is indicated by a numerical value such as or a rank such as A, B, C, etc., and is not used to detect or judge whether or not the data is correct.
In other words, "high credibility" indicates that there is a low possibility of transmitting erroneous measurement data, and "low credibility" indicates that there is a high possibility of transmitting erroneous measurement data. Just because the reliability is low does not necessarily mean that the measurement data is incorrect. For example, a temperature sensor that has been poorly managed or maintained and has become obsolete due to the passage of time since its installation may end up transmitting correct measurement data, but it is determined that there is a high possibility that it will transmit incorrect measurement data. Becomes “low credit score.” In addition, even if IoT device D is transmitting correct information, the reliability of the data is low, even if the owner has a history of transmitting malicious and false measurement data many times in the past. There is a possibility that it will become.
Judging the degree of overall creditworthiness is sometimes done by experienced people in the early stages, but by having AI or other systems learn from that experience, it is possible to automatically calculate a credit score. It becomes possible. Scoring includes, for example, methods using multivariate analysis, judgment-based processing, and logistic regression, but is not limited thereto, and various algorithms that can be used for scoring can be used.

FIG. 4 is a diagram illustrating an example of a data mining process executed by the measurement data analysis section 14. Specifically, FIG. 4A shows measurement data (time series data) of the sensor D1 of the IoT device D acquired by the first acquisition unit 11. FIG. 4(B) shows a graphic representation of the characteristics of the change in the measurement data shown in FIG. 4(A). FIG. 4(C) shows an example of a method for performing data mining processing on the data shown in FIG. 4(B). FIG. 5 is a diagram showing an example of a neural network that performs data mining processing.

Data mining may be performed directly on the time series data obtained from the IoT device D (measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11) (see FIG. 4(A)). , as shown in FIG. 4(B), if preprocessing can be performed that graphically represents the characteristics of the change, more accurate data mining can be performed. For example, a method may be considered in which a converted figure such as the frequency or chaotic change over time of the measurement data of the sensor D1 is formed and then analyzed by data mining. In addition, possible data mining methods include a method using a neural network, a method using logistic regression, a method using a support vector machine, a method using the K-nearest neighbor method, etc., which are suitable according to the content of the measured data of the sensor D1. It is good to select and combine methods.
In the example shown in FIG. 4, data mining processing is executed after the characteristics of changes in the measurement data of sensor D1 of IoT device D are visualized, so the data mining process is higher than when the characteristics of changes in the measurement data are not visualized. It is possible to perform data mining processing with high precision and a small amount of calculation.

In the example shown in FIG. 5, first, feature extraction is performed by convolution processing, then pixel reduction is performed by pooling in a pooling layer, and then feature classification is performed using a neural network.
As shown in the examples shown in FIGS. 4 and 5, once it is converted into a graphic, it is possible to use a technique such as a general convolutional neural network, which enables classification with high accuracy and a small amount of calculation.
In the example shown in FIG. 5, the output of the classification in the output layer is the probability that normality, measurement target abnormality, sensor state abnormality, installation environment defect, data tampering, device modification, etc. may occur.
Note that the data obtained from the IoT device D may be data obtained by sensing the state of the IoT device D itself, in addition to the measurement data of the sensor D1 obtained by sensing the environment in which the IoT device D is installed. . For example, the power consumption of the IoT device D itself, the temperature of the calculation unit of the IoT device D, the opening/closing status and history of the case of the IoT device D, log data, etc.
Although some examples have been given here, such as preprocessing, data mining, machine learning, and output, the invention is not limited to these, and any electronic method that can estimate the reliability of the measurement data of sensor D1 Any method such as , mechanical or human judgment may be used.

FIG. 6 is a diagram illustrating an example of a process executed in the trustworthiness calculation unit 17 of the IoT device trustworthiness calculation device 1 according to the first embodiment. Specifically, FIG. 6 is a diagram showing an example of the process executed in step S17 of FIG. 2.

In the example shown in FIG. 6, in step S101, parameters related to the reliability of measurement data of sensor D1 of IoT device D (for example, those output from the output layer shown in FIG. 5) are input.
As the data input in step S101, for example, data obtained as the reliability of data using a neural network shown in FIG. 5 or the like is used. The data input in step S101 is classified, for example, by analyzing trends in past data to determine whether the data has been artificially manipulated or whether abnormal values are shown due to a failure of IoT device D, etc. The reliability (credibility) of the data is expressed, for example, as a percentage.
Furthermore, in step S102, attributes of the device (IoT device D) are input. Device attributes include the degree of secure capability (secure level) of the device, whether the device is authenticated, etc. The security level of a device is determined by the degree of trust as a device obtained from the manufacturer and model name of IoT device D, the degree of vulnerability due to device settings, etc., the calibration history of the sensor of this device, and the communication line. It can be determined from the response of the device.

