JP7609193B2 - IoT data platform and method for processing IoT data by the IoT data platform - Google Patents

Description

The present invention relates to an IoT data platform and a method for processing IoT data using the IoT data platform.

The Internet of Things (IoT), in which all things are connected to the Internet, has been drawing attention and spreading rapidly. In addition, various security measures for IoT devices are being implemented. For example, the Communications and Information Network Association of Japan (http://www.soumu.go.jp/main_content/000538611.pdf) lists the main threats to IoT devices as leakage of terminal information, spoofing, eavesdropping, hijacking, and manipulation, and explains countermeasures against them. However, these are mainly measures against malicious third parties intervening through the network, and no measures are listed for cases where there is a problem with the IoT device itself or the owner or administrator of the IoT device.
Meanwhile, Professor Matsumoto Tsutomu of Yokohama National University and his colleagues believe 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 refers to the meshing of data distribution, multi-layering of services, and virtualization across various layers of the IoT, regardless of domain or business owner, moving towards the ultimate IoT in which multiple stakeholders are connected in diverse ways. When open IoT is realized, data users and providers will not be vertically integrated, and it is possible that data users will not be able to fully understand the owners and managers of the data provider's IoT devices, or even the installation status of the IoT devices, and in such cases, it will be impossible to judge the quality of the data sent from the IoT devices.

FIG. 17 is a diagram showing an example of a current problem.
As shown in Fig. 17, for example, there is a possibility that threats may come from third parties other than the owner or manager, such as an IoT device manager maliciously distributing false data, an IoT device not being installed correctly, or an IoT device not being properly calibrated, resulting in erroneous data transmission, etc. In particular, in cases where the credibility of information is damaged later on, such as when an administrator commits fraud after the device is installed, or when the device is not properly maintained and measurement values show abnormal values, it has been difficult for data users 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 (see, for example, Japanese Patent Application Laid-Open No. 2003-233664).
In the technology described in Patent Literature 1, a sensor device transmits first and second environmental information to a management device, and the management device receives the first and second environmental information transmitted by the sensor device. Furthermore, when the management device determines that the environment around the sensor device has changed based on the second environmental information, the management device executes control to restrict use of the first environmental information transmitted from the sensor device.
Incidentally, although Patent Document 1 describes that an administrator will check the environmental sensors (sensor devices), the technology described in Patent Document 1 determines whether or not the environmental information detected by the sensor devices is reliable without taking into consideration the identity of the administrator of the environmental sensors (sensor devices).
Furthermore, in the technology described in Patent Document 1, the reliability of environmental information detected by a sensor device is determined without taking into account attributes of the sensor device, such as whether or not the sensor device is equipped with equipment that ensures the reliability of the environmental information detected by the sensor device.
Therefore, the technology described in Patent Document 1 may not be able to accurately determine whether or not the environmental information detected by the sensor device is reliable.

JP 2018-160834 A

In view of the above-mentioned problems, the present invention aims 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.

One aspect of the present invention includes an IoT data receiving unit that receives IoT data transmitted from an IoT device, a compensation calculation unit that calculates a compensation that is the basis of compensation for the IoT data received by the IoT data receiving unit, an IoT data transmitting unit that transmits the IoT data received by the IoT data receiving unit, and a credit information receiving unit that receives information regarding the creditworthiness of the IoT data received by the IoT data receiving unit, wherein the information regarding the creditworthiness is at least non-measurement information, owner information, attribute information, or a combination thereof, and the owner information is at least the past history of the owner and/or manager, the management status of other IoT devices managed by the owner and/or manager, whether the owner and/or manager properly disposes of other IoT devices, the credit information of the owner and/or manager, the management system of the owner and/or manager, or a combination thereof, and is an IoT data platform that is owner information analyzed based on the declaration content of the owner and/or manager of the IoT device, past history, information obtained from other databases, or information obtained from investigation results .

Another aspect of the present invention is an IoT data platform that includes an IoT data receiving unit that receives IoT data transmitted from an IoT device, a fee calculation unit that calculates a fee that is the basis of compensation for the IoT data received by the IoT data receiving unit, an IoT data transmitting unit that transmits the IoT data received by the IoT data receiving unit, and a credit information receiving unit that receives information regarding the creditworthiness of the IoT data received by the IoT data receiving unit, and displays the basis of compensation when calculating the fee for the IoT data to be transmitted and received, and the information regarding the creditworthiness is at least any one of non-measurement information, owner information, and attribute information, or a combination of these.
Another aspect of the present invention is an IoT data platform capable of acquiring data from a rating unit connected outside the IoT data platform , comprising: an IoT data receiving unit that receives IoT data transmitted from an IoT device; a compensation calculation unit that calculates a compensation that is the basis of compensation for the IoT data received by the IoT data receiving unit; an IoT data transmitting unit that transmits the IoT data received by the IoT data receiving unit; and a creditworthiness information receiving unit that receives information regarding the creditworthiness of the IoT data received by the IoT data receiving unit, wherein the information regarding the creditworthiness consists of at least non-measurement information, owner information, attribute information, or a combination of these, the creditworthiness information receiving unit receives from the rating unit information regarding the creditworthiness of the IoT data corresponding to the result of rating by the external rating company , and the compensation calculation unit calculates the compensation using the information regarding the creditworthiness of the IoT data received from the rating unit by the creditworthiness information receiving unit.

In one aspect of the present invention, in the above-mentioned IoT data platform, the IoT data receiving unit receives the IoT data from an IoT data owning unit that owns the IoT data transmitted from the IoT device while keeping the information of the IoT data using unit that uses the IoT data transmitted by the IoT data transmitting unit confidential, and the IoT data transmitting unit transmits the IoT data to the IoT data using unit while keeping the information of the IoT data owning unit confidential.

According to the present invention, it is possible to provide an IoT device trustworthiness calculation device, an IoT device trustworthiness calculation system, a method for using an IoT device trustworthiness calculation device, an IoT device trustworthiness calculation method, and a program that can calculate with high accuracy the trustworthiness of measurement data transmitted from an IoT device.
Furthermore, according to the present invention, it is possible to provide an IoT data platform capable of improving 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. 4 is a flowchart for explaining an example of processing executed in the IoT device trust calculation device of the first embodiment. 1. FIG. 4 is a diagram showing an example of processing within an IoT device trustworthiness calculation device when the measurement data analysis unit shown in FIG. 1 executes data mining processing and the trustworthiness calculation unit utilizes artificial intelligence or the like. FIG. 11 is a diagram illustrating an example of a data mining process executed by a measurement data analysis unit. FIG. 1 illustrates an example of a neural network that executes a data mining process. FIG. 2 is a diagram illustrating an example of a process executed in a trustworthiness calculation unit of the IoT device trustworthiness calculation device of the first embodiment. 1 is a diagram showing an example in which a credit score is calculated by the credit calculation unit of the IoT device credit calculation device of the first embodiment by performing multivariate analysis. FIG. 11 is a flowchart for explaining an example of a process in which a credit calculation unit of the IoT device credit calculation device of the first embodiment calculates a credit score by executing a state point calculation. 1 is a diagram showing an example in which a credit score is calculated by the credit calculation unit of the IoT device credit calculation device of the first embodiment by executing a state point calculation. FIG. FIG. 2 is a diagram illustrating a first application example of the IoT device trustworthiness calculation device according to the first embodiment. FIG. 11 is a diagram illustrating a second application example of the IoT device trustworthiness calculation device according to the first embodiment. 13 is a flowchart showing an example of processing executed in an IoT device trust calculation system of a second application example of the IoT device trust calculation device of the first embodiment. FIG. 11 is a diagram illustrating a third application example of the IoT device trustworthiness calculation device according to the first embodiment. FIG. 11 is a diagram illustrating an example of a schematic configuration of an IoT data platform according to a second embodiment. 13 is a flowchart illustrating an example of processing executed in the IoT data platform of the second embodiment. FIG. 11 is a diagram illustrating an example of an IoT data distribution system to which an IoT data platform according to a second embodiment is applied. FIG. 1 is a diagram illustrating an example of a current problem.

