Global reputation evaluation method and system based on Internet of Things

Abstract

A global reputation evaluation method based on the Internet of Things (IoT) is described. In an IoT network, the method includes a first networking unit, several second networking units, and several third networking units. The method comprises the following steps: during an update cycle, all second and third networking units requesting interaction with the first networking unit independently assign a first reputation score to the first networking unit, with each first reputation score being positively correlated with the interaction time and quality of the first networking unit's requests for interaction with the other networking units; and the second reputation score is broadcasted within the IoT network. This technical solution, by weighting the first reputation scores of two or more networking units to obtain a global reputation evaluation within the network, can achieve the technical effect of quantifying the quality of interaction between a first networking unit and other networking units globally within the IoT network.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things communication technology, and in particular to a method capable of quantifying the cooperation status of units within the Internet of Things. Background Art

[0002] IoT technology is gradually developing towards multi-terminal interconnection and intelligent development. For example, it can provide a foundation for transportation management, vehicle-to-road information services, and intelligent vehicle control. With the development of IoT technology, the number of intelligent control systems, intelligent network systems, and audio-visual entertainment systems that can be installed on various terminals is increasing, and the security issues exposed by IoT are becoming increasingly prominent. For example, the various sensors installed in intelligent connected vehicles are responsible for collecting data on roads and driving conditions and for environmental perception. Intelligent transportation systems rely on the driving information, vehicle status information, and road condition information collected by intelligent connected vehicles.

[0003] Intelligent entities participating in the Internet of Things (IoT) possess similar characteristics, are interconnected through the network, and share similar values, forming a network group. Network group behavior is a form of collective behavior characterized by social, distributed, robust, and self-organizing characteristics. Individuals within each node within the network frequently interact and influence each other. Consequently, traditional statistical analysis methods are unable to address the complexity and dynamics of network group behavior. Therefore, intelligent management of each networking unit within the IoT network is necessary. Summary of the Invention

[0004] Therefore, it is necessary to provide a new technical method for intelligently managing each networking unit in the Internet of Things network, so as to achieve the technical effect of quantitatively evaluating the interactive reputation of individuals in the Internet of Things network.

[0005] To achieve the above-mentioned purpose, the inventor provides a global reputation evaluation method based on the Internet of Things, in which the Internet of Things network comprises a first networking unit, a plurality of second networking units and a plurality of third networking units.

[0006] The method includes the following steps: in an update cycle, all second networking units and third networking units that request to interact with a first networking unit independently perform a first reputation score on the first networking unit, wherein the first reputation score performed by each is positively correlated with the interaction time and interaction quality of the first networking unit's requests for interaction with each other networking unit;

[0007] Calculating a second reputation score, where the second reputation score is: a normalized weighted average score of the first reputation score of the first networking unit assigned by the third networking unit to the first networking unit and the first reputation score of the first networking unit assigned by the second networking unit to the first networking unit during the update period;

[0008] The second reputation score is broadcasted in the Internet of Things network, so that other networked units can use it to perform the first reputation score on the first networked unit in the next update cycle.

[0009] In some embodiments of the present application, the third networking unit is a centralized node, and the weight of the third networking unit is greater than that of the second networking unit.

[0010] In some embodiments of the present application, the evaluation period is determined based on the data transmission delay in the Internet of Things network and the variance of the first reputation value of each unit.

[0011] In some embodiments of the present application, the second reputation score:

[0012]

[0013] Among them, β is the global reputation evaluation weight, R D (u) is the first reputation evaluation of the third network unit on the first network unit, J is the total number of second network units that effectively interact with the first network unit, R Dj The first reputation evaluation of the first networking unit made by the second networking unit within the update period T based on the interaction between the second networking unit and the first networking unit.

[0014] In some embodiments of the present application, the credibility of the first reputation evaluation made by the j-th second networking unit is also included. The second reputation score

[0015]

[0016] In some embodiments of the present application, the The credit score is determined according to the second credit score of the j-th second networking unit in the previous cycle.

[0017] In some embodiments of the present application,

[0018] In some embodiments of the present application, a threshold is set All second reputation scores below The first credit rating R made by the networking unit Dj The results were eliminated.

[0019] A global reputation evaluation system based on the Internet of Things,

[0020] It includes a first networking unit, a plurality of second networking units and a plurality of third networking units, wherein the first networking unit forms an Internet of Things network with the second networking unit and the third networking unit.

[0021] In an update cycle, all second and third networking units that request to interact with the first networking unit independently perform a first reputation score on the first networking unit, and the first reputation score performed by each is positively correlated with the interaction time and interaction quality of the first networking unit's requests to interact with each other networking unit;

[0022] The third networking unit is used to calculate a second reputation score, where the second reputation score is a normalized weighted average score of the first reputation score of the first networking unit assigned to the third networking unit and the first reputation score of the first networking unit assigned to the second networking unit during the update period;

[0023] The third networking unit is further configured to broadcast the second reputation score in the Internet of Things network, so that other networking units can perform the first reputation score on the first networking unit in the next update cycle.

[0024] In some embodiments of the present application, the second reputation score:

[0025]

[0026] Among them, β is the global reputation evaluation weight, R D (u) is the first reputation evaluation of the third network unit on the first network unit, J is the total number of second network units that effectively interact with the first network unit, R Dj The first reputation evaluation of the first networking unit made by the second networking unit on the first networking unit according to the interaction between the second networking unit and the first networking unit within the update period T is:

[0027] The third networking unit is a centralized node or cloud server.

[0028] Different from the existing technology, the above technical solution obtains the first reputation by evaluating the interaction between the sender of the interaction request and the recipient in the Internet of Things network, and then obtains the global reputation evaluation in the network by weighting the first reputation scores of more than two networked units. It can solve the technical effect of quantifying the interaction quality of the first networked unit in the Internet of Things network with other global networked units. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of a reputation evaluation method based on the Internet of Things according to a specific embodiment of the present invention;

[0030] Figure 2 This is a unit diagram of a reputation evaluation system based on the Internet of Things according to another embodiment of the present invention;

[0031] Figure 3 This is a flow chart of a global reputation evaluation method based on the Internet of Things according to a specific embodiment of the present invention;

[0032] Figure 4 This is a unit diagram of a global reputation evaluation system based on the Internet of Things according to a specific embodiment of the present invention;

[0033] Figure 5 This is a flow chart of a mutual trust evaluation method for an Internet of Things network according to a specific embodiment of the present invention;

[0034] Figure 6 This is a unit diagram of a mutual trust evaluation system for an Internet of Things network according to a specific embodiment of the present invention;

[0035] Figure 7 This is a flow chart of a method for evaluating the mutual assistance utility of individuals in an Internet of Things network according to a specific embodiment of the present invention;

[0036] Figure 8 This is a unit diagram of an individual mutual assistance utility evaluation system in an Internet of Things network according to a specific embodiment of the present invention;

[0037] Figure 9 This is a schematic diagram of a two-way interactive mode scenario according to a specific embodiment of the present invention;

[0038] Figure 10 This is a diagram for establishing a dynamic social trust network according to a specific embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to explain the technical content, structural features, achieved objectives and effects of the technical solution in detail, the following is a detailed description in conjunction with specific embodiments and accompanying drawings.

[0040] The Internet of Things (IoT) refers to a network that connects any object to the internet through information sensing devices and agreed-upon protocols, enabling information exchange and communication to achieve intelligent identification, positioning, tracking, monitoring, and management. In layman's terms, the IoT is "the Internet of Things," enabling information exchange, collaboration, and sharing among objects.

[0041] However, it has been discovered in real life that as the Internet of Things (IoT) develops and individual intelligence increases, it becomes most resource-efficient for individuals in the IoT to simply receive information without interacting with each other. This spread of selfishness may ultimately lead to the functional paralysis of the IoT network. The inventors of this application have noticed the above problem and hope to propose a concept of altruism and construct an evaluation and scoring system for individuals in the IoT network.

