A method for determining a routing path, a node, a terminal and a storage medium

Abstract

Embodiments of the present application disclose a method for determining a routing path, a node, a terminal and a storage medium. A first node is not a head node. The first node determines an indirect trust value corresponding to a neighboring node based on trust evidence of the neighboring node. The trust evidence is obtained based on traffic information of the neighboring node. The neighboring node includes at least one. A final trust value of the first node at a current time is determined according to the indirect trust value. The final trust value is sent to a second node, so that the second node predicts a predicted trust value of the first node at a next time according to the final trust value, and determines the routing path based on the predicted trust value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of network security, and in particular to a routing path determination method, a node, a terminal and a storage medium. BACKGROUND

[0002] Mobile Ad Hoc Network is a distributed collection of wireless nodes that can work without any centralized management or fixed network infrastructure. In this network, nodes can move freely, and nodes within radio range can communicate with each other, while nodes outside the radio range need to forward packets through intermediate nodes to achieve communication. However, compared with traditional networks, nodes in mobile ad hoc networks are more vulnerable to attacks due to weak connectivity, resource constraints and limited physical protection of mobile nodes. Therefore, security is the primary problem of mobile ad hoc networks.

[0003] In mobile ad hoc networks, the overall network security can be further improved through trust management. In the prior art, methods for implementing trust management in mobile ad hoc networks mainly include a trust judgment technology for predicting results of a Long Short-Term Memory (LSTM) model and a self-recurrence model trust calculation technology of a subjective logic framework. However, the machine learning technology of the LSTM model has high requirements for training data and requires large training resources. The self-recurrence model trust calculation technology completely calculates the trust value of a node based on existing information, and the information collected in actual applications is often incomplete, resulting in deviations in the calculation results. Therefore, the existing trust routing path determination methods generally have poor calculation effects. SUMMARY

[0004] The present application provides a routing path determination method, a node, a terminal and a storage medium, which can effectively improve the calculation effect of trusted routing, and the reliability and security of the routing path.

[0005] The technical solution of the present application is as follows:

[0006] In a first aspect, the present application provides a routing path determination method applied to a first node, wherein the first node is not a head node; the method comprises the following steps:

[0007] determining an indirect trust value corresponding to a neighboring node based on trust evidence of the neighboring node, wherein the trust evidence is obtained based on traffic information of the neighboring node; and the neighboring node includes at least one of the following:

[0008] According to the indirect trust value, a final trust value of the first node at a current time is determined, the final trust value is sent to a second node, so that the second node predicts a predicted trust value of the first node at a next time according to the final trust value, and determines a routing path based on the predicted trust value.

[0009] In a second aspect, an embodiment of the present application provides a method for determining a routing path, applied to a second node, the second node being a head node in a same cluster as at least one first node; the method comprising:

[0010] obtaining final trust values of the at least one first node at a current time respectively sent by the at least one first node;

[0011] predicting predicted trust values respectively corresponding to the at least one first node based on the final trust values and a prediction calculation model; wherein the predicted trust value is a prediction of a trust value of the first node at a next time;

[0012] determining a target node from the at least one first node based on the predicted trust value, and determining a routing path based on the target node.

[0013] In a third aspect, an embodiment of the present application provides a first node, the first node comprising a first determination unit and a first sending unit,

[0014] the first determination unit is configured to determine an indirect trust value corresponding to a neighboring node based on trust evidence of the neighboring node; wherein the trust evidence is obtained based on traffic information of the neighboring node; the neighboring node comprises at least one;

[0015] the first sending unit is configured to determine a final trust value of the first node at a current time according to the indirect trust value, and send the final trust value to a second node, so that the second node predicts a predicted trust value of the first node at a next time according to the final trust value, and determines a routing path based on the predicted trust value.

[0016] In a fourth aspect, an embodiment of the present application provides a second node, the second node comprising an obtaining unit, a prediction unit and a second determination unit,

[0017] the obtaining unit is configured to obtain final trust values of the at least one first node at a current time respectively sent by the at least one first node;

[0018] the prediction unit is configured to predict predicted trust values respectively corresponding to the at least one first node based on the final trust values and a prediction calculation model; wherein the predicted trust value is a prediction of a trust value of the first node at a next time;

[0019] The second determining unit is configured to determine a target node from the at least one first node based on the predicted trust value, and determine a routing path based on the target node.