Further, in step S103, information on the owner and/or administrator (person, organization) of the IoT device D is input. The information on the owner and/or manager of IoT device D input in step S103 includes whether the owner and/or manager of IoT device D is authenticated and exists, or whether the owner and/or manager of IoT device D has sent incorrect data in the past. If yes, how often, the degree of trust in the owner and/or administrator of IoT device D (human score), the degree of trust in the management system (management system score), the score of the installation environment of IoT device D, etc. is possible.
The human score indicates score data of a person or organization obtained from past and present information such as income, debt, track record, and behavior. In the case of an organization, this may be calculated based on data such as the organization's rating, or may be calculated from separately scored information on a person.
The management system score can be obtained from the management history, calibration history, etc. performed by a person or organization not only on this device but also on the IoT device D.
Additionally, the installation environment score can be determined by checking the installation location and determining whether the sensor is correctly installed based on the actual environment, images, data, etc.
The order of step S101, step S102, and step S103 does not need to be the order shown in FIG. 6, and it is sufficient that necessary information is input. Furthermore, it is not necessary to use all of the parameter information regarding data quality shown above, and there may be data that does not exist.
Next, in step S104, a trust score that is a measure of the data quality of IoT device D is calculated.

FIG. 7 shows that the trust score calculation unit 17 of the IoT device trust calculation device 1 of the first embodiment calculates a trust score as the trust of the measurement data of the sensor D1 transmitted from the IoT device D by executing multivariate analysis. It is a figure showing an example.
In the example shown in FIG. 7, the credibility calculation unit 17 calculates a credibility score as the credibility of the measurement data of the sensor D1 transmitted from the IoT device D by executing multivariate analysis using a discriminant function.
In detail, we calculated the coefficients of the discriminant function using multivariate analysis to determine whether the data is correct or incorrect for various input parameter information of 41 IoT devices (indicated by symbols A to AP in Figure 7). . Furthermore, based on the results of the discriminant function, we converted the data into a credit score (certainty) so that it would be a positive number for ease of viewing.
In the example shown in FIG. 7, a large credit score indicates that the quality of the data is good. By using the discriminant function obtained in this way, it is possible to obtain the trust score of the data for a new IoT device. Furthermore, accuracy can be further improved by newly updating the coefficients of the discriminant function using the results for the input data.
In the example shown in FIG. 7, a linear discriminant function is used in the multivariate analysis, but in other examples, a quadratic discriminant function or a nonlinear discriminant function may be used. In still other examples, techniques such as artificial intelligence such as neural networks may be used.

FIG. 8 shows the reliability of the measurement data of the sensor D1 transmitted from the IoT device D by the reliability calculation unit 17 of the IoT device reliability calculation device 1 of the first embodiment executing state point calculation (judgment base). It is a flowchart for explaining an example of the process of calculating a score.
FIG. 9 shows the reliability of the measurement data of the sensor D1 transmitted from the IoT device D by the reliability calculation unit 17 of the IoT device reliability calculation device 1 of the first embodiment executing state point calculation (judgment base). It is a figure which shows an example of calculating a score.

In the example shown in FIG. 8, in step S201, the credibility calculation unit 17 calculates status points. Specifically, in step S201, the security strength of the IoT device D to be determined (secure level), the person/organization that manages the IoT device D (person score), the current state of management where the IoT device D is installed ( Management system score) and the state of the installation environment of IoT device D (environmental score) are given points. In the example shown in FIG. 9, the good state of IoT device D corresponds to a high point, and the points are normalized to values from 1 to 100.
Next, in step S202, the credibility calculation unit 17 executes positive point addition. Specifically, in step S202, points are assigned (addition of positive points) for past achievements such as proper management and transmission of normal data. Positive point addition is to add the current points to the previous points, and is to give a high evaluation to the IoT device D that has transmitted normal data for a long period of time. Points given for such past actions are called "cumulative positive points" (see FIG. 9). In the example shown in Figure 9, the human score, management system score, environment score used in the previous state point and the data score obtained by analyzing the measurement data of sensor D1 transmitted by IoT device D are normalized to 1 to 5. there was.
In the example shown in Figure 9, the same scores for people/organizations, management systems, and the environment were used for status points and cumulative positive points, but in other examples, different weights were used, such as changing the weight between the current situation and the past. You can.