[0042] See also Figure 1 , is a flow chart of a reputation evaluation method based on the Internet of Things. The solution includes the following steps: In the Internet of Things network, the second networking unit and the first networking unit are included. Please refer to Figure 1 ,

[0043] S11 The second networking unit performs a first reputation score on the first networking unit based on the interaction results between the second networking unit and the first networking unit. If S12 The first networking unit chooses to cooperate with the interaction request of the second networking unit, the first reputation is positively correlated with the interaction time and interaction quality of the first networking unit's cooperative behavior. If S13 The first networking unit chooses to reject the interaction request of the second networking unit, the first reputation is negatively correlated with the punitive reputation adjustment value.

[0044] In some embodiments, the first and second networking units are entities participating in the Internet of Things (IoT) network. These networking units may include smart home appliances, such as smart refrigerators, smart air conditioners, smart TVs, and smart washing machines, as well as wearable devices such as mobile phones, smart watches, and smart glasses. In the case of the Internet of Vehicles (IoV), the first and second networking units may also be onboard units (IoVs), roadside units (RSUs), or cloud servers.

[0045] For example, if the second network unit and the first network unit are both devices of the same level, such as vehicle-mounted units, then during a first reputation evaluation cycle, the second network unit may send an interaction request to the first network unit. These interaction requests may include requests for information sharing, platooning, road emergency broadcasts, efficient speed guidance, priority vehicle access management, road traffic information sharing, multimedia and entertainment information push, etc. The first network unit can choose to cooperate or refuse, which will affect the second network unit's evaluation of the first network unit's first reputation during that evaluation cycle. First reputation is positively correlated with the interaction time and quality of the first network unit's cooperative behavior. That is, the higher the interaction time and quality of the first network unit's cooperative behavior, the higher the first reputation. If the first network unit refuses the second network unit's interaction request, the first reputation is negatively correlated with a punitive reputation adjustment value, requiring a penalty mechanism. This punitive reputation adjustment value can be determined as needed. Refusing cooperation will result in a decrease in first reputation.

[0046] Through the above solution, the sender of an interaction request evaluates the interaction with the recipient in the IoT network to obtain a first reputation, which can achieve the technical effect of quantifying the interactive cooperation quality of the first networking unit in the IoT network. The first reputation represents the first networking unit's responsiveness to external interaction requests.

[0047] In some embodiments of the present application, a utility function η(u) is included. If the first network unit chooses to cooperate with the interaction request of the second network unit, the first reputation is positively correlated with the interaction time and interaction quality of the first network unit's cooperative behavior, and the positive correlation coefficient is If the first networking unit rejects the interaction request from the second networking unit, the first reputation is negatively correlated with the punitive reputation adjustment value, with a negative correlation coefficient of η(u). By designing positive and negative correlation coefficients, the growth and penalty rules for the first reputation can be further refined, allowing the growth and reduction rates to be determined, thereby better achieving the technical effect of quantifying the interactive cooperation quality of the first networking unit in the IoT network.

[0048] In some embodiments of the present application, if the first networking unit chooses to cooperate with the interaction request of the second networking unit, the first reputation:

[0049]

[0050] Where γ(u) is the weighted result of the first reputation evaluation of the first networking unit by all networking units in the entire Internet of Things network in the previous evaluation cycle, t D is the standard interaction time of the standard interaction behavior, q D is the standard interaction quality value of the standard interaction behavior, γ D1 is the cooperation adjustment value of cooperative behavior, t d ,q d The interaction time and quality values ​​for the interaction between the first and second networking units are shown in Figure 1. By incorporating the evaluation results from the previous evaluation cycle, the historical reputation of the first networking unit can be comprehensively considered to prevent abnormal individual interactions from causing sudden changes in reputation. Furthermore, by introducing standard interaction time and quality, the interaction time and quality of the first networking unit are benchmarked. These steps further achieve the technical effect of quantifying the quality of interaction and cooperation between the first networking unit in the IoT network.

[0051] In order to better determine the benchmark for the standard interaction time and the standard interaction quality value, in some embodiments of the present application, the standard interaction time and the standard interaction quality value are determined based on the interaction quality of the entire IoT network during the previous evaluation cycle. This determination can be performed by a centralized node in the IoT network. The centralized node taking on more calculation tasks can also strengthen the authority of the centralized node and reduce the resource burden on the networked units.

[0052] In some embodiments of the present application, if the first networking unit chooses to reject the interaction request of the second networking unit, the first reputation:

[0053] R D (u) = γ(u) - η(u)γ D2 ,

[0054] Where γ(u) is the weighted result of the first reputation evaluation of the first networking unit by all networking units of the entire Internet of Things network in the previous evaluation cycle, γD2 is the reputation adjustment value for the interaction behavior. By introducing the evaluation results of the previous cycle, the fluctuation of the first reputation evaluation of the first networking unit is reduced and more stable. The above steps better achieve the technical effect of quantifying the interactive cooperation quality of the first networking unit in the Internet of Things network.

[0055] In some embodiments of the present application, the utility function η(u) is an exponential function, a logarithmic function, or a binomial function, with a range of [0, 1]. The utility function can be set according to actual circumstances and can be used as a proportional coefficient for adding or subtracting the first credit. This solution does not impose any restrictions on this.

[0056] In some embodiments of the present application, the utility function:

[0057]

[0058] Where δ is the adjustment constant of the utility function, γ(u) is the weighted result of the first reputation evaluation of the first networking unit by all networking units of the entire Internet of Things network in the previous evaluation cycle, and γ max The maximum score of all connected units in the previous evaluation cycle. By specifying the specific form of the utility function, changes in the utility function are based on the ratio of the weighted evaluation results of the previous cycle to the global maximum, making reputation evaluation consistent with the realities of IoT applications. When highly reputable connected units perform well in interactions, their reputation values ​​change less. On the other hand, when highly reputable connected units fail to maintain cooperative behavior, that is, when their interaction strategies undergo a sudden change, their reputation values ​​undergo significant adjustments.

[0059] In some embodiments of the present application, an evaluation period T is included, and the evaluation period is determined based on the data transmission delay in the Internet of Things network and the variance of the first reputation value of each unit. When the data transmission delay in the Internet of Things network is large, the evaluation period can be relatively extended, so that the update frequency in the Internet of Things network is not too fast, resulting in the judgment results of many other networked units not being taken into account. When the first reputation of each networked unit tends to be stable, the evaluation period can also be extended to save computing resource consumption for evaluation actions in the entire network. The specific time of the evaluation period can also be published through a centralized node or an authoritative node or the cloud.

[0060] exist Figure 2 In the embodiment shown, a reputation evaluation system based on the Internet of Things is also disclosed, including a second networking unit 22, a first networking unit 21,

[0061] The second networking unit is configured to assign a first reputation score to the first networking unit based on the interaction results between the second networking unit and the first networking unit. If the first networking unit cooperates with the second networking unit's interaction request, the first reputation is positively correlated with the interaction time and quality of the first networking unit's cooperative behavior. If the first networking unit rejects the second networking unit's interaction request, the first reputation is negatively correlated with a punitive reputation adjustment value. This technical solution, by evaluating the interaction between the sender and the recipient of an interaction request in an IoT network to obtain a first reputation, can achieve the technical effect of quantifying the quality of interaction and cooperation between the first networking unit in the IoT network.

[0062] In some embodiments of the present application, if the first networking unit chooses to cooperate with the interaction request of the second networking unit, the first reputation:

[0063]

[0064] Where γ(u) is the weighted result of the first reputation evaluation of the first networking unit by all networking units in the entire Internet of Things network in the previous evaluation cycle, t D is the standard interaction time of the standard interaction behavior, q D is the standard interaction quality value of the standard interaction behavior, γ D1 is the cooperation adjustment value of cooperative behavior, t d ,q d The interaction time and interaction quality value of the interaction behavior between the first networking unit and the second networking unit;

[0065] If the first networking unit chooses to reject the interaction request of the second networking unit, the first reputation:

[0066] R D (u) = γ(u) - η(u)γ D2 ,

[0067] Where γ(u) is the weighted result of the first reputation evaluation of the first networking unit by all networking units of the entire Internet of Things network in the previous evaluation cycle, γ D2 The reputation adjustment value of the interaction behavior;

[0068] The utility function:

[0069]

[0070] Where δ is the adjustment constant of the utility function, γ max It is the maximum score of all networked units in the previous evaluation cycle.