First Embodiment
Hereinafter, with reference to the accompanying drawings, embodiments of an IoT device trustworthiness calculation device, an IoT device trustworthiness calculation system, a method for using an IoT device trustworthiness calculation device, an IoT device trustworthiness calculation method, and a program of the present invention will be described.

Fig. 1 is a diagram showing an example of a schematic configuration of an IoT device trust calculation device 1 according to a first embodiment. In detail, Fig. 1(A) shows an example of a schematic configuration of the IoT device trust calculation device 1 according to the first embodiment, and Fig. 1(B) shows an example of a relationship between the IoT device trust calculation device 1 according to the first embodiment and an IoT device D.
In the example shown in Fig. 1, an IoT device credibility calculation device 1 calculates the credibility of data measured by a sensor D1 of an IoT device D. The IoT device D includes a sensor D1, a monitoring unit D2, a checking unit D3, and a storage unit D4.
The sensor D1 measures data such as meteorological data such as water level, ground, temperature, humidity, wind speed, and air 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, and the open/close state of the case of the IoT device D. 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, and the log data of the IoT device D. The storage unit D4 stores owner information, which is information about the owner and/or manager of the IoT device D, and attribute information, which is information about the attributes of the IoT device D. The owner and/or manager of the IoT device D includes not only individuals but also organizations. The attributes of the IoT device D include, for example, the degree of security capability (secure level) of the IoT device D, whether the device (IoT device D) is authenticated, and the like.
In the example shown in Figure 1, the IoT device D has a monitoring unit D2, a checking unit D3, and a memory unit D4, but in other examples, the IoT device D may not have at least any of the monitoring unit D2, the checking unit D3, and the memory unit D4.

In the example shown in Figure 1, the IoT device credit 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, an IoT device attribute analysis unit 16, a credit calculation unit 17, and a memory unit 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. In detail, 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, which is information other than the measurement data transmitted from the IoT device D.
The second acquisition unit 12 acquires, as the owner information, for example, the contents of the declaration of the owner and/or manager of the IoT device D, past history, information acquired from other databases (not shown), information acquired from investigation results, etc. In addition, the second acquisition unit 12 acquires, as the owner information, for example, whether the owner and/or manager of the IoT device D transmits information about the IoT device D, the contents of information about the IoT device D transmitted in the past by the owner and/or manager of the IoT device D, the management status of other IoT devices (not shown) owned or managed by the owner and/or manager of the IoT device D, whether the owner and/or manager of the IoT device D properly performs disposal processing of other IoT devices, information about the creditworthiness of the owner and/or manager of the IoT device D, information about the management system of the owner and/or manager of the IoT device D, etc.

The third acquisition unit 13 acquires the above-mentioned attribute information from the IoT device D. In detail, 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, which is information other than the measurement data transmitted from the IoT device D.
The third acquisition unit 13 acquires, as attribute information, for example, the manufacturer of the IoT device D, the manufacturing date of the IoT device D, whether or not calibration and/or maintenance of the IoT device D has been performed, the date and time when calibration and/or maintenance of the IoT device D was performed, the contents of the calibration and/or maintenance of the IoT device D, the response of the IoT device, whether or not the IoT device D is equipped with a secure chip (not shown), the operating status of the IoT device, authentication information regarding the IoT device D, the installation location of the IoT device D, the installation environment and/or measurement environment of the IoT device D, the difficulty of measuring data by the sensor D1 of the IoT device D, the communication path of the measurement data transmitted by the IoT device D, the level of encryption in the IoT device D, and the like.
In addition, the third acquisition unit 13 acquires, as attribute information, for example, information on 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, and the open/close state of the case of the IoT device D.
In addition, the third acquisition unit 13 acquires, as attribute information, for example, whether the IoT device D checked by the checking unit D3 of the IoT device D has been modified in terms of software and/or hardware, the history of the IoT device D, log data of the IoT device D, etc.
In other words, 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 Figure 1, the IoT device trust calculation device 1 has both the second acquisition unit 12 and the third acquisition unit 13 as non-measured information acquisition units, but in other examples, the IoT device trust calculation device 1 may have only one of the second acquisition unit 12 and the third acquisition unit 13 as a non-measured information acquisition unit.
In yet another example, the above-mentioned owner information may be treated as being included in the attribute information. In this example, the IoT device credibility calculation device 1 includes a third acquisition unit 13 as a non-measurement information acquisition unit (i.e., does not include the second acquisition unit 12), and the third acquisition unit 13 also acquires the owner information included in the attribute information.

In the example shown in FIG. 1, the measurement data analysis unit 14 performs an analysis of the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11. The measurement data analysis unit 14 performs, for example, a comparison between the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 and past measurement data acquired in the past by the first acquisition unit 11 (data measured in the past by the sensor D1 of the IoT device D). In addition, the measurement data analysis unit 14 performs, for example, a comparison between 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 (data measured by a sensor of an IoT device other than the IoT device D). In addition, the measurement data analysis unit 14 performs, for example, an analysis of the presence or absence of traces of human manipulation on the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11. In addition, the measurement data analysis unit 14 performs analysis of the measurement data of the sensor D1 of the IoT device D, for example, by graphically representing the characteristics of the changes in the measurement data of the sensor D1 of the IoT device D and then performing a data mining process, as described below.

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 whether the owner and/or manager of IoT device D is the legitimate owner and/or manager of IoT device D based on, for example, the contents of the declaration made by the owner and/or manager of IoT device D, past history, information obtained from other databases, information obtained from investigation results, etc.
In addition, the owner analysis unit 15 performs an analysis of the owner and/or manager of IoT device D based on, for example, whether or not the owner and/or manager of IoT device D has transmitted information about IoT device D, the content of information about IoT device D that the owner and/or manager of IoT device D has transmitted in the past, the management status of other IoT devices owned or managed by the owner and/or manager of IoT device D, whether or not the owner and/or manager of IoT device D has properly performed disposal processing of other IoT devices, information regarding the creditworthiness 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.

The IoT device attribute analysis unit 16 performs an analysis of the attributes of the IoT device D based on the attribute information (information on the attributes of the IoT device D) acquired by the third acquisition unit 13. The IoT device attribute analysis unit 16 performs an analysis of the attributes of the IoT device D based on, for example, the manufacturer of the IoT device D, the manufacturing time of the IoT device D, whether or not the calibration and/or maintenance of the IoT device D has been performed, the time when the calibration and/or maintenance of the IoT device D was performed, the contents of the calibration and/or maintenance of the IoT device D, the response of the IoT device, whether or not the IoT device D is equipped with a secure chip (not shown), the operation status of the IoT device, authentication information regarding the IoT device D, the installation location of the IoT device D, the installation environment and/or measurement environment of the IoT device D, the difficulty of measuring data by the sensor D1 of the IoT device D, the communication path of the measurement data transmitted by the IoT device D, the encryption level in the IoT device D, and the like.
In addition, the IoT device attribute analysis unit 16 performs an analysis of the attributes of the IoT device D based on, for example, information such as 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, and the open/close state of the case of the IoT device D.
In addition, the IoT device attribute analysis unit 16 performs an analysis of the attributes of the IoT device D based on, 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 data of the IoT device D, etc.