[0020] In a fifth aspect, an embodiment of the present application provides a first terminal, wherein the first terminal runs the first node as described in the third aspect, and the first terminal comprises a first processor and a first memory storing instructions executable by the first processor, and when the instructions are executed by the first processor, the method as described in the first aspect is implemented.

[0021] In a sixth aspect, an embodiment of the present application provides a second terminal, wherein the second terminal runs the second node as described in the fourth aspect, and the terminal comprises a second processor and a second memory storing instructions executable by the second processor, and when the instructions are executed by the second processor, the method as described in the second aspect is implemented.

[0022] In a seventh aspect, an embodiment of the present application provides a computer readable storage medium, which stores a program, and the program is applied to the first terminal and the second terminal, and when the program is executed by the first processor, the method as described in the first aspect is implemented; and when the program is executed by the second processor, the method as described in the second aspect is implemented.

[0023] The embodiment of the present application provides a routing path determination method, a node, a terminal and a storage medium. A first node determines an indirect trust value corresponding to a neighboring node based on trust evidence of the neighboring node; wherein the trust evidence is obtained based on traffic information of the neighboring node; wherein the neighboring node comprises at least one; a final trust value of the first node at a current time is determined according to the indirect trust value, and the final trust value is sent to a second node, so that the second node predicts a predicted trust value of the first node at a next time according to the final trust value, and determines a routing path based on the predicted trust value. The second node obtains the final trust value of the current time sent by at least one first node respectively; a predicted trust value corresponding to at least one first node is predicted based on the final trust value and a prediction calculation model; wherein the predicted trust value is a prediction of the trust value of the first node at the next time; a target node is determined from the at least one first node based on the predicted trust value, and a routing path is determined based on the target node. That is, in the present application, the first node which is a non-head node in the cluster can determine the indirect trust value according to the trust evidence of the neighboring node, and then determine the final trust value at the current time according to the indirect trust value, the second node which is a head node in the cluster can predict the trust value of the first node at the next time according to the final trust value and the prediction calculation model by receiving the final trust value of the first node, and finally determine the routing path according to the predicted trust value of the first node; therefore, it can be seen that the final trust value is determined in combination with the indirect trust value corresponding to the neighboring node, which can effectively improve the accuracy of the final trust value, and then the predicted trust value of the first node at the next time is predicted based on the prediction calculation model and the final trust value, which can more accurately predict the node trust value, realize the trust measurement of the node, and effectively improve the calculation effect of the trusted routing; the routing path determined according to the predicted trust value can effectively improve the reliability and security of the routing path. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 Implementation process diagram of the routing path determination method provided by the embodiment of the present application Figure One ;

[0025] Figure 2 Implementation process diagram of the routing path determination method provided by the embodiment of the present application Figure Two ;

[0026] Figure 3 Implementation process diagram of the routing path determination method provided by the embodiment of the present application Figure Three ;

[0027] Figure 4 Implementation process diagram of the routing path determination method provided by the embodiment of the present application Figure Four ;

[0028] Figure 5A schematic diagram of a component structure of a first node according to an embodiment of the present application is shown in FIG. 1.

[0029] Figure 6 A schematic diagram of a component structure of a second node according to an embodiment of the present application is shown in FIG. 2.

[0030] Figure 7 A schematic diagram of a component structure of a first terminal according to an embodiment of the present application is shown in FIG. 3.

[0031] Figure 8 A schematic diagram of a component structure of a second terminal according to an embodiment of the present application is shown in FIG. 4. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. It can be understood that the specific embodiments described herein are only used to explain the related application, and not to limit the application. In addition, it should be noted that, for the convenience of description, only the parts related to the application are shown in the drawings.

[0033] Mobile Ad hoc Networks are distributed collections of wireless nodes that can operate without any centralized management or fixed network infrastructure. In this network, nodes can move arbitrarily at any given time. Therefore, the topology of the network and the interconnection between nodes are fast and unpredictable. In a mobile Ad hoc network, nodes within radio range can communicate with each other immediately, while nodes not within radio range of each other need to forward packets at intermediate nodes, relaying from the source node to the destination node, thus achieving communication. Therefore, nodes in a mobile Ad hoc network must cooperate with each other for multi-hop communication. However, due to the weak connectivity of mobile nodes, resource constraints and limited physical protection, nodes in a mobile Ad hoc network are more vulnerable to attacks than in traditional networks. Therefore, security is the primary problem of mobile Ad hoc networks.