[0071] The above system solves the problem of first credit evaluation in a better and more complete way.

[0072] Others such as Figure 3 The illustrated embodiment also introduces a global reputation evaluation method based on the Internet of Things (IoT). The IoT network includes a first networking unit, several second networking units, and several third networking units. In some embodiments of this solution, the third networking unit can be a smart home appliance, such as a smart refrigerator, smart air conditioner, smart TV, or smart washing machine, or a wearable device such as a mobile phone, smart watch, or smart glasses. In the case of the Internet of Vehicles (IoV), the third networking unit can also be an onboard unit, a roadside unit, or a cloud server. In some preferred embodiments, the second and third networking units have different functions within the IoV network, or their permissions and authority levels are different. For example, the third networking unit can be a centralized node, which can be a roadside unit responsible for coordination and networking, or a cloud server, while the second networking unit is an onboard unit with the same functions and permissions as the first networking unit.

[0073] The global reputation evaluation method includes the following steps: in an update cycle, S31, all second network units and third network units that request to interact with a first network unit independently perform a first reputation score on the first network unit, and the first reputation score performed by each is positively correlated with the interaction time and interaction quality of the first network unit's request to interact with each other network unit;

[0074] S33 calculates a second reputation score, where the second reputation score is: a normalized weighted average score of the first reputation score of the first networking unit assigned to the third networking unit and the first reputation score of the first networking unit assigned to the second networking unit during the update period;

[0075] S35 broadcasts the second reputation score in the Internet of Things network, so that other networked units can use it to perform the first reputation score on the first networked unit in the next update cycle.

[0076] Second reputation combines the weighted scores of all networked units on the first reputation score of the first networked unit, also known as a global reputation score or global reputation. Second reputation evaluates the first networked unit's interaction with the entire IoT network. Normalized weighting refers to the sum of all weights in the second reputation score being 1. There can be multiple second networked units, and multiple third networked units. All second and third networked units requesting interaction with the first networked unit independently assign a first reputation score to the first networked unit. This allows for a better global reputation evaluation within a weighted computing network, addressing the technical effect of quantifying the quality of interaction between the first networked unit and other networked units globally within the IoT network.

[0077] In some embodiments of the present application, the third networking unit is a centralized node, which can be a roadside terminal that undertakes coordination and networking functions, or a cloud server. The authority of the third networking unit in the Internet of Things should be set higher, the third networking unit generally undertakes more computing functions, and the third networking unit is not easily tampered with, so the weight of the third networking unit can be set greater than the second networking unit. By giving the third unit a greater weight than the second networking unit for the first reputation score, the evaluation result of the second reputation can be made more reliable and more authoritative.

[0078] In some embodiments of the present application, the evaluation period is determined based on the data transmission delay in the Internet of Things network, the variance of the first reputation value of each unit, or the global reputation distribution. When the data transmission delay between the first networking unit and the second networking unit or the third networking unit in the Internet of Things network is large, the evaluation period can be relatively extended, so that the update frequency in the Internet of Things network will not be too fast, resulting in the judgment results of many other networking units not being taken into account. When the first reputation of each networking unit tends to be stable, the evaluation period can also be extended to save computing resource consumption for evaluation actions in the entire network. The specific time of the evaluation period can also be published through a centralized node or an authoritative node or the cloud.

[0079] In some embodiments of the present application, the second reputation score:

[0080]

[0081] Among them, β is the global reputation evaluation weight, R D (u) is the first reputation evaluation of the third network unit on the first network unit, J is the total number of second network units that effectively interact with the first network unit, R Dj The first reputation evaluation made by the second networking unit to the first networking unit based on the interaction between the second networking unit and the first networking unit within the update period T. In this embodiment, assuming that there is only one third networking unit in the Internet of Things network, an evaluation R can be obtained. D (u), On the other hand, it can also be assumed that there are multiple third networking units in the Internet of Things network, but only one unified evaluation R is made D (u), or there are multiple third networking units in the Internet of Things network, and each of them makes a weighted average of the first reputation evaluation and obtains R D Through the above scheme, the global reputation of the first networking unit can be obtained, and an objective quantitative result is obtained for the cooperative interaction of the first networking unit with all other units in the Internet of Things network.

[0082] In some embodiments of the present application, the credibility of the first reputation evaluation made by the j-th second networking unit is also included. The second reputation score

[0083]

[0084] Among them, β is the global reputation evaluation weight, R D (u) is the first reputation evaluation of the third network unit on the first network unit, J is the total number of second network units that effectively interact with the first network unit, R Dj The second network unit in the update period T makes a first reputation evaluation of the first network unit based on its own interaction with the first network unit. The third network unit is a centralized node or cloud server. The credibility of the reputation evaluation of the second networking unit.

[0085] The second networking unit is generally a decentralized networking unit. Therefore, it is necessary to distinguish the credibility of the evaluation results of different second networking units to prevent malicious units from infiltrating the Internet of Things network and causing damage.

[0086] In a further embodiment, in order to distinguish malicious units, the first evaluation result produced by the second network unit can be related to its own cooperative interaction level. Therefore, the Determined based on the second reputation score of the jth second networking unit in the previous cycle. In order to normalize the evaluation results, you can set

[0087] In some further embodiments, in order to punish or better avoid the impact of the malicious second networking unit on the Internet of Things network and increase the penalty for the malicious unit, the following steps may be performed: setting a threshold All second reputation scores below The first credit rating R made by the networking unit Dj Through the above solution, the present application can better achieve the result of evaluating the global reputation of the first unit, and the global reputation can also be uploaded to the cloud or uploaded to the roadside and broadcast to the entire network, so as to meet the needs of the next cycle of reputation calculation.

[0088] In such Figure 4 In the embodiment shown, a global reputation evaluation system based on the Internet of Things is also introduced.

[0089] It includes a first networking unit 41, a plurality of second networking units 42 and a plurality of third networking units 43. The first networking unit forms an Internet of Things network with the second networking unit and the third networking unit.

[0090] In an update cycle, all second and third networking units that request to interact with the first networking unit independently perform a first reputation score on the first networking unit, and the first reputation score performed by each is positively correlated with the interaction time and interaction quality of the first networking unit's requests to interact with each other networking unit;

[0091] The third networking unit is used to calculate a second reputation score, where the second reputation score is: a normalized weighted average score of the first reputation score of the first networking unit assigned by the third networking unit and the first reputation score of the first networking unit assigned by the second networking unit during the update cycle; the third networking unit is also used to broadcast the second reputation score in the Internet of Things network for other networking units to use in performing the first reputation score on the first networking unit during the next update cycle.

[0092] Through the above system design, all second networking units and third networking units that request to interact with the first networking unit independently perform a first reputation score on the first networking unit, which can better achieve a global reputation evaluation in the weighted computing network and solve the technical effect of quantifying the quality of interaction between the first networking unit in the Internet of Things network and other global networking units.

[0093] In some embodiments of the present application, the second reputation score:

[0094]

[0095] Among them, β is the global reputation evaluation weight, R D (u) is the first reputation evaluation of the third network unit on the first network unit, J is the total number of second network units that effectively interact with the first network unit, R Dj The second network unit in the update period T makes a first reputation evaluation of the first network unit based on its own interaction with the first network unit. The third network unit is a centralized node or cloud server. The credibility of the reputation evaluation of the second networking unit.

[0096] Different from the existing technology, the above technical solution obtains the first reputation by evaluating the interaction between the sender of the interaction request and the recipient in the Internet of Things network, and then obtains the global reputation evaluation in the network by weighting the first reputation scores of more than two networked units. It can solve the technical effect of quantifying the interaction quality of the first networked unit in the Internet of Things network with other global networked units.