The credibility calculation unit 17 calculates the credibility of the measurement data of the sensor D1 transmitted from the IoT device D based on the analysis results of the measurement data analysis unit 14, the analysis results of the owner analysis unit 15, and the analysis results of the IoT device attribute analysis unit 16. That is, the credibility calculation unit 17 calculates the credibility of the measurement data of the sensor D1 transmitted from the IoT device D based on the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11, the owner information (information on the owner and/or manager of the IoT device D) acquired by the second acquisition unit 12, and the attribute information (information on the attributes of the IoT device D) acquired by the third acquisition unit 13.
In an example in which the IoT device credibility calculation device 1 has only one of the second acquisition unit 12 and the third acquisition unit 13 as a non-measurement information acquisition unit, the credibility calculation unit 17 calculates the credibility of the measurement data of the sensor D1 of the IoT device D based on the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 and the non-measurement information acquired by the non-measurement information acquisition unit.

In the example shown in FIG. 1 , the credibility calculation unit 17 calculates a numerical value or rank as the credibility of the measurement data of the sensor D1 transmitted from the IoT device D.
Even after calculating the credibility of the measurement data of sensor D1 transmitted from IoT device D, the credibility calculation unit 17 continuously calculates the credibility of the measurement data of sensor D1 transmitted from IoT device D based on the results of the analysis continuously performed by the measurement data analysis unit 14, the results of the analysis continuously performed by the owner analysis unit 15, and the results of the analysis continuously performed by the IoT device attribute analysis unit 16.
In the example shown in Figure 1, the monitoring and/or checking of IoT device D has been described as part of the functions of IoT device D, but this is not limited to this, and monitoring and/or checking of IoT device D may be performed by another device, and the information may be input to the IoT device credibility calculation device.
In addition, although it has been stated that the owner information of the IoT device D and the attribute data of the IoT device are acquired directly from the IoT device, this is not limited thereto, and separately stored data may be acquired from the second acquisition unit and the third acquisition unit.

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 executing a multivariate analysis described later.
In yet 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 executing a state point calculation described below.

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

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.
In addition, in step S12, the second acquisition unit 12 acquires owner information, which is information about the owner and/or manager of the IoT device D, from the IoT device D.
In addition, 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 measurement data analysis unit 14 performs an analysis of the measurement data of the sensor D1 of the IoT device D acquired in step S11.
Also, in step S15, 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 (information on the owner and/or manager of the IoT device D) acquired in step S12.
Also, in step S16, the IoT device attribute analysis unit 16 performs 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 credibility of the measurement data of sensor D1 transmitted from 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.
That is, in step S17, the credibility calculation unit 17 calculates the credibility of the measurement data of sensor D1 transmitted from IoT device D based on the measurement data of sensor D1 of IoT device D acquired in step S11, the owner information acquired in step S12, and the attribute information acquired in step S13.
It should be noted that steps S11 to S17 do not have to be performed in this order, and they may be interchanged as appropriate.

As described above, in the first embodiment of the IoT device trust calculation device 1, when calculating the trust of the measurement data of the sensor D1 transmitted from the IoT device D, not only is the measurement data of the sensor D1 of the IoT device D taken into consideration, but information about the owner and/or administrator of the IoT device D and information about the attributes of the IoT device D are also taken into consideration.
Therefore, in the first embodiment of the IoT device credibility calculation device 1, the credibility of the measurement data transmitted from the sensor D1 can be calculated with higher accuracy than when the credibility of the measurement data transmitted from the IoT device D is calculated without based on information about the owner and/or administrator of the IoT device D and information about the attributes of the IoT device D.
In detail, in the technology described in Patent Document 1, the behavior of the first sensor is stopped by the second sensor, making it impossible to use the information from the first sensor, whereas in the IoT device credibility calculation device 1 of the first embodiment, the use of data can be appropriately determined depending on the credibility of the measurement data of sensor D1 calculated by the credibility calculation unit 17.

In addition, in the IoT device credibility calculation device 1 of the first embodiment, when calculating the credibility of the measurement data of the sensor D1 transmitted from the IoT device D, not only is an analysis of the measurement data of the sensor D1 of the IoT device D performed, but an analysis of the owner and/or administrator of the IoT device D and an analysis of the attributes of the IoT device D are also performed.
Therefore, in the first embodiment of the IoT device credibility calculation device 1, the credibility of the measurement data transmitted from the sensor D1 can be calculated with higher accuracy than when the credibility of the measurement data of the sensor D1 transmitted from the IoT device D is calculated without performing an analysis of the owner and/or administrator of the IoT device D and an analysis of the attributes of the IoT device D.

In addition, in the IoT device credibility calculation device 1 of the first embodiment, even after the credibility calculation unit 17 has calculated the credibility of the measurement data of the sensor D1 transmitted from the IoT device D, the credibility calculation unit 17 continuously calculates the credibility of the measurement data of the sensor D1 transmitted from the IoT device D based on the results of the analysis continuously performed by the measurement data analysis unit 14, the results of the analysis continuously performed by the owner analysis unit 15, and the results of the analysis continuously performed by the IoT device attribute analysis unit 16.
Therefore, in the first embodiment of the IoT device credibility calculation device 1, even if any of the analysis results of the measurement data analysis unit 14, the analysis results of the owner analysis unit 15, and the analysis results of the IoT device attribute analysis unit 16 change, the credibility of the measurement data of the sensor D1 transmitted from the IoT device D can be calculated by reflecting that change.

Next, points to be considered when applying the IoT device trustworthiness calculation device 1 according to the first embodiment described above will be described.
In order for a user of the measurement data of sensor D1 of IoT device D to determine the degree of reliability of the measurement data transmitted from IoT device D, in addition to preventing attacks by malicious third parties via a network, etc., it is necessary to make a judgment by (1) confirming whether the IoT device D itself is trustworthy, (2) confirming whether the person who manages or owns the IoT device D is trustworthy, and (3) confirming whether the IoT device D is installed correctly.
These matters do not require a single confirmation of the degree of trustworthiness of the IoT device D. For example, with regard to (1) above, the degree of trustworthiness of the IoT device D may change due to deterioration of the IoT device D, inadequate maintenance of the IoT device D, etc. The degree of trustworthiness of the IoT device D may also change due to modifications to the IoT device D. With regard to (2) above, even if there is no problem with the administrator of the IoT device D at first, it cannot be denied that there is a possibility that false information may be transmitted from the IoT device D due to various reasons, such as an inadequate management system by the administrator of the IoT device D, or the administrator of the IoT device D succumbing to financial temptation and maliciously directing users of the measurement data. Regarding (3) above, even if the sensor D1 of the IoT device D itself is normal, it is possible that the IoT device D will transmit measurement data of the sensor D1 that cannot be said to be correct due to a change in the surroundings of the installation environment of the sensor D1 (for example, if the sensor D1 is a temperature sensor, a change from a situation where the sensor D1 is not exposed to direct sunlight to a situation where the sensor D1 is exposed to direct sunlight). Therefore, it is desirable to continuously and repeatedly check the reliability.

FIG. 3 is a diagram showing an example of processing within the IoT device trustworthiness calculation device 1 when the measurement data analysis unit 14 shown in FIG. 1 executes data mining processing and the trustworthiness calculation unit 17 utilizes artificial intelligence or the like.
In the example shown in Figure 3, "IoT device data" (measurement data of sensor D1 transmitted over time from IoT device D), "owner/administrator information" (information on the owner and/or manager of IoT device D), and "device information" (information regarding the attributes of IoT device D (attribute information)) are input to the IoT device credibility calculation device 1.
"IoT device data" is, for example, time-series data distributed from IoT device D. "Owner/administrator information" is, for example, registration information, management system, past data distribution information, etc., which correspond to positive points described later. "Device information" is, for example, registration information, security of communication path, manufacturer, manufacturing date, calibration status, etc.
The "state analysis by data mining or the like" is performed by the measurement data analysis unit 14 (see FIG. 1). In addition, the "credit score calculation by AI (artificial intelligence) or the like" and the "creditworthiness judgment" are performed by the creditworthiness calculation unit 17 (see FIG. 1).