[0034] A single cryptographic mechanism cannot detect or completely prevent all attacks that pose a security threat to the network. In order to ensure the availability of nodes and provide secure end-to-end communication between them, the trustworthiness of individual nodes and trust management of multiple nodes are effective complements to cryptographic mechanisms, that is, the overall network security can be further improved through trust management. In a mobile Ad hoc network, cryptographic mechanisms and trust management can work together to improve overall network security.

[0035] The existing trust decision framework of mobile Internet of Things mainly includes a trusted judgment technology based on LSTM model prediction results and a trust calculation technology based on subjective logic framework autoregressive model. For example, the LSTM seq2seq model is used to predict the behavior data of the node at the next moment, the difference value between the predicted value and the actual operation behavior data is calculated according to the actually collected operation behavior data, and it is judged whether the user behavior is trusted according to the difference value, so as to ensure the behavior of the Internet of Things device is trusted and solve the security risk of being attacked and utilized in the Internet of Things. The subjective logic model and the autoregressive model (Autoregressive Model) can be combined to calculate the direct trust value of the node. At the same time, the indirect trust value between nodes is calculated by the propagation distance factor. The combination of direct trust and indirect trust can effectively avoid malicious node attacks.

[0036] However, the existing technology still has some problems, for example, the machine learning technology of the LSTM model has high requirements for training data and requires large training resources, and cannot be calculated in parallel, and the calculation efficiency is low when the model network is deep; the trust value calculation scheme based on subjective logic and autoregressive model completely calculates the trust value of the node based on the existing information, and the information collected in the actual application is often incomplete, and the calculation result will be biased; and the autoregressive model and the LSTM model cannot completely and accurately describe all the characteristics of the node trust data; the autoregressive model only considers the self-correlation of the data, and the variance of the interference term in the model is set to a constant value, without considering the sequence correlation of the conditional heteroscedastic sequence, which does not conform to the actual situation; the LSTM model can better fit the long memory characteristics of time series data, but it cannot accurately predict data with aggregation and burst characteristics. Therefore, the existing trust calculation method generally has the problem of poor calculation effect.

[0037] To solve the problems in the prior art, the embodiments of the present application provide a method for determining a routing path, a node, a terminal and a storage medium. A first node determines an indirect trust value corresponding to a neighboring node based on trust evidence of the neighboring node, wherein the trust evidence is obtained based on traffic information of the neighboring node, and the neighboring node includes at least one. The first node determines a final trust value of the first node at a current time according to the indirect trust value, and sends the final trust value to a second node, so that the second node predicts a predicted trust value of the first node at a next time according to the final trust value, and determines a routing path based on the predicted trust value. The second node is a head node in a same cluster as the at least one first node. The second node obtains the final trust value of the at least one first node at the current time. The second node predicts a predicted trust value corresponding to the at least one first node based on the final trust value and a prediction calculation model. The predicted trust value is a prediction of the trust value of the first node at the next time. The second node determines a target node from the at least one first node based on the predicted trust value, and determines the routing path based on the target node. The method can effectively improve the calculation effect of trusted routing, and further improve the reliability and security of the determined routing path.

[0038] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

[0039] Embodiment one

[0040] The embodiments of the present application provide a method for determining a routing path, which is applied to a first node. Figure 1 The implementation process of the method for determining a routing path proposed in the embodiments of the present application is shown in Figure One As shown in Figure 1 The method for determining a routing path of the first node can include the following steps:

[0041] In step 101, an indirect trust value corresponding to a neighboring node is determined based on trust evidence of the neighboring node, wherein the trust evidence is obtained based on traffic information of the neighboring node, and the neighboring node includes at least one.

[0042] In the embodiments of the present application, the first node can determine an indirect trust value corresponding to a neighboring node based on trust evidence of the neighboring node, wherein the trust evidence is obtained based on traffic information of the neighboring node, and the neighboring node includes at least one.

[0043] It should be noted that in the embodiments of the present application, in a cluster or a cluster, a plurality of nodes can be included, and the plurality of nodes include a head node, i.e., a cluster head (CH) in the cluster; the cluster head in each cluster is generated by random election, and the cluster head can initiate trusted computing, can be responsible for trust propagation, and can establish a route from the source to the destination; the first node is a node in the cluster or the cluster, and is not a head node or a cluster head.

[0044] Further, in the embodiments of the present application, the adjacent node refers to the adjacent node corresponding to the first node, and the adjacent node includes at least one; the first node determines the trust evidence based on the traffic information of the adjacent node, and then determines the indirect trust value of the adjacent node according to the trust evidence.