Next, in step S203, the credibility calculation unit 17 determines whether there is a negative event (whether or not a negative event has occurred), which will be described later. For example, if there is a negative event (a negative event occurs), such as the IoT device D transmitting incorrect data or a lie in the registered data, the process advances to step S204. On the other hand, if there is no negative event (no negative event has occurred), the process advances to step S205.
In step S204, the credibility calculation unit 17 resets or deducts points, and proceeds to step S205. In step S204, a penalty is given to an object that has caused an accident that greatly impairs reliability by significantly reducing the accumulated positive points. Points to be reset or deducted may be applied only to accumulated positive points, or may be applied to status points and accumulated positive points.
Note that for points and negative events related to people, organizations, and environments, points are calculated in common for all IoT devices included in those people, organizations, and environments.

In step S205, the credit score calculation unit 17 calculates a credit score from the points calculated in steps S201 to S204.
Next, in step S206, the credit score calculation unit 17 executes point updating (storage of the points calculated in steps S201 to S204).
Next, in step S207, the credibility calculation unit 17 determines whether to continue the process shown in FIG. 8. If the process shown in FIG. 8 is to be continued, the process returns to step S202. On the other hand, if the process shown in FIG. 8 is not to be continued, the process shown in FIG. 8 is ended.

In the example shown in FIG. 9, for 14 IoT devices (indicated by symbols A to N in FIG. 9), the security level, human score, and management system of the IoT devices are normalized from 1 to 100 using "status points". The sum of the score and the environment score was calculated.
Next, the "cumulative positive points" were calculated by adding the human score, data score, management system score, and environmental score normalized to 1 to 5 to the previous cumulative positive point value. "Cumulative positive points" are added to past points, and devices that consistently transmit correct data over a long period of time will receive higher points.
Next, as examples of negative events, vulnerabilities are discovered due to misconfiguration or management of a device (IoT device), incorrect data is sent due to malicious intent or human error, and there is a lie in the registered data. In the event of an accident, such as a failure in the management system being discovered, points were reset to zero.
The points obtained in this way were aggregated as "points" and formatted into an easy-to-read "credit score."
Note that the point items, the range of points to be added or subtracted, etc. shown in FIG. 9 are not limited to those shown here, and may be increased or decreased or selected as appropriate in other examples.

The information and input order for calculating the trustworthiness of the IoT device D shown in the example described above are merely examples, and the information is not limited thereto.
Possible fields in which the data user requires reliability of the measurement data of the sensor D1 of the IoT device D are as follows.
Data from public institutions such as local governments, such as weather data such as water level, ground, temperature, humidity, wind speed, and atmospheric pressure, data related to disasters, nature-related projects such as agriculture and fishing, data related to traffic volume, movement of people, etc. Conceivable. This is because these data are difficult to manage as they are installed in nature and are exposed to wind and rain, making them prone to abnormalities due to various causes.
Other examples include measurement data related to product quality at production sites, building construction sites, and the like. Specifically, data related to quality such as weight, size, thickness, hardness, color, image, speed, density, and quantity are to be measured. These are fields where there is a strong need for data to be checked by a third party, as it is necessary to prove that the administrator is not falsifying data.
Also included is data transmitted by businesses in the agriculture, forestry, and fisheries industries. By mutually using these data, it is possible to improve the accuracy of predictions of climate change, pests, and diseases. Specifically, water level, water temperature, air temperature, humidity, wind speed, solar radiation, CO ₂ concentration, pests, diseases, images, growth status, fertilizer concentration, soil, water quality, etc. are assumed.
Other possible uses include measurement data on bacteria and viruses that cause infectious diseases, measurement data on radiation doses and radioactive substances, measurement data on poisonous substances and dangerous substances, and other methods for predicting the distribution of hazardous substances. Specifically, the presence/absence and amount of viruses, the presence/absence and amount of pathogenic bacteria, the presence/absence and amount of poisonous substances, the presence/absence and amount of radioactive substances, the presence/absence and amount of dangerous substances, the amount of radioactivity, the amount of PM2.5, and nitrogen oxidation. Possible factors include the amount of material, oxygen concentration, carbon monoxide concentration, number of infections, and amount of infectious disease vectors.

A large amount of data is required in the field of obtaining and analyzing a large amount of data to make various predictions, but if the reliability of the input data is low, correct analysis will not be possible.
Information from devices related to life and health, such as medical devices used for remote diagnosis, etc., also requires high reliability of data. Specifically, body temperature, pulse rate, blood sugar level, neutral fat, skin color, blood pressure, blood oxygen concentration, amount of exercise, calorie consumption, meal content, voice, blood components, exhaled breath components, urine components, saliva components, Sweat components, mucosal secretion components, etc. are assumed.
Information on energy-related products such as crude oil, gas, and coal, mining-related information such as ores, precious metals, rare metals, and gemstones, and yields of agricultural products, etc., fields where prices in trading markets depend on the environment and have a wide range of fluctuations. There is a risk that data may be manipulated intentionally. Specifically, data such as images that can estimate yield, yield, etc., and data related to the content and quality of target substances are assumed.
Although some fields that require the reliability of data from IoT devices have been listed above, the field is not limited to these as long as it is a field that requires confirmation of the reliability of data from IoT devices.