[0097] exist Figure 5 In the embodiment shown, this solution also introduces a mutual trust evaluation method for an Internet of Things network, comprising the following steps:

[0098] S51: Before establishing an interaction request, the first networking unit and the second networking unit obtain a third reputation rating of the first networking unit. The third reputation rating includes a weighted average of the first networking unit's second reputation rating and a reputation age score. The second reputation rating is the normalized weighted average of the first reputation ratings of all networking units in the IoT network that requested interaction with the first networking unit during the previous update cycle. The first reputation rating is positively correlated with the interaction time and quality of the first networking unit's requests for interaction with other networking units. The reputation age score is positively correlated with the number of consecutive update cycles in which the first networking unit's second reputation score exceeds a second reputation threshold. As previously explained, the second reputation can be announced to the IoT network by the third networking unit or a cloud server. The second reputation can reflect the first networking unit's past interactions with other networking units in the IoT network. The reputation age is accumulated by continuously maintaining a reputation above the second reputation threshold in the IoT network. The second reputation threshold can be automatically set based on actual conditions. By invoking the second reputation information of the first networking unit, the above technical solution can form an expectation of the quality of cooperation between other networking units before sending requests to the first networking unit, and calculate the third reputation rating, which can also be called expected reputation. This achieves the effect of quantifying the expected value of service interaction between each networking unit in the Internet of Things network.

[0099] To better penalize networked units with unsatisfactory interaction results, step S53 is further executed: when the third reputation rating of the first networked unit is less than the third threshold, the first networked unit and the second networked unit do not establish an interaction request. At this point, although the first networked unit may still belong to the same IoT network as the second networked unit through other means, the interaction request between the two will be unable to proceed due to the expected reputation not being met. This is the penalty mechanism for the first networked unit. Through this penalty mechanism, the consumption of computing resources within the IoT network can be reduced. At the same time, it can encourage networked units with low reputation ratings to change their own interaction strategies, thereby promoting the generation and dissemination of cooperative behavior within the IoT network.

[0100] In some embodiments of the present application, a time decay function ψ(m) is further included.

[0101] The third reputation evaluation is further adjusted by the time decay function to obtain a historical reputation evaluation

[0102]

[0103] m is the current cycle and the second historical credit score γ(u m) is the number of update cycles between each cycle. As m increases, ψ(m) decreases. By designing a decay function, the utility weight of the second reputation score in the latest cycle can be increased, allowing some punished first-network units to "reform" more quickly and participate in mutual assistance services.

[0104] The value of the attenuation function can generally be 1 when m=0. The attenuation function only needs to be designed so that ψ(m) decreases as m increases. This application does not limit this. In some embodiments of this application,

[0105]

[0106] k is the decay constant. Designed as an exponential function, it can reduce the impact of historical evaluations on current credit ratings.

[0107] In some embodiments of the present application, the reputation age score

[0108]

[0109] Among them, Y max is the maximum age limit, γ(u) is the current global reputation score, and g is the adjustment constant. The reputation age score is designed to accumulate by continuously providing a reputation score above the second reputation threshold in the IoT. This setting enables individuals who consistently and stably provide interactive services to receive a better and more stable reputation age score, thereby achieving a better and more stable expected reputation.

[0110] In some embodiments of the present application, the third credibility evaluation also includes a weighted average of the IoT network attribute correlation scores of the first networking unit and the second networking unit. IoT network attributes include whether they belong to the same IoT subnetwork, whether they belong to the same network usage attributes, such as whether the network is mainly used for entertainment purposes, road condition information sharing purposes, autonomous driving purposes, etc. The more identical attributes they have, the more likely it is that the two belong to the same social network. Therefore, the higher the network attribute correlation score is set. By setting the IoT network attribute correlation, the expected credibility of two networking units in the same social network before requesting interaction can be improved, thereby better establishing interaction.

[0111] In some embodiments of the present application, the IoT network attribute correlation score

[0112]

[0113] Among them, γ C1 Adjust the value of the IoT network attribute correlation. The above design can improve the expected reputation of two networked units in the same social network before requesting interaction, thereby better establishing interaction.

[0114] In some embodiments of the present application, the third reputation evaluation further includes a weighted average of the social attribute tag association scores of the first networking unit and the second networking unit, wherein the social attribute tag association score

[0115]

[0116] Among them, γ C2 is the social attribute label association adjustment value. SP is the social attribute label association cluster. A social attribute label is a label assigned by a reputable authority to an individual's service behavior with a high level of contribution after integrating the interactions of all individuals in the group with the individual. Different service categories correspond to different label descriptions. For example: Traffic safety is SVR1, traffic management is SVR2, traffic information is SVR3, traffic efficiency is SVR4, business entertainment is SVR5, collaborative services is SVR6, and so on. For example: if the cumulative number of traffic management and collaborative services provided by the individual during all interactions within the evaluation period exceeds the assignable threshold, the social attribute label SP = {SVR2, SVR6} is assigned. When the service request type of the service requester is consistent with its social attribute label, it indicates that the expected return level for providing this type of service is high, that is, the service requester is likely to provide this type of service to other individuals in the Internet of Vehicles group in the future. The above design can improve the expected reputation of two networked units with the same social attribute label before requesting an interaction, thereby better establishing interaction.

[0117] In such Figure 6 In the embodiment shown, a mutual trust evaluation system for an Internet of Things network is also introduced.

[0118] The system includes a first networking unit 61 and a second networking unit 62 in an IoT network. Before establishing an interaction request, the first and second networking units obtain a third reputation rating of the first networking unit. The third reputation rating includes a weighted average of the first networking unit's second reputation rating and a reputation age score. The second reputation score is the normalized weighted average of the first reputation scores of all networking units in the IoT network that requested interaction with the first networking unit during the previous update cycle. The first reputation score is positively correlated with the interaction time and quality of the first networking unit's interaction requests with other networking units. The reputation age score is positively correlated with the number of consecutive update cycles in which the first networking unit's second reputation score exceeds a second reputation threshold. When the first networking unit's third reputation rating is less than the third threshold, the first and second networking units do not establish an interaction request. By invoking the first networking unit's second reputation information, the system can calculate a third reputation rating, also known as an expected reputation, based on expectations regarding the quality of cooperation between other networking units when sending requests to the first networking unit. This quantifies the expected value of service interactions between networking units in the IoT network.

[0119] In some embodiments of the present application, the third reputation evaluation

[0120]

[0121] where ω i For different evaluation dimensions F i (u i ) weights, different evaluation dimensions F i (u i )include:

[0122] Historical credit rating

[0123]

[0124] m is the current cycle and the second historical credit score γ(u m ) interval,

[0125]

[0126] k is the decay constant;

[0127] Credit Age Score

[0128]

[0129] Among them, Y max is the maximum age limit, γ(u) is the current global credit rating, and g is the adjustment constant;

[0130] IoT network attribute correlation score

[0131]

[0132] Among them, γ C1 Adjust the value of the IoT network attribute correlation;

[0133] Social attribute label association score

[0134]

[0135] Among them, γ C2 Adjusts the value of social attribute tag relevance.

[0136] Unlike existing technologies, the above system, by invoking the second reputation information of the first networking unit, can form corresponding expectations about the quality of cooperation between other networking units when sending requests to the first networking unit. This system quantifies the expected value of service interactions between networking units in the IoT network by comprehensively considering four aspects: historical reputation evaluation, reputation age, IoT network attributes, and social attribute tags. This effectively reduces interaction costs and computing resources in the IoT network.

[0137] In other Figure 7 The flowchart of a method for evaluating the utility of individual mutual assistance in an Internet of Things network is shown, which is applicable to an Internet of Things network and includes a first networking unit and a second networking unit. The method includes the following steps: S71: when providing services to the first networking unit, the second networking unit obtains a third reputation evaluation of the first networking unit. The third reputation evaluation includes a weighted average of the second reputation evaluation of the first networking unit and a reputation age score. The reputation age score is positively correlated with the number of consecutive update cycles in which the second reputation score of the first networking unit exceeds a second reputation threshold.