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

The information on the owner and/or manager of IoT device D includes whether or not the owner and/or manager is the legitimate owner and/or manager of IoT device D based on the declaration of the owner and/or manager of IoT device D, past history, information obtained from other databases and survey results, etc.; whether or not the owner and/or manager transmits information regarding the IoT device D that they own and/or manage; whether or not they properly dispose of IoT device D; the content of information transmitted in the past, the management status, qualifications, credit, management system, etc. of other IoT devices that they own and/or manage.
In other words, the owner analysis unit 15 (see Figure 1) analyzes whether the owner and/or manager of IoT device D is the legitimate owner and/or manager of IoT device D based on the declaration of the owner and/or manager of IoT device D, past history, information obtained from other databases, information obtained from investigation results, etc.
In addition, the owner analysis unit 15 performs an analysis of the owner and/or manager of IoT device D based on whether or not the owner and/or manager of IoT device D has transmitted information about IoT device D, the content of information about IoT device D that the owner and/or manager of IoT device D has transmitted in the past, the management status of other IoT devices owned or managed by the owner and/or manager of IoT device D, whether or not the owner and/or manager of IoT device D has properly performed disposal processing of other IoT devices, information regarding the creditworthiness 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.

The information on IoT device D includes the manufacturer of IoT device D, the date of manufacture, whether or not IoT device D has been calibrated and when, the response of IoT device D, whether or not a secure chip has been present, operating status, various authentication information, installation location, installation environment, the level of difficulty of the data measured by sensor D1 of IoT device D, the communication path, and the level of encryption. This information can be obtained from the model of IoT device D, the type of device used, and responses via communication lines.
In other words, the IoT device attribute analysis unit 16 (see Figure 1) analyzes the attributes of the IoT device D based on the manufacturer of the IoT device D, the manufacturing date of the IoT device D, whether or not calibration and/or maintenance of the IoT device D has been performed, the date and time when calibration and/or maintenance of the IoT device D was performed, the contents of the calibration and/or maintenance of the IoT device D, the response of the IoT device, whether or not the IoT device D is equipped with a secure chip, the operating status of the IoT device, authentication information regarding the IoT device D, the installation location of the IoT device D, the installation environment and/or measurement environment of the IoT device D, the difficulty of measuring data by the sensor D1 of the IoT device D, the communication path of the measurement data of the sensor D1 transmitted by the IoT device D, the level of encryption in the IoT device D, and the like.

The 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, and comparison with transmitted data from other IoT devices.
In other words, the measurement data analysis unit 14 (see Figure 1) compares the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 (see Figure 1) with past measurement data acquired in the past by the first acquisition unit 11.
In addition, the measurement data analysis unit 14 performs a comparison between the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 and data transmitted from IoT devices other than the IoT device D.

It is not necessary to use all of this information, but a comprehensive judgment is made by combining one or more of them. The judgment may be made by a person, but since the number of installed IoT devices is enormous, it is desirable to make the judgment entirely or partially electronically, and AI such as machine learning may be used for the electronic judgment. In particular, statistical analysis of past data, the history of the owner and/or manager of the IoT device D, etc., is important, in addition to analysis based on information only at the time of investigation.
In addition, possible methods for obtaining the basis information include using communication systems such as the Internet or other lines, information from IoT device manufacturers, information recorded in other databases, registered information by the owner and/or administrator of the IoT device D, and confirmation by investigators.
In addition, some or all of this information may be registered in the IoT device D and referenced via a communication line. In this case, it is desirable to store the information in a secure area such as a chip to prevent tampering, and to reference the information via encrypted communication.
It is desirable to require authorization according to the content of the information to be written. Also, the information written to the chip can be added but cannot be erased, and it is desirable to store information such as the date, person, and organization that wrote the data.
In addition, the IoT device D itself may be given a function for monitoring its own power consumption, or a function for self-checking whether any software or hardware modifications have been made to the IoT device D, and this information may also be stored in a securely protected area and made available for reference.

In analyzing the measurement data of the sensor D1 of the IoT device D, it is important to determine whether the contents of the data have been rewritten, whether the IoT device D has been tampered with, and whether there have been any changes in the measurement environment. The key points in making these judgments are the presence or absence of traces of artificial processing or manipulation, and changes in the power consumption of the IoT device D. These judgments may be made by humans, but in order to deal with a large number of IoT devices, it is better to make some or all of the judgments by electronic processing. It is desirable to perform electronic processing such as data mining as necessary for data analysis and classification.
In this manner, by processing the time-series output data from the IoT device D, the probability that an abnormality or an abnormal state has occurred is determined.

For people, organizations, devices, etc., the results of interviews or on-site inspections may be used as a basis for judgment, as necessary. For IoT devices D for which it is necessary to more accurately grasp the reliability of the measurement data of the sensor D1, on-site interviews or inspections where data tampering is difficult are effective.
On the other hand, interviews and on-site inspections require manpower and are therefore not suitable for checking a large number of IoT devices. Therefore, in some cases, it may be possible to reduce the frequency of inspections by conducting surprise inspections or to check remotely using cameras, etc.
In this specification, the degree of reliability of the measurement data of sensor D1 and the reliability are used interchangeably and indicate the degree to which the measurement data of sensor D1 transmitted by the target IoT device D is correct information using a numerical value such as probability or a rank such as A, B, or C, and are not intended to detect or determine whether the data is correct.
In other words, "high credibility" indicates a low possibility of transmitting erroneous measurement data, and "low credibility" indicates a high possibility of transmitting erroneous measurement data. Low credibility does not necessarily mean that the measurement data is incorrect. For example, a temperature sensor that has been insufficiently managed and maintained and has deteriorated over time since its installation may transmit correct measurement data, but it is determined to have a high possibility of transmitting incorrect measurement data and is therefore "low credibility." In addition, in the case of an IoT device D owned by an owner who has a history of transmitting malicious false measurement data many times in the past, the data may be "low credibility" even if it transmits correct information.
Although such a judgment of the degree of comprehensive creditworthiness may be made by an experienced person in the early stages, it becomes possible to automatically calculate a credit score by having AI or the like learn from that experience. Scoring can be performed, for example, using multivariate analysis, judgment-based processing, logistic regression, etc., but is not limited to these, and various algorithms that can be used for scoring can be used.

Figure 4 is a diagram showing an example of data mining processing executed by the measurement data analysis unit 14. In detail, Figure 4 (A) shows the measurement data (time series data) of the sensor D1 of the IoT device D acquired by the first acquisition unit 11. Figure 4 (B) shows a diagram illustrating the characteristics of the changes in the measurement data shown in Figure 4 (A). Figure 4 (C) shows an example of a method for executing data mining processing on the data shown in Figure 4 (B). Figure 5 is a diagram showing an example of a neural network that executes 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. 4A). However, if pre-processing can be performed to graphically represent the characteristics of the changes, as shown in FIG. 4B, more accurate data mining can be performed. For example, a method of forming a converted figure such as the frequency or chaos of the time-dependent changes in the measurement data of the sensor D1 and then analyzing it by data mining can be considered. In addition, as data mining methods, 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, and the like can be considered, and it is advisable to select and combine suitable methods according to the contents of the measurement data of the sensor D1.
In the example shown in Figure 4, the data mining process is performed after the characteristics of the changes in the measurement data of the sensor D1 of the IoT device D are graphically represented, so that the data mining process can be performed with higher accuracy and less computational effort than if the characteristics of the changes in the measurement data are not graphically represented.