<Application example>
FIG. 10 is a diagram showing a first application example of the IoT device trust calculation device 1 of the first embodiment. Specifically, FIG. 10 shows an IoT device credibility calculation system S to which the IoT device credibility calculation device 1 of the first embodiment is applied.
In the example shown in FIG. 10, the IoT device trust calculation system S includes an IoT device trust calculation device 1, an IoT device D, and a network NW. The IoT device trust calculation device 1 is owned or managed by a person who does not own, manage, or manufacture the IoT device D. The IoT device D transmits the measurement data of the sensor D1 (see FIG. 1) to the first acquisition unit 11 (see FIG. 1) of the IoT device credibility calculation device 1 and a location other than the IoT device credibility calculation device 1 via the network NW. Configured to automatically make calls.
In the example shown in FIG. 10, the credibility of the measurement data of the sensor D1 of the IoT device D calculated by the credibility calculation unit 17 (see FIG. 1) of the IoT device credibility calculation device 1 is used for rating the IoT device D.

The IoT device credibility calculation device 1 of the IoT rating company checks the credibility of the IoT device D through a network NW such as the Internet, and ranks it according to the obtained degree of credibility. Next, the rating results or results and their basis are provided to the data user side and compensation is obtained. The data user analyzes and utilizes the data based on the trustworthiness of IoT device D obtained from the IoT rating company. Another possibility is a service in which a data user requests a rating agency to check the creditworthiness of the target, and the rating agency reports the results.
The consideration for these services may be money, set sales with data, etc., and these may be used alone or in combination. Additionally, the service itself may be provided free of charge if it is used for public acts or to improve corporate value.
The target to be rated may be the IoT device D itself, or it may be grouped by a person or corporation such as the owner or manager of the IoT device D, or by a location such as the installation location or area. It may also be done for the target. By doing so, data users can obtain more accurate data analysis results by using the reliability of the data to be used as a premise for data analysis.

FIG. 11 is a diagram showing a second application example of the IoT device trust calculation device 1 of the first embodiment. Specifically, FIG. 11 shows an IoT device credibility calculation system S to which the IoT device credibility calculation device 1 of the first embodiment is applied.
In the example shown in FIG. 11, the IoT device credibility calculation system S includes an IoT device credibility calculation device 1, an IoT device D, and a network NW. The IoT device trust calculation device 1 is owned or managed by a person who does not own, manage, or manufacture the IoT device D. The IoT device D transmits the measurement data of the sensor D1 (see FIG. 1) to the first acquisition unit 11 (see FIG. 1) of the IoT device credibility calculation device 1 and a location other than the IoT device credibility calculation device 1 via the network NW. Configured to automatically make calls.
In the example shown in FIG. 11, the credibility of the measurement data of the sensor D1 of the IoT device D calculated by the credibility calculation unit 17 (see FIG. 1) of the IoT device credibility calculation device 1 is Publication).

In the current authentication of IoT devices, the authentication information of the IoT device is registered in the IoT device during the manufacturing or installation stage of the IoT device, and the received data is definitely the data sent from the target IoT device. It is checked whether Although this authentication is effective as a countermeasure against tampering or alteration of data on communication paths, etc., it is not possible to confirm the trustworthiness of the target IoT device or the trustworthiness of the owner or administrator.
In IoT device authentication, which checks the trustworthiness of the target IoT device and the trustworthiness of the owner or administrator, the IoT device is verified as registered based on registration information from the owner or administrator of the IoT device, or both. Is it properly managed? Are the qualifications and qualifications of the administrator appropriate? Is the sensor calibrated properly? Does the measured data seem correct? Are the device settings appropriate? Are the installation methods and locations appropriate? These will be checked and if there are no problems, a certificate will be issued. Guidance will be given if there are any problems. For matters that are difficult to judge based on digital information alone, such as the installation environment and management system, inspectors conduct regular and/or surprise visits. If the installation location of the IoT device can be monitored with a camera or the like, it may be confirmed remotely. From this information, the degree of trust is calculated by the method described above.