[0138] The second reputation score is: a normalized weighted average score of the first reputation score of the first networking unit by all networking units in the IoT network that requested interactions with the first networking unit in the previous update cycle, wherein the first reputation score is positively correlated with the interaction time and interaction quality of the first networking unit's requests for interactions with other networking units;

[0139] S73: The second networking unit provides differentiated tiered services according to the distribution of the third reputation evaluation value of the first networking unit relative to the second reputation evaluations of all networking units in the Internet of Things network.

[0140] According to the above introduction, the second reputation can be calculated by the centralized node or cloud server in the previous cycle and broadcast to the Internet of Things network. Each participating unit has its own second reputation value. Therefore, the distribution of the second reputation evaluation of all networked units in the Internet of Things network can be obtained. Therefore, the present application can provide differentiated services by comparing the second reputation level of the second networked unit with the first networked unit in the entire Internet of Things network when establishing interaction. It can save the computing resources and network resources of the second networked unit, thereby comprehensively improving the cooperation efficiency of each networked unit in the Internet of Things network and improving the information interaction level of the Internet of Things network.

[0141] In some embodiments of the present application, the differentiated services include information transmission rate differences, response rate differences, response rate differences, or response priority differences. The transmission rate difference may refer to the high and low bandwidth capacity of information transmission, the response rate difference may refer to the delay from receiving information to processing information, the response rate difference may refer to the probability of returning valid information after receiving an interaction request, that is, even if an interaction request is received, it may be ignored by the recipient, and the response priority refers to the preset resources giving priority to responding to interaction requests of higher-priority networking units. Designing these differentiated services can ensure that high-quality networking units can obtain higher-quality services, thereby improving the overall interaction level of the Internet of Things network.

[0142] The specific difference grading method can be set up by technical personnel according to actual conditions. In some embodiments of the present application, when the third reputation evaluation value of the first networking unit is in the top 25%, 25%-50%, 50%-75%, and 75%-100% of the distribution percentage of the second reputation evaluation value of all networking units in the Internet of Things network, the second networking unit provides the first networking unit with successively lower response priority, response rate, response rate, and transmission rate. The design of a percentage ladder grading method is simpler than a gradually decreasing grading method.

[0143] In order to better calculate the reputation age, some embodiments of the present application also propose the following scheme: when the update period T arrives, the reputation age is updated based on the comparison between the current individual's global reputation evaluation and its historical value.

[0144] Specifically, when the change in the individual's third reputation evaluation Δγ=|γ n -γ n-1 |, greater than the fourth threshold V γ When , the age value returns to the initial value 1, otherwise:

[0145] Determine whether the credit age is equal to the upper limit value Y max If it is, it remains unchanged. If it is not, the credit age Y will increase by 1. The specific expression is as follows:

[0146]

[0147] n is the number of cycles. Through the above method, network units that continuously provide effective interactive services can obtain better credit age evaluation, which helps to maintain the stability and effectiveness of the Internet of Things network.

[0148] Others such as Figure 8 In the embodiment shown, a system for evaluating individual mutual aid utility in an Internet of Things network is presented. The system includes a first networking unit 81 and a second networking unit 82 in the Internet of Things network.

[0149] The second networking unit, when providing services to the first networking unit, obtains a third reputation evaluation of the first networking unit, the third reputation evaluation comprising a weighted average of the second reputation evaluation and a reputation age score of the first networking unit, the reputation age score being positively correlated to the number of consecutive update cycles in which the second reputation score of the first networking unit exceeds a second reputation threshold. The second networking unit is further configured to provide differentiated tiered services to the first networking unit based on a distribution of the third reputation evaluation value of the first networking unit relative to the second reputation evaluations of all networking units in the Internet of Things network;

[0150] The second reputation score is: the normalized weighted average score of the first reputation score of the first networking unit by all networking units in the Internet of Things network that requested interaction with the first networking unit in the previous update cycle, and the first reputation score is positively correlated with the interaction time and interaction quality of the first networking unit's requests for interaction with other networking units.

[0151] The above-mentioned system can provide differentiated services by comparing the third credibility level of the second networking unit in the entire Internet of Things network when establishing interaction with the first networking unit, which can save the computing resources and network resources of the second networking unit, thereby comprehensively improving the cooperation efficiency of each networking unit in the Internet of Things network and improving the information interaction level of the Internet of Things network.

[0152] In some embodiments of the present application, the differentiated service includes information transmission rate difference, response rate difference, response rate difference or response priority difference.

[0153] In some embodiments of the present application, the second networking unit is used to provide the first networking unit with a response priority, response rate, response rate and transmission rate that decreases in sequence when the distribution percentage of the third credibility evaluation value of the first networking unit relative to the third credibility evaluation of all networking units in the Internet of Things network is in the top 25%, 25%-50%, 50%-75%, and 75%-100%.

[0154] In some embodiments of the present application, Figure 8 As shown, it includes a central node 83, which is used to update the reputation age based on the comparison between the individual's global reputation evaluation and its historical value when the update period T arrives, and broadcast it in the Internet of Things network.

[0155] Specifically:

[0156] When the change in the individual's third reputation evaluation Δγ=|γ n -γ n-1 |, greater than the fourth threshold V γ When , the age value returns to the initial value 1, otherwise:

[0157] Determine whether the credit age is equal to the upper limit value Y max If it is, it remains unchanged. If it is not, the credit age Y will increase by 1. The specific expression is as follows:

[0158]

[0159] n is the number of cycles.

[0160] In other comprehensive embodiments, this application uses the Internet of Vehicles (IoV) as an example. The IoV reputation management mechanism includes three components: participants, behavioral guidelines, and social paradigms. IoV reputation management participants include reputable authoritative organizations and reputable individuals, forming a hierarchical reputation management framework. The behavioral guidelines refer to the principles by which reputable individuals determine their behavior based on the reputation of their interacting partners, with different principles applying to different participants. The social paradigm refers to the criteria for comprehensively evaluating the reputation of different participants in IoV interactions.

[0161] Participants make decisions based on their own behavioral codes. Based on their behavioral codes, participants can be categorized into three types: cooperative, reciprocal, and selfish. The specific behavioral codes for these three types are as follows: cooperative individuals respond to the needs of all requesting individuals, adopting an unconditional cooperation strategy and not making decisions based on the reputation of their counterparts; reciprocal individuals adopt a conditional cooperation strategy and make decisions based on the reputation of their counterparts; and selfish individuals disregard the reputation of their counterparts, refusing to provide services and only seeking services from them.

[0162] IoV reputation is a judgment based on the trust demonstrated by an individual's interactive behaviors within the IoV network. It is a comprehensive assessment of the trust demonstrated by all of that individual's interactions. In this invention, an incentive mechanism based on social indirect reciprocity is employed. Therefore, IoV reputation reflects the level of cooperation of participating individuals, as well as their participation and contribution to IoV system activities. In this invention, reputation is categorized into three types: global reputation, direct reputation, and expected reputation. Global reputation is a unique value at a given moment, representing a comprehensive assessment of all of an individual's interactions at that moment. Global reputation is determined by a reputation authority. Direct reputation is the assessment of the individual's performance during the interaction process by other individuals who directly interact with the individual. Direct reputation is aggregated according to global reputation evaluation rules to form global reputation. Expected reputation is an individual's assessment of the expected reputation of the service interaction partner. Even individuals with the same current global reputation may have different expectations of their performance after interaction due to differences in their environment and individual attributes. This means that the interaction partner's expected contribution to the IoV network may vary. Expected reputation reflects the expected benefit judgment of the interaction. When the expected benefit is insufficient, the responder will reject the service request and punish selfish individuals with low reputation in the Internet of Vehicles, promote selfish individuals to change their own interaction strategies, and encourage the generation and spread of cooperative behavior.