[0045] For example, in the embodiments of the present application, the first node is node A, and the adjacent node of node A is node B; node A determines the trust evidence of node B based on the traffic information from node B to node A, and then determines the indirect trust value of node B according to the trust evidence of node B.

[0046] It should be noted that in the embodiments of the present application, the indirect trust value can be determined by collecting the trust evidence in a preset time, specifically, the first node can obtain the traffic information of the adjacent node in the preset time, and then perform statistical processing on the network parameters according to the traffic information to obtain the trust evidence; and the indirect trust value is predicted based on the trust evidence and the first calculation model.

[0047] Further, in the embodiments of the present application, the first node can periodically perform indirect trust value calculation, i.e., periodically collect the trust evidence in a preset time, so that the determined indirect trust value can represent the trustworthiness of the adjacent node of the first node at the current time after the expiration of the preset time.

[0048] Step 102, determining the final trust value of the first node at the current time according to the indirect trust value, and sending the final trust value to the second node, so that the second node predicts the predicted trust value of the first node at the next time according to the final trust value, and determines the routing path based on the predicted trust value.

[0049] In the embodiments of the present application, after the first node determines the indirect trust value corresponding to the adjacent node based on the trust evidence of the adjacent node, the first node can determine the final trust value of the first node at the current time according to the indirect trust value, and send the final trust value to the second node, so that the second node predicts the predicted trust value of the first node at the next time according to the final trust value, and determines the routing path based on the predicted trust value.

[0050] It should be noted that in the embodiments of the present application, the final trust value is the trust value of the first node at the current time, and the final trust value represents the trust degree of the first node at the current time.

[0051] Specifically, in the embodiments of the present application, the method for the first node to determine the final trust value of the first node at the current time according to the indirect trust value can include: calculating an arithmetic mean value according to the indirect trust values corresponding to each adjacent node, and taking the arithmetic mean value as the final trust value; or, determining a weight parameter corresponding to the indirect trust value of each adjacent node; and then calculating a weighted average value according to each indirect trust value and the weight parameter corresponding to each indirect trust value, and taking the weighted average value as the final trust value of the first node at the current time.

[0052] For example, in the embodiments of the present application, the first node is node A, and the adjacent nodes of node A include node B, node C and node D; then node A can calculate an arithmetic mean value according to the indirect trust value of node B, the indirect trust value of node C and the indirect trust value of node D to obtain the final trust value; or, because there are differences in distance, traffic information, etc. between node B, node C, node D and node A, different weight parameters can be respectively assigned to the indirect trust value of node B, the indirect trust value of node C and the indirect trust value of node D 、 and , so as to calculate a weighted average value according to the indirect trust value of node B, the indirect trust value of node C, the indirect trust value of node D 、 and , to obtain the final trust value of node A at the current time.

[0053] Further, in the embodiments of the present application, the first node needs to send the final trust value representing the trust degree of the first node at the current time to the second node, and the second node can use the final trust value to make a prediction to obtain a predicted trust value of the first node at the next time, and determine a routing path based on the predicted trust value, so that a routing path that can guarantee reliability in the future period of time can be selected, and the security of routing is improved.

[0054] It can be understood that in the embodiments of the present application, the predicted trust value can represent the trust degree of the first node at the next time.

[0055] It should be noted that in the embodiments of the present application, the next time can be a specific time or a period of time.

[0056] It can also be understood that in the embodiments of the present application, the routing path is the determined path from the source end to the destination end for data transmission.

[0057] It should be noted that in the embodiments of the present application, the second node can obtain the final trust value of the current time respectively sent by the at least one first node; and then predict the prediction trust value respectively corresponding to the at least one first node based on the final trust value and a prediction calculation model; wherein the prediction trust value is a prediction of the trust value of the next time corresponding to the first node; so as to determine the target node from the at least one first node based on the prediction trust value, and determine the routing path based on the target node; wherein the prediction calculation model is constructed based on an autoregressive moving average model (ARMA Model) and a generalized autoregressive conditional heteroskedasticity model (GARCH Model); wherein the generalized autoregressive conditional heteroskedasticity model can adopt an optimized GARCH model (Markov Regime-Switching Zero-Drift EGARCH, MRS-ZD-EGARCH).

[0058] Further, in the embodiments of the present application, after the second node determines the routing path, if the determined routing path contains the first node, the first node can also perform monitoring processing on the routing path at a preset time interval to obtain a link result; if the link result is a routing path failure, the link result is sent to the second node, so that the second node cancels the routing path according to the link result. That is, in the embodiments of the present application, each node on the routing path will periodically monitor the link, and when the routing path fails, for example, due to node movement, the second node can cancel the routing path.