FIG. 12 is a flowchart showing an example of a process executed in the IoT device credibility calculation system S of the second application example of the IoT device credibility calculation device 1 of the first embodiment.
In the example shown in FIG. 12, in step S301, the reliability of IoT device D is checked, and it is determined whether it is reliable. If it is not reliable, the process advances to step S302. On the other hand, if it is reliable, the process advances to step S303.
In step S302, guidance and correction are performed, and then the process returns to step S301.
In step S303, a certificate is issued.
Next, in step S304, it is determined whether the certificate is within its expiration date. If the certificate is within the expiration date, the process advances to step S305. On the other hand, if the certificate is not within the expiration date, the process returns to step S301.
In step S305, it is determined whether the IoT device D is normal. If IoT device D is normal, the process returns to step S301. On the other hand, if IoT device D is not normal, the process advances to step S302.

In the examples shown in FIGS. 11 and 12, a time limit is set on the certificate, the time limit is periodically checked, and the certificate is reissued as necessary. Furthermore, the IoT device D to which the certificate has been issued is monitored via a communication line such as the Internet.
If it is determined that there is a high possibility that the reliability of the measurement data is not maintained, guidance and correction will be provided.
In other cases, strong demands for impartiality may require that an employee not engage in consulting activities such as coaching.
In yet another example, the certificate may be revoked if it is determined that there is a high possibility that the reliability of the measurement data is not maintained.

The IoT device, or the individual or group that manages it, or both, which has proven the trustworthiness of the data of the IoT device as described above, will be able to transmit data that has been proven to be trustworthy, and the value of the data will be increased. It becomes possible to increase the
When seeking compensation for these services, consideration may be money, the right to secondary use of data, the use of a specific secure chip, etc., and these may be used alone or in combination.

FIG. 13 is a diagram showing a third application example of the IoT device trust calculation device 1 of the first embodiment. Specifically, FIG. 13 shows an IoT device credibility calculation system S to which the IoT device credibility calculation device 1 of the first embodiment is applied.
In the example shown up to FIG. 12, a data user directly receives data from IoT device D. On the other hand, in the example shown in FIG. 13, a data user receives data stored in a database such as a cloud. Collecting information in the database DB has advantages such as being able to prevent data access from being concentrated on the IoT device D and being able to utilize a cloud security system. Regarding the risk of tampering within the database DB, if blockchain is used as the database DB, it can be proven that the registered data has not been tampered with, and by combining it with the proof before registration, the credibility of the data will be higher. It becomes possible to obtain.

<Second embodiment>
DESCRIPTION OF EMBODIMENTS Hereinafter, embodiments of an IoT data platform of the present invention and a method of processing IoT data using the IoT data platform will be described with reference to the accompanying drawings.

FIG. 14 is a diagram showing an example of a schematic configuration of the IoT data platform P according to the second embodiment.
In the example shown in FIG. 14, the IoT data platform P includes an IoT data reception section P1, a first consideration calculation section P2, an IoT data transmission section P3, a second consideration calculation section P4, and a credibility information reception section P5. We are prepared.
The IoT data receiving unit P1 receives IoT data (for example, measurement data of the sensor D1 of the IoT device D) transmitted from the IoT device D (see FIG. 1). The first consideration calculation unit P2 calculates the first consideration, which is the basis of consideration for the IoT data received by the IoT data reception unit P1. The IoT data transmitter P3 transmits the IoT data received by the IoT data receiver P1. The second consideration calculation unit P4 calculates the second consideration, which is the basis for the consideration for the IoT data transmitted by the IoT data transmission unit P3. The credibility information receiving unit P5 receives information regarding the credibility of the IoT data received by the IoT data receiving unit P1.
Specifically, the first consideration calculation unit P2 calculates the first consideration based on the information regarding the credibility of the IoT data received by the credibility information receiving unit P5. Further, the second consideration calculation unit P4 calculates the second consideration based on the information regarding the credibility of the IoT data received by the credibility information receiving unit P5.

FIG. 15 is a flowchart for explaining an example of processing executed in the IoT data platform P of the second embodiment.
In the example shown in FIG. 15, in step S1, the IoT data receiving unit P1 receives IoT data transmitted from the IoT device D (see FIG. 1).
Next, in step S2, the credibility information receiving unit P5 receives information regarding the credibility of the IoT data received by the IoT data receiving unit P1.
Next, in step S3, the first consideration calculation unit P2 calculates the first consideration, which is the basis of the consideration for the IoT data received in step S1, based on the information regarding the reliability of the IoT data received in step S2. .
Furthermore, in step S4, the IoT data transmitter P3 transmits the IoT data received in step S1.
Next, in step S5, the second consideration calculation unit P4 calculates the second consideration, which is the basis of the consideration for the IoT data transmitted in step S4, based on the information regarding the reliability of the IoT data received in step S2. .

In the IoT data platform P of the second embodiment, the basis of compensation for the transmitted and received IoT data is calculated based on information regarding the trustworthiness of the transmitted and received IoT data, thereby improving the convenience of using IoT data. Can be done.