[0163] Hierarchical reputation management is primarily divided into three levels: reputation authority center, reputation authority nodes, and reputation participants. The reputation authority serves as the reputation authority center, responsible for member registration, identity acquisition, global reputation evaluation, trust level adjustment, IoV service control, and the assignment of individual social attribute tags for IoV participants. It occupies a core position in the reputation management architecture and is trusted. Reputation authority nodes, such as IoV roadside intelligent terminals (RSUs), are responsible for interactive behavior collection, reputation evidence collection, reputation edge computing, and IoV service distribution. They serve as distributed authority nodes in the reputation management architecture and are trusted. Reputation participants are intelligent entities participating in the IoV, primarily smart cars comprised of vehicles, onboard units, and systems. They are responsible for generating and uploading reputation evidence, performing direct reputation evaluation, expected reputation evaluation, and making trust decisions. The reputation of a trusted individual is the maximum value of its reputation, and its trust is 1.

[0164] Services in the Internet of Vehicles (IoV) are categorized into multiple categories, such as traffic safety, traffic management, traffic information, traffic efficiency, commercial entertainment, and collaborative services. Specific services include vehicle sensor information sharing, platooning, road emergency broadcasting, efficient speed guidance, priority vehicle access management, road traffic information sharing, and multimedia and entertainment information push. Service models include one-way service, request-response, and publish-subscribe. Both the request-response and publish-subscribe models are bidirectional interaction models. After a requester initiates a request, the responder provides the corresponding service based on the service decision and the corresponding service requirements. For example, in a request-response service, a requester requests sensor information from surrounding vehicles. After the responder makes a service decision, the responder shares road condition information based on the service request. In the request-response model, the responder immediately provides information sharing within the requested time period. In the publish-subscribe model, the responder pushes road condition information to the subscribing requester as updates are published. In a one-way service model, the service provider proactively provides services to the recipient without the recipient's request. For example, a traffic emergency broadcast service involves a vehicle imminently striking an animal that has strayed into the roadway. This vehicle immediately applies emergency braking and broadcasts the emergency to surrounding vehicles via V2V (Vehicle-to-Vehicle) technology, preventing rear-end collisions and ensuring road safety. As authoritative nodes serve as data collection and aggregation nodes, they provide and manage services related to traffic safety, traffic efficiency, and collaborative services within the Internet of Vehicles. Individuals can access comprehensive road condition information, green wave speed guidance, and priority vehicle access from these nodes. Authoritative nodes also play a management role in service scenarios such as priority vehicle access, platooning, and traffic management. When authoritative nodes assume this management role and request individual participation and services, individuals who refuse will receive a reputation penalty.

[0165] like Figure 9 The architecture shown is a schematic diagram of a two-way interaction scenario. After the requester initiates an interaction request to the responder, the responder evaluates the requester's expected reputation based on a multi-factor evaluation method. Based on this evaluation, the responder makes a trust decision, providing or denying the service. After receiving a response indicating service provision or denial, the requester directly evaluates the responder's reputation during this interaction. Behavior 2 represents a one-way service model. The service provider proactively pushes a service, and after receiving the service, the service recipient directly evaluates the service provider's reputation based on this behavior. Global reputation evaluation is a comprehensive assessment of individual reputations by a reputation authority based on direct reputation evaluations between participating individuals, evidence from these evaluations, and individual age attributes.

[0166] The expected reputation evaluation adopts a multi-factor evaluation method. Each factor is assigned a different weight based on its impact on reputation, as shown below: Among them, ω i are the weights corresponding to different reputation influencing factors, and It is the mathematical expression of each influencing factor. The main influencing factors include: global reputation, historical reputation, reputation age, social network connection attributes, social attribute label description, etc.

[0167] Historical reputation mainly considers the impact of historical global reputation. Therefore, the factors affecting historical reputation are calculated as follows:

[0168]

[0169] Among them, γ(u m ) is the historical global reputation evaluation value. ψ(m) is the time decay function, which can be taken as is a decay constant, defined by the system itself, that controls the decay rate and regulates the impact of historical global reputation values ​​on expected reputation. m is the number of intervals between the current global reputation evaluation and the update cycle of historical reputation evaluations. For example, the m value for a global reputation evaluation within the current update week is 0, the m value for a global reputation evaluation within the previous update cycle is 1, the m value for a global reputation evaluation within the previous two update cycles is 2, and so on. A larger value of m results in a smaller value for the time decay function, meaning that older historical reputation evaluations have less impact on current expected reputation.

[0170] The impact of reputation age on expected reputation is reflected in the fact that the older the individual is, the longer the individual maintains the current contribution level. The impact of age is calculated and summarized as follows:

[0171]

[0172] Among them, Y max is the maximum age limit, γ(u) is the current global reputation evaluation, and g is an adjustment constant, which is defined by the system and adjusts the impact of age factors on expected reputation.

[0173] The social network connection attribute records the dynamic social trust network that the interactive opponent is connected to. Its impact on expected reputation is reflected in the fact that when the interactive opponent and the party belong to the same dynamic social trust network, the probability of receiving corresponding feedback for providing services is very high, thus improving expected reputation. The influencing factors of the social network connection attribute are calculated as follows:

[0174]

[0175] Among them, γ C1 Adjust the reputation value for the social network connection attribute.

[0176] The social attribute label is a label that a reputable authority organization makes to an individual's service behavior with a high contribution level after integrating the interaction behaviors of all individuals in the group with the individual. Different service categories correspond to different label descriptions. For example: traffic safety is SVR1, traffic management is SVR2, traffic information is SVR3, traffic efficiency is SVR4, business entertainment is SVR5, collaborative service is SVR6, and so on. For example: during the entire interaction process of the individual within the evaluation period, when the cumulative number of times the traffic management and collaborative service categories are provided exceeds the assignable threshold, the social attribute label SP = {SVR2, SVR6) is assigned to the individual. When the service request type of the service requester is consistent with its social attribute label, it means that the expected return level for providing this type of service is high, that is, the service requester is likely to provide this type of service to other individuals in the Internet of Vehicles group in the future. The influencing factors of the social attribute label are calculated and summarized as follows:

[0177]

[0178] Among them, γ C2 Adjust the value of the social attribute tag reputation.

[0179] Direct reputation evaluation is made by the service recipient to the service provider in direct interaction. Direct reputation evaluation adjusts reputation by the degree of deviation of the interactive behavior, that is, whether the performance of the behavior can be rewarded with reputation compared with the standard interactive behavior defined by the system. After a single interactive behavior is completed, the evaluation is made immediately. Considering the practical significance of the reputation evaluation of the Internet of Vehicles, when the Internet of Vehicles individuals with high reputation perform well, their reputation value changes less; on the other hand, when individuals with high reputation do not maintain cooperative behavior, that is, the interaction strategy mutation node, their reputation evaluation should be adjusted significantly. Therefore, the reputation evaluation utility function η(u) is introduced. The specific expression is as follows:

[0180]

[0181] Among them, t D ,q D The interaction time and interaction quality values ​​of standard interaction behaviors are published by reputable authoritative centers based on the distribution of overall group interaction behaviors. D1 , γ D2 is the reputation adjustment value of the interactive behavior, which is a constant value and is adjusted according to the evolution of the group. d ,q d The interaction time and interaction quality values ​​for each interaction behavior. The interaction quality value is derived by normalizing the fuzzy description of the interaction quality evaluation. For example, using a five-point scale, interaction quality is divided into five levels, from satisfying to not satisfying, with corresponding scores of 1-5. After obtaining the evaluation, the score is converted into a numerical value as follows.

[0182] Five-point evaluation value Corresponding evaluation Interaction Quality Value 1 Totally dissatisfied -1 2 Dissatisfied -0.5 3 Basic satisfaction 0.3 4 satisfy 0.5 5 Completely satisfied 1

[0183] η(u) is the utility function, which can be an exponential utility function, a logarithmic utility function, a binomial utility function, etc.