[0059] Further, in the embodiments of the present application, the method for the first node to determine the indirect trust value corresponding to the adjacent node based on the trust evidence of the adjacent node, i.e., the method proposed in step 101, can include the following steps:

[0060] Step 101a, obtaining the traffic information of the adjacent node within a preset time, and performing statistical processing on the network parameters according to the traffic information to obtain the trust evidence; wherein the trust evidence is the statistical quantity of different network parameters.

[0061] In the embodiments of the present application, the first node determines the indirect trust value corresponding to the adjacent node based on the trust evidence of the adjacent node. Specifically, the first node can first obtain the traffic information of the adjacent node within a preset time, and perform statistical processing on the network parameters according to the traffic information to obtain the trust evidence; wherein the trust evidence is the statistical quantity of different network parameters.

[0062] It should be noted that in the embodiments of the present application, the traffic information can be traffic information between the adjacent node and the first node; the first node can obtain the trust evidence by collecting the traffic information of the adjacent node within a preset time, and performing statistical processing on the network parameters according to the traffic information, and the size of the preset time is not limited in the present application.

[0063] For example, in the embodiments of the present application, the first node is any node A in the cluster, node B is an adjacent node of node A, node A obtains the traffic information of node B to node A within a preset time through monitoring, and then performs statistical processing on the network parameters according to the traffic information, so as to obtain the trust evidence of node B within the preset time. .

[0064] It should be noted that in the embodiments of the present application, the trust evidence is the statistical number of different network parameters obtained by performing statistical processing on the traffic information. The different network parameters include at least two of the correctly forwarded data packets, the discarded data packets and the error injected data packets.

[0065] Further, in the embodiments of the present application, the trust evidence can include but is not limited to the number of correctly forwarded data packets, the number of discarded data packets and the number of error injected data packets.

[0066] It can be understood that in the embodiments of the present application, since the adjacent node can be at least one, the first node can obtain the traffic information of multiple adjacent nodes, and perform statistical processing according to the traffic information of the multiple adjacent nodes, to obtain the trust evidence corresponding to each of the multiple adjacent nodes.

[0067] Step 101b, determining the prior probability corresponding to each network parameter according to the trust evidence.

[0068] In the embodiments of the present application, after the first node obtains the traffic information of the adjacent node within a preset time, and performs statistical processing on the network parameters according to the traffic information to obtain the trust evidence, the first node can determine the prior probability corresponding to each network parameter according to the trust evidence.

[0069] It should be noted that in the embodiments of the present application, the Bayesian decision theory model can be used to predict the indirect trust value, and the Bayesian decision theory model needs to be based on the prior probability for prediction; the present application determines the prior probability corresponding to each network parameter according to the trust evidence, so as to predict the indirect trust value by using the Bayesian decision theory model and the prior probability.

[0070] Step 101c, predicting the prior probability corresponding to each network parameter by using the first calculation model to obtain the indirect trust value.​​

[0071] In the embodiments of the present application, after determining the prior probability corresponding to each network parameter according to the trust evidence, the first node can utilize the first calculation model to predict the prior probability corresponding to each network parameter to obtain the indirect trust value.

[0072] It should be noted that in the embodiments of the present application, the first calculation model is used to predict the indirect trust value; for example, the first calculation model can be a Bayesian decision theory model based on beta distribution, which can be expressed as the following formula:

[0073] (1)

[0074] For example, corresponding to the embodiments of the present application, represents the prior probability determined according to the trust evidence; is the posterior probability, that is, the indirect trust value obtained after prediction.

[0075] For example, in the embodiments of the present application, the expectation of the indirect trust value can also be calculated, which can be expressed as the following formula:

[0076] (2)

[0077] wherein, and It can be understood that and in the trust evidence of the adjacent node B of the first node are the cumulative numbers of all positive (good) and negative (bad) behaviors; in the initial stage, since the adjacent node B has not been monitored or observed by the node A, and can be assigned according to the resources and importance of the node B, etc. For example, the initial values of and can be set to 0.5. and The initial values of and can be assigned different initial values; for example, the initial values of and can be set to 0.5.