FIG. 16 is a diagram showing an example of an IoT data distribution system DS to which the IoT data platform P of the second embodiment is applied.
In the example shown in FIG. 16, the IoT data reception unit P1 (see FIG. 14) of the IoT data platform P receives information about the IoT data usage unit U that uses the IoT data transmitted by the IoT data transmission unit P3 (see FIG. 14). The IoT data is received from the IoT data ownership unit W that owned the IoT data transmitted from the IoT device D (see FIG. 1) in a state where the data is kept confidential. Furthermore, the IoT data transmitting unit P3 transmits the IoT data to the IoT data using unit U while keeping the information of the IoT data owning unit W confidential.
Furthermore, the credibility information receiving unit P5 receives information regarding the credibility of IoT data from the rating unit R.

IoT data platform P has a data pricing function. That is, in the IoT data platform P, a usage price is determined in response to a request (demand) from a data user (IoT data usage unit U) according to a data sender (IoT data ownership unit W). Information is exchanged when mutual agreement is reached.
At this time, the reliability of the IoT data can be reflected in the price by referring to and taking into account the information of the rating company (Rating Department R) regarding the reliability of the IoT data. Reliability can also be displayed as a basis for pricing.
Furthermore, the IoT data platform P may execute processing such as intentionally adding a premium or discount when information is traded.

In the IoT data platform P, information such as data owners (IoT data ownership department W) and users (IoT data usage department U) is anonymized, and the distribution of IoT data is managed and operated without using personal information. is desirable.
The rating company (rating department R) can also use the guarantee of anonymity as one of the pieces of information for evaluation and rating.

IoT Data Platform P uses not only blockchain and other technologies but also existing cloud security systems to manage the history of IoT data owners (IoT data ownership W), flow paths, movement, etc. , it can be used as a strong means against data tampering.
By using the IoT data platform P, data users (IoT data usage unit U) can easily search for the IoT data they want to use, making it easier to obtain data that meets their requirements. Furthermore, when obtaining the IoT data that the user wants to use, the user can make a request to the IoT data platform P and receive a service that allows the user to easily use highly reliable information.
By utilizing the IoT data platform P, the data owner (IoT data ownership unit W) can easily find a data user (IoT data usage unit U), making it easier to obtain compensation for the data. Furthermore, it becomes possible to obtain the information (data in high demand) that the data user (IoT data utilization unit U) desires. The provider side (IoT data ownership department W) can freely set the usage range, period, etc. of the distributed data, and the provided value is also guaranteed.
The rating company (rating department R) audits the security, management status, and usage status of IoT data platform P, and performs rating, certification, and correction. User convenience such as real-time performance and searchability can also be used as evaluation information. Furthermore, if the data of the IoT data platform P is managed using blockchain, it can be determined that data falsification is unlikely to occur, and it can be used as one of the information for score rating. If there is a need to ensure fairness, consulting activities such as guidance may not be required.
This IoT data platform P can be used as a platform that handles and distributes not only information from IoT devices (IoT equipment D) but also a wide range of data.

Although the mode for implementing the present invention has been described above using embodiments, the present invention is not limited to these embodiments in any way, and various modifications and substitutions can be made without departing from the gist of the present invention. can be added. The configurations described in each of the embodiments and examples described above may be combined as appropriate.

In addition, all or part of the functions of each part provided in the IoT device trust calculation device 1 and the IoT data platform P in the embodiments described above can be achieved by recording a program for realizing these functions on a computer-readable recording medium. The program recorded on the recording medium may be read into a computer system and executed. Note that the "computer system" herein includes hardware such as an OS and peripheral devices.
Furthermore, the term "computer-readable recording medium" refers to portable media such as flexible disks, magneto-optical disks, ROMs, and CD-ROMs, and storage units such as hard disks built into computer systems. Furthermore, a "computer-readable recording medium" refers to a storage medium that dynamically stores a program for a short period of time, such as a communication line when transmitting a program via a network such as the Internet or a communication line such as a telephone line. It may also include a device that retains a program for a certain period of time, such as a volatile memory inside a computer system that is a server or client. Further, the above-mentioned program may be one for realizing a part of the above-mentioned functions, or may be one that can realize the above-mentioned functions in combination with a program already recorded in the computer system.

(Additional note)
In the IoT device trust calculation device according to one aspect of the present invention, the IoT device determines whether the IoT device has been modified in terms of software and/or hardware, the history of the IoT device, and/or the log data of the IoT device. The third acquisition unit further includes a check unit that checks whether the IoT device checked by the check unit has been modified in terms of software and/or hardware, the history of the IoT device, and/or the third acquisition unit. The IoT device attribute analysis unit acquires log data of the IoT device, and determines whether the IoT device acquired by the third acquisition unit has been modified in terms of software and/or hardware, the history of the IoT device, and /Or an analysis of attributes of the IoT device may be performed based on log data of the IoT device.