In the example shown in FIG. 5, feature extraction is first 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 in the examples shown in FIGS. 4 and 5, once the data is graphically represented, techniques such as a general convolutional neural network can be used, enabling classification with high accuracy and with a small amount of calculation.
In the example shown in FIG. 5, the classification output in the output layer is the probability of occurrence of normality, abnormality in the measurement target, abnormality in the sensor state, improper installation environment, data tampering, device modification, and the like.
The data obtained from the IoT device D may be measurement data obtained by the sensor D1 by sensing the environment in which the IoT device D is installed, or may be data sensing the state of the IoT device D itself, such as the power consumption of the IoT device D itself, the temperature of the calculation unit of the IoT device D, the state and history of opening and closing the case of the IoT device D, and log data.
Although several examples such as pre-processing, data mining, machine learning, and output have been given here, the present invention is not limited to these, and any method, such as electronic, mechanical, or human judgment, can be used as long as it can estimate the reliability of the measurement data of sensor D1.

Figure 6 is a diagram showing an example of the process executed in the credibility calculation unit 17 of the IoT device credibility calculation device 1 of the first embodiment. In detail, Figure 6 is a diagram showing an example of the process executed in step S17 of Figure 2.

In the example shown in FIG. 6, in step S101, a parameter related to the reliability of the measurement data of the sensor D1 of the IoT device D (for example, output from the output layer shown in FIG. 5) is input.
The data input in step S101 may be, for example, data obtained as the reliability of data using a neural network or the like shown in Fig. 5. The data input in step S101 is classified, for example, by analyzing the trends of past data to determine whether the data has been intentionally processed or whether an abnormal value has been indicated due to a malfunction of the IoT device D, and the like, and the likelihood (reliability) of the data is indicated, for example, as a percentage.
In step S102, the attributes of the device (IoT device D) are input. The device attributes may be the degree of the secure capability of the device (secure level), whether the device has been authenticated, etc. The secure level of the device can be calculated from the degree of reliability of the device obtained from the manufacturer and model name of the IoT device D, the degree of vulnerability due to the device settings, the calibration history of the sensor of the device, the reaction of the device obtained through the communication line, etc.

Also, in step S103, information is input about the owner and/or manager (person, organization) of the IoT device D. Possible information about the owner and/or manager of the IoT device D input in step S103 includes whether the owner and/or manager of the IoT device D is authenticated and actually exists, whether or not they have transmitted erroneous data in the past, and if so, the frequency of such transmissions, the degree of trustworthiness of the owner and/or manager of the IoT device D (person score), the degree of trustworthiness of the management system (management system score), and the score of the installation environment of the IoT device D.
The human score indicates score data of a person or organization obtained from past and present information such as income, debt, performance, and behavior. In the case of an organization, the score may be calculated based on data such as the organization's rating, or may be calculated from separately scored human information. The management system score can be obtained from the management history, calibration history, and the like, of the IoT device D, as well as the device itself, by the person or organization.
In addition, the installation environment score can be obtained by checking the installation location and judging whether the sensor is installed correctly by checking the actual environment and judging from images and data, etc.
The order of steps S101, S102, and S103 does not have to be the order shown in Fig. 6, as long as necessary information is input. In addition, it is not necessary to use all of the parameter information related to data quality shown above, and some data may not exist.
Next, in step S104, a trust score that is an indicator of the data quality of the IoT device D is calculated.

Figure 7 is a diagram showing an example in which the credit score is calculated as the creditworthiness of the measurement data of sensor D1 transmitted from IoT device D by the creditworthiness calculation unit 17 of the IoT device creditworthiness calculation device 1 of the first embodiment performing multivariate analysis.
In the example shown in FIG. 7, the credibility calculation unit 17 uses a discriminant function to execute multivariate analysis to calculate a credibility score as the credibility of the measurement data of the sensor D1 transmitted from the IoT device D.
In detail, the coefficients of the discriminant function were obtained by multivariate analysis to determine whether the data was correct or not for various input parameter information of 41 IoT devices (indicated by symbols A to AP in FIG. 7). Furthermore, the results of the discriminant function were converted into a credibility score (likelihood) of the data so that it would be a positive number from the viewpoint of readability.
In the example shown in FIG. 7, a larger credit score indicates better quality of the data. By using the discriminant function obtained in this way, the credit score of the data of a new IoT device can be obtained. Furthermore, by using the results for the input data to newly update the coefficients of the discriminant function, the accuracy can be further improved. 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, a method such as artificial intelligence, such as a neural network, may be used.

Figure 8 is a flowchart for explaining an example of a process in which the credit score calculation unit 17 of the IoT device credit calculation device 1 of the first embodiment calculates a credit score as the creditworthiness of the measurement data of the sensor D1 transmitted from the IoT device D by executing a state point calculation (judgment-based).
Figure 9 is a diagram showing an example in which the trust 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 a state point calculation (judgment-based).

In the example shown in Fig. 8, in step S201, the credibility calculation unit 17 executes calculation of state points. In detail, in step S201, points are assigned to the strength of security (secure level) of the IoT device D to be determined, the person/organization managing 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 the IoT device D (environment score). In the example shown in Fig. 9, a good state of the IoT device D corresponds to a high point, and a normalized value of 1 to 100 is used as the point.
Next, in step S202, the credibility calculation unit 17 executes positive point addition. In detail, in step S202, points are assigned (positive point addition) for past achievements such as proper management and transmission of normal data. Positive point addition is performed by adding the current point to the previous point, and a high evaluation is given to the IoT device D that has transmitted normal data for a long period of time. Points assigned for such past behavior are called "accumulated positive points" (see FIG. 9). In the example shown in FIG. 9, the human score, management system score, and environment score used in the previous state points and the data score obtained by analyzing the measurement data of the sensor D1 transmitted by the IoT device D are normalized to 1 to 5.
In the example shown in Figure 9, the same scores are used for the status points and the accumulated positive points as scores for people/organizations, management systems, and environments, but in other examples, different scores may be used, such as by changing the weighting between the current situation and the past.

Next, in step S203, the credibility calculation unit 17 executes a judgment as to whether or not a negative event has occurred (whether or not a negative event has occurred), which will be described later. For example, if a negative event has occurred (a negative event has occurred), such as the IoT device D sending erroneous data or the registered data being false, the process proceeds to step S204. On the other hand, if there has been no negative event (no negative event has occurred), the process proceeds to step S205.
In step S204, the credibility calculation unit 17 resets or subtracts points, and the process proceeds to step S205. In step S204, a penalty is imposed on a target that has caused an accident that significantly damages credibility by significantly reducing the accumulated positive points. The reset or subtraction of points may be performed on only the accumulated positive points, or may be performed on both the state points and the accumulated positive points.
Regarding points and negative events related to people, organizations, or the environment, the points are calculated commonly for all IoT devices included in that person, organization, or environment.

In step S205, the credit rating calculation unit 17 calculates a credit score from the points calculated in steps S201 to S204.
Next, in step S206, the credit rating calculation unit 17 executes point update (storage of the points calculated in steps S201 to S204).
Next, in step S207, the credit score calculation unit 17 judges whether or not 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 terminated.

In the example shown in Figure 9, for 14 IoT devices (indicated by the symbols A to N in Figure 9), the "status points" were calculated by adding up the secure levels, human scores, management system scores, and environmental scores of the IoT devices, which were normalized to 1 to 100.
Next, the "cumulative positive points" were calculated by adding the human score, data score, management system score, and environment score, all normalized to 1 to 5, to the previous cumulative positive point value. "Cumulative positive points" are added to past points, and high points are awarded to devices that have been stably transmitting correct data for a long period of time.
Next, as examples of negative events, if an accident occurs, such as the discovery of a vulnerability due to incorrect device (IoT equipment) settings or management, the transmission of incorrect data due to malicious or human error, false data being registered, or flaws in the management system being discovered, the points are reset to 0.
The points obtained in this way were then tallied and converted into an easy-to-read "credit score."
It should be noted that the point items shown in FIG. 9 and the range of points to be added or subtracted are not limited to those shown here, and in other examples, they may be increased, decreased, or selected as appropriate.