[0078] ​​In summary, in the embodiments of the present application, firstly, based on the collected trust evidence, the indirect trust value of the node is calculated by using the Bayesian decision theory, which effectively combines the prior knowledge and the posterior information, and the calculation is more accurate. Secondly, the indirect trust value of the neighbor node is collected, and the final trust value is synthesized, which can accurately quantify the trust score. Finally, the method of using the autoregressive moving average model combined with the generalized conditional heteroscedasticity model is used to predict the trust degree of the node at the next moment, which considers the autocorrelation and burst aggregation characteristics in the trust data; the present application combines the ARMA model and the MRS-ZD-EGARCH model, uses their respective advantages, and makes up for the poor prediction effect of the GARCH model on the changing data; at the same time, by using the ARMA+MRS-ZD-EGARCH model and combining the final trust value, the next moment prediction trust value of each first node can be predicted one or more steps in advance, and the trust is accurately quantified, so as to better realize the trust measurement of the node. Therefore, the nodes reliable in the future period of time are selected to form a routing path, which on the one hand improves the security of the routing, and on the other hand can reduce the relative amount of calculation, in addition, the model has small demand for data quantity, is lightweight in calculation, and has flexibility and accuracy in managing the trust of the Internet of Things-mobile ad hoc network.

[0079] The embodiment of the application provides a routing path determination method, a node, a terminal and a storage medium, a first node determines an indirect trust value corresponding to a neighboring node based on trust evidence of the neighboring node; wherein the trust evidence is obtained based on traffic information of the neighboring node; wherein the neighboring node comprises at least one; according to the indirect trust value, a final trust value of the first node at a current time is determined, and the final trust value is sent to a second node, so that the second node predicts a predicted trust value of the first node at a next time according to the final trust value, and determines a routing path based on the predicted trust value. The second node obtains the final trust value of the current time sent by at least one first node respectively; based on the final trust value and a prediction calculation model, a predicted trust value corresponding to the at least one first node respectively is predicted; wherein the predicted trust value is a prediction of the trust value of the first node at the next time; based on the predicted trust value, a target node is determined from the at least one first node, and a routing path is determined based on the target node. That is, in the application, the first node which is not a head node in the cluster can determine the indirect trust value according to the trust evidence of the neighboring node, and then determine the final trust value at the current time according to the indirect trust value, the second node which is the head node in the cluster can predict the trust value of the first node at the next time according to the final trust value and the prediction calculation model by receiving the final trust value of the first node, and finally determine the routing path according to the predicted trust value of the first node; it can be seen that, in the application, the final trust value is determined in combination with the indirect trust value corresponding to the neighboring node, which can effectively improve the accuracy of the final trust value, and then the predicted trust value of the first node at the next time is obtained based on the prediction calculation model and the final trust value, which can more accurately predict the node trust value, realize the trust measurement of the node, and effectively improve the calculation effect of the trusted routing; the routing path determined according to the predicted trust value can effectively improve the reliability and security of the routing path.

[0080] Embodiment two

[0081] Based on the above embodiment, in another embodiment of the application, the embodiment of the application provides a routing path determination method, applied to a second node; the second node is a head node in a same cluster as at least one first node. Figure 2 The implementation process of the routing path determination method provided in the embodiment of the application is shown Figure Two As shown in Figure 2 The routing path determination method of the second node can include the following steps:

[0082] Step 201, obtaining the final trust value of the current time sent by at least one first node respectively.

[0083] In the embodiment of the application, the second node can obtain the final trust value of the current time sent by at least one first node respectively.

[0084] It should be noted that in the embodiments of the present application, the final trust value is the trust value of the first node at the current time, and the final trust value represents the trust degree of the first node at the current time.

[0085] It can be understood that in the embodiments of the present application, the second node and the first node belong to the same cluster, the second node is the head node in the cluster, and the first node includes at least one, so that the second node can obtain the final trust value of the current time sent by each of the at least one first node, to make subsequent prediction of the predicted trust value of the at least one first node.

[0086] Step 202, predicting the predicted trust value corresponding to each of the at least one first node based on the final trust value and a prediction calculation model; wherein the predicted trust value is a prediction of the trust value of the next time corresponding to the first node.

[0087] In the embodiments of the present application, after the second node obtains the final trust value of the current time sent by each of the at least one first node, the second node can predict the predicted trust value corresponding to each of the at least one first node based on the final trust value and a prediction calculation model; wherein the predicted trust value is a prediction of the trust value of the next time corresponding to the first node.