In the IoT device trustworthiness calculation device according to one aspect of the present invention, the trustworthiness calculation unit calculates a trust score as the trustworthiness of the measurement data transmitted from the IoT device by executing multivariate analysis or state point calculation. You may.

In the IoT device trustworthiness calculation device according to one aspect of the present invention, the trustworthiness calculation unit may calculate a numerical value or rank as the trustworthiness of the measurement data transmitted from the IoT device.

In the IoT device trust calculation device according to one aspect of the present invention, the IoT device further includes a storage unit that stores the owner information and the attribute information, and the second acquisition unit stores the owner information and the attribute information. The third acquisition unit may acquire the attribute information stored in the storage unit.

In the method for using the IoT device trust calculation device according to one aspect of the present invention, the certificate may be an electronic certificate.

One aspect of the present invention is a method of using an IoT device trustworthiness calculation device, wherein the trustworthiness of the measurement data calculated by the trustworthiness calculation unit is used for rating the IoT device. This is how to use it.

In the IoT data platform according to one aspect of the present invention, the credibility information receiving unit may receive information regarding the credibility of the IoT data from a rating unit.

DESCRIPTION OF SYMBOLS 1... IoT device credibility calculation device, 11... First acquisition part, 12... Second acquisition part, 13... Third acquisition part, 14... Measured data analysis part, 15... Owner analysis part, 16... IoT device attribute analysis part , 17...Credibility calculation unit, 18...Storage unit, S...IoT device credibility calculation system, D...IoT device, D1...Sensor, D2...Monitoring unit, D3...Check unit, D4...Storage unit, NW...Network, DB... Database, P... IoT data platform, P1... IoT data receiving section, P2... first consideration calculating section, P3... IoT data transmitting section, P4... second consideration calculating section, P5... credibility information receiving section, DS... IoT data distribution System, R... Rating Department, W... IoT Data Ownership Department, U... IoT Data Utilization Department

Claims (17)