[0184] For example: The exponential utility function is as follows

[0185]

[0186] Among them, δ is the adjustment constant of the utility function. When it increases, the impact of interactive behavior on reputation is aggravated. max The current maximum reputation value published by the reputation center based on the global reputation distribution of the group.

[0187] The reputation authority center's global reputation evaluation is a comprehensive assessment of participating individuals, taking into account all interactions within the IoV community. Since the reputation authority center also manages IoV-related services and considers interactions between reputation authority nodes as direct interactions with the center, it must consider both direct and recommended reputation evaluations. Because global reputation evaluation requires integrating mutual evaluation data between individuals within the community and taking into account differences in data collection and transmission latency, the global reputation update cycle T can be customized by the system and adjusted based on the current system evolution. Short update cycles accelerate the convergence of reputation evaluation consistency. As the system evolves toward a global equilibrium state, the reputations of individuals within the system stabilize, and the update cycle T can be appropriately increased to conserve system resources.

[0188] The reputation authority center's global reputation evaluation of participating individuals is expressed as follows:

[0189]

[0190] Among them, β is the global reputation evaluation weight. D (u) is the direct reputation evaluation of the individual by the reputation center. J is the total number of individuals who participated in the direct reputation evaluation of the individual, that is, the total number of individuals who interacted with the individual and made effective direct reputation evaluations within the update period T. R Dj It is the direct reputation evaluation made by the participating individuals on other individuals within the update period T based on their own interactions with other individuals, with reference to the individual reputation evaluation. The credibility of direct reputation evaluation for individuals is determined by the reputation authority node, which assesses the reputation of each participating individual when collecting reputation evaluation evidence. When there are individuals with low credibility in the group, the rejection behavior of high credibility individuals should not be punished by credibility. However, according to the direct credibility evaluation rule, the rejection behavior will uniformly lower the credibility evaluation. Therefore, the credibility evaluation of low credibility individuals should be filtered out to prevent selfish individuals from affecting the evolution of the group. Refer to the trust level division to determine the value and set the threshold at the same time All direct reputation evaluations made by individuals whose credibility is lower than the threshold are deemed invalid and are removed. Since global reputation evaluation affects the reputation update of individuals, it has a wide impact on the reputation judgment and service decision-making of the Internet of Vehicles group. Therefore, global reputation evaluation supports asynchronous update, that is, when the change of individual reputation ΔR(u) exceeds the update threshold V R When , the global reputation evaluation of the individual is updated immediately, otherwise wait for the update period T. When a new individual joins, the reputation authority center assigns it a default initial reputation value γ0, which is the median of the current reputation distribution in the system.

[0191] Trust evaluation of participating individuals is based on behavioral criteria. Based on their behavioral strategies, IoV participants can be divided into three categories: cooperative individuals, reciprocal individuals, and selfish individuals. Cooperative individuals adopt an unconditional cooperative strategy, responding to all service requests from all requesters. Therefore, cooperative individuals have unconditional trust in all participating individuals, meaning their trust in their interacting counterparts is 1. Selfish individuals adopt a selfish rejection strategy, disregarding the other party's reputation and rejecting all service requests, seeking only resources and services from the IoV system. Consequently, their trust in their interacting counterparts is 0.

[0192] To implement the social indirect reciprocity incentive mechanism, suppress the refusal of selfish individuals in the IoV community to cooperate, and encourage the emergence, maintenance, and spread of indirect reciprocity-based cooperative behavior within the community, reciprocal individuals use the reputation of their counterparts as the basis for differentiating services, judging the counterpart's contribution to the evolution of the IoV community and determining the corresponding services to provide. Reciprocal individuals' service decisions are based on the global reputation distribution of participating individuals within the IoV community, and are graded based on the trust of their counterparts, as shown below:

[0193]

[0194] Wherein, the trust level A={a1, a2, ..., a N-1 , t N}, is an ordered segmentation class. The credit cutoff point b1 of the trust classification, b t ,...,b N-1 , b NThe reputable authority center is determined based on the overall distribution of reputation within the current group, that is, the dividing point is the corresponding quantile. For example, if the trust level is divided into 4 levels, b1, b2, and b3 correspond to the 25th, 50th, and 75th percentiles of the reputation distribution respectively.

[0195] In order to accelerate the cooperative evolution of the Internet of Vehicles group, the individual reputation age evolution rule is established. When the update cycle T arrives, the reputation authority center confirms whether the individual's reputation age Y is updated based on the comparison between the individual's global reputation evaluation and its historical value. When the individual's global reputation age changes from the historical value by Δγ = |γ n -γ n-1 |, whose value is greater than the credit age growth change threshold V γ When the age value returns to the initial value 1, otherwise the credit age Y will increase by 1 naturally; when the age value increases to the upper limit value Y max After that, it is considered as an evolutionary mature individual, and its age does not increase any more. The specific expression is as follows:

[0196]

[0197] Based on the theory of spatial reciprocity, the network structure between individuals participating in the Internet of Vehicles is conducive to the maintenance and dissemination of cooperative behavior. Reciprocal individuals have the ability to independently choose social connections to adjust the network structure. By establishing a dynamic social trust network to isolate selfish individuals, it is effective to resist selfish individuals from encroaching on resources and engaging in malicious behavior. At the same time, in certain services of the Internet of Vehicles, such as vehicle platooning, efficient traffic speed guidance, priority vehicle traffic security management and other services rely on continuous and effective trust connection relationships. Therefore, establishing a dynamic social trust network will help improve the service benefits of the Internet of Vehicles group and resist the invasion of selfish individuals. The connection establishment in the social trust network is based on the evaluation of expected reputation, that is, considering the expected contribution level that the Internet of Vehicles group may obtain after establishing a connection with the individual, a connection is established when the expected reputation is greater than the threshold. After the connection is established, the expected reputation of the connection is the lower value of the expected reputation evaluated by both parties, that is, R e(i，j) =min(R ei , R ej Expected reputation evaluation follows the same multi-factor evaluation method as previously described. After a connection is established, information such as the individual's social network connection attributes is updated, disseminated within the group, and uploaded to the reputation evaluation authority node. When a connection's expected reputation falls below the connection threshold, the connection is disconnected. This enhances trust between individuals within the connected social network, incentivizing the maintenance and spread of cooperative behavior.

[0198] This application further describes a specific example process for individual interactions. After a service requester initiates a service request from a service responder, the service responder generates an individual reputation evaluation of the service requester based on a multi-factor individual reputation evaluation method and relevant reputation evidence data. The responder then assesses the trustworthiness of the requester based on its own service behavior decision criteria and the overall reputation distribution published by the authority, providing the corresponding service level. After providing the corresponding service, the requester uploads the reputation data generated and reputation evidence, including the interaction process, to a reputation authority. After receiving a service response, the service requester generates an individual reputation evaluation of the responder's interaction and then uploads the updated individual reputation evaluation and reputation evidence to the reputation authority. Reputation data refers to the individual reputation evaluation value generated after the interaction. Reputation evidence refers to the behavioral information of both parties during the interaction, including the specific service request information of the service requester, the decision made by the service responder to reject or trust the service, information on the service provided, the quality evaluation of the service interaction, and the corresponding time of the service interaction.

[0199] When participating individuals interact directly with a reputation authority node, the authority node will perform an individual reputation evaluation based on the direct reputation evaluation method. Participating individuals upload the reputation data and evidence generated during the interaction to the reputation authority node. After collecting the relevant reputation data and evidence, the node makes an assessment. When the reputation of some individuals undergoes a sudden change, exceeding a threshold, the node submits the data to the reputation authority center, which promptly updates the global reputation of the mutated individuals. Within the update cycle T, the authority center comprehensively evaluates the collected reputation data and determines the global reputation of the group. It also publishes the reputation distribution to facilitate service tiering decisions for individuals within the group. Furthermore, the authority evaluates changes in individual reputation and updates and publishes the individual's reputation age.