[0088] It should be noted that in the embodiments of the present application, the prediction calculation model is constructed based on an autoregressive moving average model and a generalized conditional heteroscedasticity model; the generalized conditional heteroscedasticity model can use an optimized GARCH model, i.e. MRS-ZD-EGARCH.

[0089] For example, in the embodiments of the present application, the autoregressive moving average model ARMA(p, q) can be represented by the following formula:

[0090] (3)

[0091] wherein, is a white noise sequence, i.e. a sequence of independent and identically distributed random variables with a mean of 0 and a variance of 1; and are non-negative integers; , is an autoregressive parameter; represents the prediction result, i.e. the predicted trust value in the embodiments of the present application; in the embodiments of the present application, in order to facilitate calculation, a first-order ARMA model is used for prediction, and the first-order ARMA model can be represented by the following formula:

[0092] (4)

[0093] Further, the conditional mean of the prediction trust value calculated by the ARMA model of the first order can be:

[0094] (5)

[0095] The prediction error variance can be:

[0096] (6)

[0097] Meanwhile, the ARMA model assumes that the variance of the time series is constant, that is .

[0098] It should be noted that in the embodiments of the present application, since the ARMA model uses a white noise sequence, however, the real prediction trust value time series is often a heteroscedastic time series; therefore, in order to estimate the heteroscedastic time series independent of the trust evidence set, that is, the self-evidence set, the MRS-ZD-GARCH model is used to estimate the heteroscedastic time series, which requires the mean to be 0 and the variance to be .

[0099] For example, in the embodiments of the present application, the first-order MRS-ZD-GARCH model is used to estimate the heteroscedastic time series, which can be represented by the following formula:

[0100] (7)

[0101] That is, in the embodiments of the present application, the variance is fitted by the first-order MRS-ZD-GARCH model, and the heteroscedastic time series is estimated; thus, the prediction trust value is obtained by using the first-order ARMA model and the heteroscedastic time series for prediction. The MRS-ZD-GARCH model can solve the problem of changing volatility over time, better cope with structural changes in data, and achieve good prediction results.

[0102] Specifically, in the embodiments of the present application, the method for predicting the prediction trust value corresponding to each of the at least one first node based on the final trust value and the prediction calculation model can include: estimating the heteroscedastic time series by using the first-order generalized conditional heteroscedasticity model; and further predicting the prediction trust value based on the first-order autoregressive moving average model, the heteroscedastic time series and the final trust value; wherein the coefficients of the first-order generalized conditional heteroscedasticity model and the first-order autoregressive moving average model are determined according to the maximum likelihood estimation method.

[0103] Step 203, determining a target node from the at least one first node based on the prediction trust value, and determining a routing path based on the target node.

[0104] In an embodiment of the present application, after predicting the respective predicted trust value of each of the at least one first node based on the final trust value and the prediction calculation model, the second node can determine a target node from the at least one first node based on the predicted trust value, and determine the routing path based on the target node.

[0105] It should be noted that, in an embodiment of the present application, the target node refers to a node on the routing path; that is, the second node determines the target node from the at least one first node, and then determines the routing path according to the target node.

[0106] Further, in an embodiment of the present application, when determining the routing path, the source end and the destination end need to be determined first, and then the target node is determined from the at least one first node, so as to determine the routing path from the source end to the destination end according to the target node.

[0107] Further, in an embodiment of the present application, Figure 3 The implementation flow of the method for determining the routing path proposed in the embodiment of the present application is shown in Figure Three As shown in Figure 3 , after predicting the respective predicted trust value of each of the at least one first node based on the final trust value and the prediction calculation model, that is, after step 202, the second node can further include the following steps:

[0108] Step 204, determining the confidence interval of the predicted trust value.

[0109] In an embodiment of the present application, after predicting the respective predicted trust value of each of the at least one first node based on the final trust value and the prediction calculation model, the second node can determine the confidence interval of the predicted trust value.

[0110] It should be noted that, in an embodiment of the present application, the corresponding confidence interval can be determined according to the predicted trust value; and then, combined with the confidence interval, the predicted trust value can be estimated to the maximum extent, so as to realize more accurate prediction of the node trustworthiness.

[0111] For example, in an embodiment of the present application, the confidence level is , and the confidence interval of the predicted trust value can be expressed as: .

[0112] Step 205, determining the preset trust threshold corresponding to the predicted trust value based on the confidence interval.

[0113] In an embodiment of the present application, after determining the confidence interval of the predicted trust value, the second node can determine the preset trust threshold corresponding to the predicted trust value based on the confidence interval.