The information and input order for calculating the trustworthiness of the IoT device D shown in the above example are merely examples and are not limited to these.
Possible fields in which a data user requires the reliability of the measurement data of the sensor D1 of the IoT device D include the following:
Possible examples include data from public organizations such as local governments, such as meteorological data on water levels, ground conditions, temperature, humidity, wind speed, air pressure, and other data related to disasters, agriculture, fisheries, and other nature-related businesses, and data on traffic volume and human movement, etc. This is because such data is installed in the wild, is difficult to manage, and is exposed to wind and rain, making it prone to abnormalities for various reasons.
Other examples include measurement data related to product quality at production sites and construction sites. Specifically, data related to quality such as weight, size, thickness, hardness, color, images, speed, density, and quantity are measured. These are fields in which administrators need to prove that they are not falsifying data, and there is a high demand for third-party data checking. Other examples include data sent by businesses in the agriculture, forestry, and fisheries industries. By using these data mutually, the accuracy of climate change, pests, and disease warning signs can be improved. Specifically, water level, water temperature, air temperature, humidity, wind speed, solar radiation, _CO2 concentration, pests, diseases, images, growth status, fertilizer concentration, soil, water quality, etc. are expected.
Other examples of data that can be used include measurement data on infectious disease-causing bacteria and viruses, radiation doses and radioactive substances, and measurement data on poisons and hazardous substances, etc., and can be used to predict the distribution of hazardous substances. Specifically, possible data include the presence or absence and amount of viruses, the presence or absence and amount of pathogens, the presence or absence and amount of poisons, the presence or absence and amount of radioactive substances, the presence or absence and amount of hazardous materials, the amount of radioactivity, the amount of PM2.5, the amount of nitrogen oxides, oxygen concentration, carbon monoxide concentration, the number of infectious disease infections, and the amount of infectious disease vectors.

In fields where various predictions are made by obtaining and analyzing large amounts of data, large amounts of data are necessary, but if the reliability of the input data is low, it will be impossible to perform a correct analysis.
A high degree of reliability is also required for information from devices related to life and health, such as medical devices used for remote diagnosis, etc. Specifically, body temperature, pulse rate, blood sugar level, triglyceride level, skin color, blood pressure, blood oxygen concentration, amount of exercise, calories burned, dietary details, voice, blood components, exhaled breath components, urine components, saliva components, sweat components, mucous membrane secretion components, etc. are expected.
There is a risk of deliberate data manipulation in fields where prices in trading markets are dependent on the environment and subject to large fluctuations, such as information on energy-related items such as crude oil, gas, and coal, ores, precious metals, rare metals, and gems, or yields of agricultural products, etc. Specifically, data such as images that can estimate yields and amounts, and data related to the content and quality of target substances are expected to be subject to manipulation.
Above, we have listed several fields in which the reliability of data from IoT devices is required, but the field is not limited to these, as long as it is a field in which it is necessary to confirm the reliability of data from IoT devices.

<Application Examples>
Fig. 10 is a diagram showing a first application example of the IoT device credibility calculation device 1 of the first embodiment. In detail, 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 is configured to automatically transmit measurement data of a sensor D1 (see Fig. 1) to a first acquisition unit 11 (see Fig. 1) of the IoT device trust calculation device 1 and to a location other than the IoT device trust calculation device 1 via the network NW.
In the example shown in Figure 10, the credibility of the measurement data of the sensor D1 of the IoT device D calculated by the credibility calculation unit 17 (see Figure 1) of the IoT device credibility calculation device 1 is used to rank the IoT device D.

The IoT rating company's IoT device credibility calculation device 1 checks the credibility of the IoT device D through a network NW such as the Internet, and rates it according to the obtained degree of credibility. Next, the rating result or the result and its basis are provided to the data user, and compensation is received. The data user analyzes and utilizes the data based on the credibility of the IoT device D obtained from the IoT rating company. Another possible service would be one in which the data user requests a rating company to check the credibility of an object, and the rating company reports the results.
The compensation for these services may be money, or a set of data and the like, either individually or in combination. The services themselves may be free of charge, provided that they are used for public activities, to improve corporate value, etc.
The object to be rated may be the IoT device D itself, or may be a group of objects, such as a person or corporation, such as the owner or manager of the IoT device D, or a location, such as an installation location or area. In this way, data users can obtain more accurate data analysis results by using the reliability of the data they use as a premise for data analysis.

Fig. 11 is a diagram showing a second application example of the IoT device credibility calculation device 1 of the first embodiment. In detail, 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 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 is configured to automatically transmit measurement data of a sensor D1 (see Fig. 1) to a first acquisition unit 11 (see Fig. 1) of the IoT device trust calculation device 1 and to a location other than the IoT device trust calculation device 1 via the network NW.
In the example shown in Figure 11, the credibility of the measurement data of sensor D1 of IoT device D calculated by the credibility calculation unit 17 (see Figure 1) of the IoT device credibility calculation device 1 is used to authenticate the IoT device D (issuance of an electronic certificate).

In the current authentication of IoT devices, authentication information for the IoT device is registered in the IoT device during the manufacturing or installation stage, and it is confirmed whether the received data is definitely data sent from the target IoT device. This authentication is effective in preventing data tampering or substitution along the communication path, but it does not allow confirmation of the trustworthiness of the target IoT device or the owner or manager.
In the authentication of IoT devices, which checks the trustworthiness of the target IoT device and the owner or manager, based on the registration information from the owner or manager or both of the IoT device, it is confirmed whether the IoT device is as registered, whether it is managed properly, whether the manager's qualities and qualifications are appropriate, whether the sensor calibration is properly performed, whether the measurement data seems correct, whether the device settings are appropriate, whether the installation method and installation location are appropriate, etc., and if there are no problems, a certificate is issued. If there are problems, guidance is provided. For things that are difficult to judge from digital information alone, such as the installation environment and management system, investigators will conduct regular or surprise inspections or both. If the installation location of the IoT device can be monitored by a camera, etc., it may be checked remotely. From this information, the degree of trust is calculated using the method described above.

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

11 and 12, a deadline is set for the certificate, the deadline is checked periodically, and the certificate is reissued as necessary. In addition, monitoring is performed via a communication line such as the Internet for the IoT device D that has been issued a certificate.
If it is determined that there is a high possibility that the reliability of the measurement data is not being maintained, guidance and correction will be provided.
In other instances, strong impartiality requirements may require refraining from providing guidance or other consulting services.
In yet another example, revocation of the certificate may be performed if it is determined that there is a high possibility that the reliability of the measurement data is not maintained.

IoT devices whose data has been proven to be trustworthy as described above, or the individuals or groups that manage them, or both, will be able to transmit data that has been proven to be trustworthy, thereby increasing the value of the data.
Payment for these services could be in the form of money, the right to secondary use of data, or use of a specific secure chip, either alone or in combination.

Fig. 13 is a diagram showing a third application example of the IoT device credibility calculation device 1 of the first embodiment. In detail, 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 examples shown up to Fig. 12, the data user receives data directly from the IoT device D. On the other hand, in the example shown in Fig. 13, the data user receives data stored in a database such as a cloud. Collecting information in a database DB has the advantage of preventing concentration of data access to the IoT device D and utilizing the cloud security system. Regarding the risk of tampering within the database DB, if a blockchain is used as the database DB, it can be proven that the registered data has not been tampered with, and by combining this with the proof before registration, it is possible to obtain a higher degree of credibility for the data.

Second Embodiment
Hereinafter, an embodiment of an IoT data platform and an IoT data processing method by the IoT data platform of the present invention will be described with reference to the accompanying drawings.