[0200] like Figure 10 In the embodiment shown, the process of establishing a dynamic social trust network is also demonstrated. After participant 1, participant 2, participant 3, and participant 4 form a dynamic social network, participant 5 only needs to establish a social connection with any of the members to join the network. Participant 5 initiates a connection request to participant 1, a member of the network. Participant 1 evaluates the expected reputation of the individual based on the individual's reputation, age, social attribute label description, and other reputation evidence. When the expected reputation is greater than the connection establishment threshold, the connection is allowed to be established. After the connection is established, the expected reputation of the connection is the lower value of the expected reputation evaluated by both parties, that is, R e(i，j) =min(R ei , R ej). The expected reputation evaluation refers to the aforementioned multi-factor evaluation method. At the same time, both parties update the individual's social network connection attribute information and share the connection establishment information with other members in the network. When the network connection structure changes, the corresponding information is uploaded to the reputation authority as reputation evidence. At the same time, after the change information is propagated among members within the network, each member adjusts the expected reputation of the connection between each member based on the connection situation to monitor whether the connection is valid. During the process of network structure change, if an individual's individual reputation evaluation suddenly changes due to the establishment of a new connection and exceeds the update threshold, the reputation authority will update the corresponding global reputation evaluation. When the reputation of participating individual 5 is low, it is difficult to join the network, it is impossible to establish a strong social relationship connection, and selfish behavior is difficult to obtain a good service return. Therefore, the selfish behavior of selfish individuals in the group will be suppressed, and the formation and maintenance of cooperative behavior will be promoted. At the same time, after the update period T, network members will evaluate the expected reputation of each connection established internally. When the expected reputation value is lower than the connection threshold, the connection will be disconnected.

[0201] After the dynamic social trust network is established, some participating individuals will occupy an important position in the network. For example Figure 10 Participating individual 3 can forward information from the central authority node, or perhaps because they have the highest number of established interactions. After achieving significant status, if individual 3 changes their service strategy and becomes selfish, then due to the established social network, they may still receive high-level services during the update cycle. Consequently, selfish behavior can spread within the network, prompting members to "free ride" on services and resources while rejecting them. In this situation, the reputation authority node should focus on sudden changes in the reputation of network members. After their service behavior decision-making criteria change, their interaction behavior and reputation ratings will likely undergo a sudden change in the short term. The reputation authority node immediately reports individual reputation changes to the central authority node, asynchronously updating the global reputation ratings of some individuals in a timely manner to curb the occurrence and spread of selfish behavior.

[0202] It should be noted that, in this document, relational terms such as first and second, etc., are used solely to distinguish one entity or operation from another, and do not necessarily require or imply any actual relationship or order between these entities or operations. Furthermore, the terms "include," "comprise," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or terminal device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. Without further limitation, elements defined by the phrase "include..." or "comprising..." do not exclude the presence of additional elements in the process, method, article, or terminal device comprising the elements. Furthermore, in this document, "greater than," "less than," "exceeding," etc., are understood to exclude the number itself; "above," "below," "within," etc., are understood to include the number itself.

[0203] Those skilled in the art will appreciate that the above embodiments may be provided as methods, devices, or computer program products. These embodiments may take the form of fully hardware embodiments, fully software embodiments, or embodiments combining software and hardware. All or part of the steps in the methods involved in the above embodiments may be completed by instructing the relevant hardware through a program, and the program may be stored in a storage medium readable by a computer device for executing all or part of the steps described in the methods of the above embodiments. The computer device includes, but is not limited to, personal computers, servers, general-purpose computers, special-purpose computers, network devices, embedded devices, programmable devices, smart mobile terminals, smart home devices, wearable smart devices, in-vehicle smart devices, etc.; the storage medium includes, but is not limited to, RAM, ROM, magnetic disks, magnetic tapes, optical disks, flash memory, USB flash drives, mobile hard disks, memory cards, memory sticks, network server storage, network cloud storage, etc.

[0204] The above embodiments are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a computer device to generate a machine, so that the instructions executed by the processor of the computer device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0205] These computer program instructions can also be stored in a computer device readable memory that can guide a computer device to work in a specific manner, so that the instructions stored in the computer device readable memory produce a product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0206] These computer program instructions can also be loaded onto a computer device so that a series of operating steps are executed on the computer device to produce a computer-implemented process, whereby the instructions executed on the computer device provide for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0207] Although the above embodiments have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concepts. Therefore, the above descriptions are merely embodiments of the present invention and do not limit the scope of patent protection of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the scope of patent protection of the present invention.

Claims

1. A global reputation evaluation method based on the Internet of Things, characterized in that: In the Internet of Things network, including a first networking unit, a plurality of second networking units and a plurality of third networking units, The method includes the following steps: in an update cycle, all second and third networking units that request to interact with a first networking unit independently perform a first reputation score on the first networking unit; when the first networking unit chooses to cooperate with interaction requests from other networking units, the first reputation score performed by each of the second and third networking units is positively correlated with the interaction time and interaction quality of the first networking unit's requests to interact with the other networking units; Calculating a second reputation score, where the second reputation score is: a normalized weighted average score of the first reputation score of the first networking unit assigned by the third networking unit to the first networking unit and the first reputation score of the first networking unit assigned by the second networking unit to the first networking unit during the update period; Broadcasting the second reputation score in the Internet of Things network for other networked units to perform the first reputation score on the first networked unit in the next update cycle; The update period is determined according to a data transmission delay in the Internet of Things network and a variance of the first reputation score of each unit.

2. The global reputation evaluation method based on the Internet of Things according to claim 1 is characterized in that: The third networking unit is a centralized node, and the weight of the third networking unit is greater than that of the second networking unit.

3. The global reputation evaluation method based on the Internet of Things according to claim 1, characterized in that: The second reputation score: ； in, is the global reputation evaluation weight, is the first reputation evaluation of the third network unit on the first network unit, J is the total number of second network units that effectively interact with the first network unit, The first reputation evaluation of the first networking unit made by the second networking unit within the update period T based on the interaction between the second networking unit and the first networking unit.

4. The global reputation evaluation method based on the Internet of Things according to claim 3 is characterized in that: Also includes the credibility of the first reputation evaluation made by the jth second networking unit , The second reputation score 。 5. The global reputation evaluation method based on the Internet of Things according to claim 4 is characterized in that: described The credit score is determined according to the second credit score of the j-th second networking unit in the previous cycle.

6. The global reputation evaluation method based on the Internet of Things according to claim 4 is characterized in that: 。 7. The global reputation evaluation method based on the Internet of Things according to claim 4 is characterized in that: Setting thresholds , all second reputation scores are lower than The first credit evaluation made by the networking unit The results were eliminated.

8. A global reputation evaluation system based on the Internet of Things, characterized by: It includes a first networking unit, a plurality of second networking units and a plurality of third networking units, wherein the first networking unit forms an Internet of Things network with the second networking unit and the third networking unit. During an update cycle, all second and third networking units that request interaction with the first networking unit independently perform a first reputation score on the first networking unit. When the first networking unit chooses to cooperate with the interaction requests of the other networking units, the first reputation score performed by each of them is positively correlated with the interaction time and interaction quality of the first networking unit's interaction requests with the other networking units. The third networking unit is used to calculate a second reputation score, where the second reputation score is a normalized weighted average score of the first reputation score of the first networking unit assigned to the third networking unit and the first reputation score of the first networking unit assigned to the second networking unit during the update period; The third networking unit is further configured to broadcast the second reputation score in the Internet of Things network, so that other networking units can perform the first reputation score on the first networking unit in the next update cycle; The update period is determined according to a data transmission delay in the Internet of Things network and a variance of the first reputation score of each unit.

9. The global reputation evaluation system based on the Internet of Things according to claim 8, characterized in that: The second reputation score: ； in, is the global reputation evaluation weight, is the first reputation evaluation of the third network unit on the first network unit, J is the total number of second network units that effectively interact with the first network unit, The first reputation evaluation of the first networking unit made by the second networking unit within the update period T based on the interaction between the second networking unit and the first networking unit.

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