a first acquisition unit that acquires measurement data transmitted over time from an IoT device having a sensor;
a non-measurement information acquisition unit that obtains non-measurement information that is information other than the measurement data transmitted from the IoT device;
a credibility calculation that calculates the credibility of the measurement data transmitted from the IoT device based on the measurement data acquired by the first acquisition unit and the non-measurement information acquired by the non-measurement information acquisition unit; An IoT device credibility calculation device comprising: The non-measurement information includes owner information that is information about the owner and/or manager of the IoT device, and attribute information that is information about attributes of the IoT device,
The non-measurement information acquisition unit includes:
a second acquisition unit that acquires the owner information;
and a third acquisition unit that acquires the attribute information,
The credit score calculation unit is configured to calculate the amount of information based on the measurement data acquired by the first acquisition unit, the owner information acquired by the second acquisition unit, and the attribute information acquired by the third acquisition unit. calculating the reliability of the measurement data transmitted from the IoT device;
The IoT device trust calculation device according to claim 1. a measurement data analysis unit that analyzes the measurement data acquired by the first acquisition unit;
an owner analysis unit that analyzes the owner and/or manager of the IoT device based on the owner information acquired by the second acquisition unit;
further comprising an IoT device attribute analysis unit that analyzes attributes of the IoT device based on the attribute information acquired by the third acquisition unit,
The credibility calculation unit calculates the credibility of the measurement data transmitted from the IoT device based on the analysis result of the measurement data analysis unit, the analysis result of the owner analysis unit, and the analysis result of the IoT device attribute analysis unit. calculate,
The IoT device trust calculation device according to claim 2. The measurement data analysis section includes:
Comparing the measurement data acquired by the first acquisition unit with past measurement data acquired in the past by the first acquisition unit,
Comparing the measurement data acquired by the first acquisition unit with data transmitted from an IoT device other than the IoT device;
The IoT device trust calculation device according to claim 3. The measurement data analysis section includes:
performing an analysis of the presence or absence of traces of human manipulation on the measurement data acquired by the first acquisition unit;
The IoT device trust calculation device according to claim 4. The measurement data analysis section includes:
analyzing the measurement data by performing data mining processing after graphically representing the characteristics of changes in the measurement data;
The IoT device trust calculation device according to any one of claims 3 to 5. The owner analysis department is
Ownership of the IoT device is determined based on at least one of the declared contents of the owner and/or manager of the IoT device, past history, information obtained from other databases, and information obtained from the investigation results. analyzing whether the person and/or manager is the authorized owner and/or manager of the IoT device;
The IoT device trust calculation device according to any one of claims 3 to 6. The owner analysis department is
Whether or not the owner and/or manager of the IoT device has transmitted information regarding the IoT device; the content of information regarding the IoT device that the owner and/or administrator of the IoT device has transmitted in the past; The management status of other IoT devices owned or managed by the owner and/or manager of the IoT device, and whether the owner and/or manager of the IoT device appropriately disposes of the other IoT device. based on at least one of information regarding the credit of the owner and/or manager of the IoT device, and information regarding the management system of the owner and/or manager of the IoT device. perform an analysis of the owners and/or managers of;
The IoT device trust calculation device according to any one of claims 3 to 7. The IoT device attribute analysis section
the manufacturer of the IoT device, when the IoT device was manufactured, whether calibration and/or maintenance of the IoT device was performed, when calibration and/or maintenance of the IoT device was performed, calibration and maintenance of the IoT device; /or Contents of maintenance, response of the IoT device, whether or not the IoT device is equipped with a secure chip, operating status of the IoT device, authentication information regarding the IoT device, installation location of the IoT device, and the IoT device installation environment and/or measurement environment, the difficulty of measuring the measurement data by the sensor of the IoT device, the communication route of the measurement data transmitted by the IoT device, and the level of encryption in the IoT device. performing an analysis of attributes of the IoT device based on at least one of the following:
The IoT device trust calculation device according to any one of claims 3 to 8. The IoT device further includes a monitoring unit that monitors power consumption of the IoT device, temperature of a calculation unit of the IoT device, and/or opening/closing state of a case of the IoT device,
The third acquisition unit acquires information on the power consumption of the IoT device monitored by the monitoring unit, the temperature of the calculation unit of the IoT device, and/or the open/closed state of the case of the IoT device,
The IoT device attribute analysis section
Analyzing the attributes of the IoT device based on the information on the power consumption of the IoT device, the temperature of the calculation unit of the IoT device, and/or the open/closed state of the case of the IoT device acquired by the third acquisition unit. do,
The IoT device trust calculation device according to claim 9. After the credibility calculation unit calculates the credibility of the measurement data transmitted from the IoT device,
The credit score calculation unit is
Results of analysis continuously performed by the measurement data analysis section, results of analysis continuously performed by the owner analysis section, and results of analysis continuously performed by the IoT device attribute analysis section. Continuously calculating the reliability of the measurement data transmitted from the IoT device based on
The IoT device trust calculation device according to any one of claims 3 to 10. The credit score calculation unit calculates a credit score as a credit score of the measurement data transmitted from the IoT device by using artificial intelligence.
The IoT device trust calculation device according to any one of claims 1 to 11. The IoT device trust calculation device according to any one of claims 1 to 12;
The IoT device;
An IoT device trust calculation system comprising a network,
The IoT device credit rating calculation device is owned or managed by a person who does not own, manage, or manufacture the IoT device,
The IoT device is configured to be able to automatically transmit the measurement data to the first acquisition unit of the IoT device trustworthiness calculation device and a location other than the IoT device trustworthiness calculation device via the network.
IoT device trust calculation system. A method of using the IoT device trust calculation device according to any one of claims 1 to 12, comprising:
The credibility of the measurement data calculated by the credibility calculation unit is used to issue a certificate for the IoT device.
How to use IoT device trust calculation device. A method of using the IoT device trust calculation device according to any one of claims 1 to 12, comprising:
The credibility of the measurement data calculated by the credibility calculation unit is used for rating the owner and/or manager of the IoT device and/or the rating of the installation area of the IoT device.
How to use IoT device trust calculation device. a first acquisition step in which a first acquisition unit of the IoT device trust calculation device acquires measurement data transmitted over time from an IoT device having a sensor;
a non-measurement information acquisition step in which a non-measurement information acquisition unit of the IoT device credibility calculation device acquires non-measurement information that is information other than the measurement data transmitted from the IoT device;
The reliability calculation unit of the IoT device reliability calculation device transmits information transmitted from the IoT device based on the measurement data acquired in the first acquisition step and the non-measurement information acquired in the non-measurement information acquisition step. An IoT device reliability calculation method, comprising: a reliability calculation step of calculating the reliability of the measured data. to the computer,
a first acquisition step of acquiring measurement data transmitted over time from an IoT device having a sensor;
a non-measurement information acquisition step of acquiring non-measurement information that is information other than the measurement data transmitted from the IoT device;
credibility calculation of calculating the credibility of the measurement data transmitted from the IoT device based on the measurement data acquired in the first acquisition step and the non-measurement information acquired in the non-measurement information acquisition step; A program to execute steps and .

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