FIG. 14 is a diagram showing an example of a schematic configuration of an IoT data platform P according to the second embodiment.
In the example shown in Figure 14, the IoT data platform P includes an IoT data receiving unit P1, a first consideration calculation unit P2, an IoT data transmitting unit P3, a second consideration calculation unit P4, and a credit information receiving unit P5.
The IoT data receiving unit P1 receives IoT data (e.g., measurement data of the sensor D1 of the IoT device D) transmitted from the IoT device D (see FIG. 1). The first compensation calculation unit P2 calculates a first compensation which is the basis of compensation for the IoT data received by the IoT data receiving unit P1. The IoT data transmission unit P3 transmits the IoT data received by the IoT data receiving unit P1. The second compensation calculation unit P4 calculates a second compensation which is the basis of compensation 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.
In detail, the first consideration calculation unit P2 calculates the first consideration based on the information on the credibility of the IoT data received by the credibility information receiving unit P5. The second consideration calculation unit P4 calculates the second consideration based on the information on 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 compensation calculation unit P2 calculates the first compensation, which is the basis of compensation for the IoT data received in step S1, based on information regarding the credibility of the IoT data received in step S2.
Also, in step S4, the IoT data transmission unit P3 transmits the IoT data received in step S1.
Next, in step S5, the second compensation calculation unit P4 calculates a second compensation, which is the basis of compensation for the IoT data transmitted in step S4, based on information regarding the credibility of the IoT data received in step S2.

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

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 Figure 16, the IoT data receiving unit P1 (see Figure 14) of the IoT data platform P receives IoT data from the IoT data ownership unit W that owns the IoT data transmitted from the IoT device D (see Figure 1) in a state in which the information of the IoT data utilization unit U that utilizes the IoT data transmitted by the IoT data transmission unit P3 (see Figure 14) is kept confidential. Also, the IoT data transmission unit P3 transmits the IoT data to the IoT data utilization unit U in a state in which the information of the IoT data ownership unit W is kept confidential.
In addition, the creditworthiness information receiving unit P5 receives information regarding the creditworthiness of the IoT data from the rating unit R.

The IoT data platform P has a data pricing function. In other words, in the IoT data platform P, the usage price is determined according to the data sender (IoT data owner W) in response to the request (demand) from the data user (IoT data user U). When both parties reach an agreement, information is exchanged.
At this time, it is possible to refer to and take into account information on the reliability of the IoT data from a rating company (rating department R) and to reflect the reliability of the IoT data in the price. The reliability can also be displayed as a basis for setting the price.
In addition, the IoT data platform P may perform processes such as intentionally adding premiums or discounts when information is traded.

In the IoT data platform P, it is desirable to anonymize information on data owners (IoT data ownership part W) and users (IoT data usage part U), etc., and to manage and operate the distribution of IoT data without involving personal information.
The rating company (rating department R) can also use the guarantee of anonymity as one of the pieces of information for evaluation and rating.

In the IoT data platform P, by using not only technologies such as blockchain but also existing cloud security systems, it is possible to manage the history of IoT data such as the owner (IoT data owner W), flow path, and movement, making it possible to use it as a strong means against data tampering.
By using the IoT data platform P, data users (IoT data users U) can easily search for the IoT data they want to use and easily obtain data according to their requirements. In addition, when obtaining the IoT data they want to use, they can make a request to the IoT data platform P and receive a service that allows them to easily use highly reliable information. By utilizing the IoT data platform P, data owners (IoT data owners W) can easily find data users (IoT data users U) and easily obtain payment for data. In addition, data users (IoT data users U) can also obtain information they want (data with high demand). The provider (IoT data owners W) can freely set the scope of use and period of the data to be distributed, and the value of the data provided is guaranteed.
The rating company (rating department R) audits the security, management status, and usage status of the IoT data platform P, and performs rating, certification, and correction. User convenience such as real-timeness and searchability can also be used as evaluation information. In addition, if data management of the IoT data platform P is managed using a blockchain, it can be determined that data tampering is unlikely to occur, and this can be used as one of the pieces of information for score rating. In cases where there is a need to ensure fairness, it may be required that consulting activities such as guidance not be performed.
This IoT data platform P can be used as a platform for handling and distributing a wide range of data, not just information from IoT devices (IoT equipment D).

Although the above describes the form for carrying out the present invention using the 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 scope of the present invention. The configurations described in the above-mentioned embodiments and examples may be combined as appropriate.

In addition, the whole or part of the functions of each unit of the IoT device credibility calculation device 1 and the IoT data platform P in the above-mentioned embodiment may be realized by recording a program for realizing these functions in a computer-readable recording medium, reading the program recorded in the recording medium into a computer system, and executing it. Note that the "computer system" here includes hardware such as the OS and peripheral devices.
Furthermore, "computer-readable recording medium" refers to portable media such as flexible disks, optical magnetic disks, ROMs, and CD-ROMs, and storage units such as hard disks built into computer systems. Furthermore, "computer-readable recording medium" may also include those that dynamically hold 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, and those that hold a program for a certain period of time, such as volatile memory inside a computer system that serves as a server or client in such cases. Furthermore, the above program may be one that realizes 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 one embodiment of the IoT device credit calculation device of the present invention, the IoT device further includes a check unit that checks the history and/or log data of the IoT device to determine whether the IoT device has been modified in terms of software and/or hardware, and the third acquisition unit acquires the history and/or log data of the IoT device to determine whether the IoT device checked by the check unit has been modified in terms of software and/or hardware, and the IoT device attribute analysis unit may perform an analysis of the attributes of the IoT device based on the history and/or log data of the IoT device to determine whether the IoT device has been modified in terms of software and/or hardware acquired by the third acquisition unit.

In one embodiment of the IoT device credibility calculation device of the present invention, the credibility calculation unit may calculate a credibility score as the credibility of the measurement data transmitted from the IoT device by performing multivariate analysis or state point calculation.

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

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

In the method of using an 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 credibility calculation device, in which the credibility of the measurement data calculated by the credibility calculation unit is used to rate the IoT device.

In an 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.

(Additional Note)
The above-described embodiment of the invention can also be understood as follows.
One aspect of the present invention aims to provide an IoT device trustworthiness calculation device, an IoT device trustworthiness calculation system, a method for using an IoT device trustworthiness calculation device, an IoT device trustworthiness calculation method, and a program that can calculate the trustworthiness of measurement data transmitted from an IoT device with high accuracy.
(Aspect 1)
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 acquires non-measurement information other than the measurement data transmitted from the IoT device;
An IoT device credibility calculation device comprising: a credibility calculation unit 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.
(Aspect 2)
The non-measurement information includes owner information, which is information about an owner and/or manager of the IoT device, and attribute information, which is information about attributes of the IoT device;
The non-measurement information acquisition unit
A second acquisition unit that acquires the owner information;
a third acquisition unit that acquires the attribute information,
The credibility calculation unit calculates the credibility of the measurement data transmitted from the IoT device 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.
2. The IoT device trustworthiness calculation device according to claim 1.
(Aspect 3)
a measurement data analysis unit that performs analysis of the measurement data acquired by the first acquisition unit;
An owner analysis unit that performs an analysis of an 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 performs an analysis of the 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.
The IoT device trustworthiness calculation device according to aspect 2.
(Aspect 4)
The measurement data analysis unit
A comparison is performed between the measurement data acquired by the first acquisition unit and past measurement data acquired in the past by the first acquisition unit,
Executing a comparison between the measurement data acquired by the first acquisition unit and data transmitted from an IoT device other than the IoT device;
The IoT device trustworthiness calculation device according to aspect 3.
(Aspect 5)
The measurement data analysis unit
Executing an analysis of the measurement data acquired by the first acquisition unit to determine whether or not there is a trace of human manipulation;
The IoT device trustworthiness calculation device according to aspect 4.
(Aspect 6)
The measurement data analysis unit
performing an analysis of the measurement data by graphically representing the characteristics of the changes in the measurement data and then performing a data mining process;
The IoT device trustworthiness calculation device according to any one of aspects 3 to 5.
(Aspect 7)
The owner analysis unit,
Analyzing whether the owner and/or manager of the IoT device is a legitimate owner and/or manager of the IoT device based on at least one of the declaration of the owner and/or manager of the IoT device, past history, information obtained from other databases, and information obtained from the investigation results;
The IoT device trustworthiness calculation device according to any one of aspects 3 to 6.
(Aspect 8)
The owner analysis unit,
performing an analysis of the owner and/or manager of the IoT device based on at least any of the following: whether the owner and/or manager of the IoT device transmits information about the IoT device, the content of information about the IoT device previously transmitted by the owner and/or manager of the IoT device, the management status of other IoT devices owned or managed by the owner and/or manager of the IoT device, whether the owner and/or manager of the IoT device properly performs disposal processing of the other IoT devices, information about the creditworthiness of the owner and/or manager of the IoT device, and information about the management system of the owner and/or manager of the IoT device;
The IoT device trustworthiness calculation device according to any one of aspects 3 to 7.
(Aspect 9)
The IoT device attribute analysis unit
Perform an analysis of attributes of the IoT device based on at least one of the manufacturer of the IoT device, the manufacturing date of the IoT device, whether or not calibration and/or maintenance of the IoT device has been performed, the date and time when calibration and/or maintenance of the IoT device has been performed, the content of the calibration and/or maintenance of the IoT device, the response of the IoT device, whether or not the IoT device is equipped with a secure chip, the operation status of the IoT device, authentication information regarding the IoT device, the installation location of the IoT device, the installation environment and/or measurement environment of the IoT device, the difficulty of measuring the measurement data by the sensor of the IoT device, the communication path of the measurement data transmitted by the IoT device, and the level of encryption in the IoT device.
The IoT device trustworthiness calculation device according to any one of aspects 3 to 8.
(Aspect 10)
The IoT device further includes a monitoring unit that monitors the power consumption of the IoT device, the temperature of a calculation unit of the IoT device, and/or an open/close state of a case of the IoT device,
The third acquisition unit acquires information on the power consumption of the IoT device, the temperature of a calculation unit of the IoT device, and/or an open/close state of a case of the IoT device, which are monitored by the monitoring unit,
The IoT device attribute analysis unit
An analysis of attributes of the IoT device is performed based on information on the power consumption of the IoT device, the temperature of a calculation unit of the IoT device, and/or the open/close state of a case of the IoT device acquired by the third acquisition unit.
10. The IoT device trustworthiness calculation device according to aspect 9.
(Aspect 11)
After the credibility calculation unit calculates the credibility of the measurement data transmitted from the IoT device,
The credit rating calculation unit is
Based on the result of the analysis continuously performed by the measurement data analysis unit, the result of the analysis continuously performed by the owner analysis unit, and the result of the analysis continuously performed by the IoT device attribute analysis unit, the reliability of the measurement data transmitted from the IoT device is continuously calculated.
The IoT device trustworthiness calculation device according to any one of aspects 3 to 10.
(Aspect 12)
The credit score calculation unit calculates a credit score as the credit score of the measurement data transmitted from the IoT device by using artificial intelligence.
The IoT device trustworthiness calculation device according to any one of aspects 1 to 11.
(Aspect 13)
An IoT device trustworthiness calculation device according to any one of aspects 1 to 12;
The IoT device;
An IoT device trust calculation system comprising:
The IoT device credibility 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 automatically transmit the measurement data to the first acquisition unit of the IoT device trust calculation device and to a location other than the IoT device trust calculation device via the network.
IoT device credibility calculation system.
(Aspect 14)
A method for using the IoT device trustworthiness calculation device according to any one of aspects 1 to 12,
The credibility of the measurement data calculated by the credibility calculation unit is used to issue a certificate of the IoT device.
A method for using an IoT device credibility calculation device.
(Aspect 15)
A method for using the IoT device trustworthiness calculation device according to any one of aspects 1 to 12,
The credibility of the measurement data calculated by the credibility calculation unit is used to rank the owner and/or manager of the IoT device and/or the installation area of the IoT device.
A method for using an IoT device credibility calculation device.
(Aspect 16)
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;
A non-measurement information acquisition step in which a non-measurement information acquisition unit of an IoT device credibility calculation device acquires non-measurement information other than the measurement data transmitted from the IoT device;
An IoT device credibility calculation method comprising: a credibility calculation step in which a credibility calculation unit of an IoT device credibility calculation device calculates 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.
(Aspect 17)
On 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 other than the measurement data transmitted from the IoT device;
A program for executing a credibility calculation step 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.

1...IoT device credit rating calculation device, 11...first acquisition unit, 12...second acquisition unit, 13...third acquisition unit, 14...measurement data analysis unit, 15...owner analysis unit, 16...IoT device attribute analysis unit, 17...credit rating calculation unit, 18...storage unit, S...IoT device credit rating 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 unit, P2...first consideration calculation unit, P3...IoT data transmitting unit, P4...second consideration calculation unit, P5...credit rating information receiving unit, DS...IoT data distribution system, R...rating unit, W...IoT data ownership unit, U...IoT data utilization unit

Claims (4)

An IoT data receiving unit that receives IoT data transmitted from an IoT device;
A compensation calculation unit that calculates a compensation that is a basis for the IoT data received by the IoT data receiving unit;
An IoT data transmission unit that transmits the IoT data received by the IoT data receiving unit;
A credibility information receiving unit that receives information regarding the credibility of the IoT data received by the IoT data receiving unit,
The information on the creditworthiness is at least one of non-measurement information, owner information, and attribute information, or a combination of these,
The owner information is composed of at least the past history of the owner and/or manager, the management status of other IoT devices managed by the owner and/or manager, whether the owner and/ or manager properly disposes of other IoT devices, the credit information of the owner and/or manager, and the management system of the owner and/or manager, or a combination of these, and is owner information analyzed based on the declaration of the owner and/or manager of the IoT device, the past history, information obtained from other databases, and information obtained from investigation results.
IoT data platform. An IoT data receiving unit that receives IoT data transmitted from an IoT device;
A compensation calculation unit that calculates a compensation that is a basis for the IoT data received by the IoT data receiving unit;
An IoT data transmission unit that transmits the IoT data received by the IoT data receiving unit;
A credibility information receiving unit that receives information regarding the credibility of the IoT data received by the IoT data receiving unit,
Display the basis for the calculation of the price for IoT data sent and received;
The information regarding the creditworthiness is at least one of non-measurement information, owner information, and attribute information, or a combination thereof.
IoT data platform. An IoT data platform capable of acquiring data from a rating unit connected to an outside of the IoT data platform ,
An IoT data receiving unit that receives IoT data transmitted from an IoT device;
A compensation calculation unit that calculates a compensation that is a basis for the IoT data received by the IoT data receiving unit;
An IoT data transmission unit that transmits the IoT data received by the IoT data receiving unit;
A credibility information receiving unit that receives information regarding the credibility of the IoT data received by the IoT data receiving unit,
The information on the creditworthiness is at least one of non-measurement information, owner information, and attribute information, or a combination of these,
The credit rating information receiving unit receives information regarding the credit rating of the IoT data according to the result of the rating by the external rating company from the rating unit ;
The consideration calculation unit calculates the consideration using information regarding the creditworthiness of the IoT data received from the rating unit by the creditworthiness information receiving unit. An IoT data platform. The IoT data receiving unit receives the IoT data from an IoT data possessing unit that possesses the IoT data transmitted from the IoT device in a state in which information of an IoT data utilizing unit that utilizes the IoT data transmitted by the IoT data transmitting unit is concealed;
The IoT data transmission unit transmits the IoT data to the IoT data utilization unit while concealing the information of the IoT data ownership unit.
The IoT data platform according to any one of claims 1 to 3.

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