[0114] It should be noted that in the embodiments of the present application, the preset trust threshold is used for node classification processing, and then the second node can determine the target node based on the node classification processing result.

[0115] It can be understood that in the embodiments of the present application, the confidence interval of the predicted trust value fluctuates on a certain data segment, and therefore a preset trust threshold can be determined based on the confidence interval, and the selection of the target node by using the preset trust threshold can greatly reduce trust revocation. The specific value of the preset trust threshold is not limited in the present application.

[0116] Further, in the embodiments of the present application, the method for the second node to predict the respective predicted trust values of the at least one first node based on the final trust value and the prediction calculation model, i.e., the method proposed in step 202, can include the following steps:

[0117] Step 202a, estimating the heteroscedastic time series by using a first-order generalized conditional heteroscedasticity model.

[0118] In the embodiments of the present application, the second node predicts the respective predicted trust values of the at least one first node based on the final trust value and the prediction calculation model, and specifically, the second node can first estimate the heteroscedastic time series by using a first-order generalized conditional heteroscedasticity model.

[0119] It should be noted that in the embodiments of the present application, a first-order optimized generalized conditional heteroscedasticity model, i.e., an MRS-ZD-GARCH(1,1) model, can be used to estimate the heteroscedastic time series, and the model is shown in the aforementioned formula (7).

[0120] Step 202b, predicting based on the first-order autoregressive moving average model, the heteroscedastic time series, and the final trust value to obtain the predicted trust value; wherein the coefficients of the first-order generalized conditional heteroscedasticity model and the first-order autoregressive moving average model are determined according to a maximum likelihood estimation method.

[0121] In the embodiments of the present application, after the second node estimates the heteroscedastic time series by using the first-order generalized conditional heteroscedasticity model, the second node can predict based on the first-order autoregressive moving average model, the heteroscedastic time series, and the final trust value to obtain the predicted trust value; wherein the coefficients of the first-order generalized conditional heteroscedasticity model and the first-order autoregressive moving average model are determined according to a maximum likelihood estimation method.

[0122] It should be noted that in the embodiments of the present application, a first-order autoregressive moving average model ARMA(1,1) is used for prediction to obtain the predicted trust value; wherein the ARMA(1,1) model is shown in the aforementioned formula (4).

[0123] Further, in the embodiments of the present application, the maximum likelihood estimation method can be used to determine the coefficients of the MRS-ZD-GARCH(1, 1) model and the ARMA(1, 1) model.

[0124] Further, in the embodiments of the present application, the method for the second node to determine the target node from the at least one first node based on the predicted trust value can include the following steps:

[0125] Step 203a, performing node classification processing on the first nodes corresponding to the predicted trust values to obtain node classification results corresponding to the first nodes.

[0126] In the embodiments of the present application, the second node determines the target node from the at least one first node based on the predicted trust value, specifically, the second node can first perform node classification processing on the first nodes corresponding to the predicted trust values to obtain node classification results corresponding to the first nodes.

[0127] It should be noted that in the embodiments of the present application, the first nodes include at least one, and each first node can be classified by using the predicted trust value corresponding to the first node, thereby obtaining a node classification result corresponding to each first node.

[0128] It should be noted that in the embodiments of the present application, the node classification result can include multiple cases, and the types of the node classification result can be set according to the network deployment scenario and user demand, which is not limited in the present application.

[0129] For example, in some real network deployment scenarios, some inherent properties of the nodes can cause data packet loss, although the actual reason is not easy to be identified, such “bad nodes” of the lost data packets can be regarded as nodes with poor credibility, and after such nodes are classified by using the node classification processing, such nodes can be largely avoided or identified; for example, the types of the node classification result can include “high credibility”, “medium credibility” and “low credibility”, which is not limited in the present application.

[0130] Step 203b, updating the trust states in the node list according to the node classification result to obtain an updated node list; wherein the node list is composed of the at least one first node and the trust states corresponding to the at least one first node.

[0131] In an embodiment of the present application, after the second node performs the node classification processing on the first node corresponding to the predicted trust value and obtains the node classification result corresponding to the first node, the second node can update the trust state in the node list according to the node classification result and obtain an updated node list; wherein the node list is composed of at least one first node and the trust state corresponding to the at least one first node.

[0132] It should be noted that in an embodiment of the present application, the node list is a list stored and maintained by the second node and including the trust states of the member nodes in the cluster; that is, the node list can be composed of at least one first node and the trust state of the at least one first node.

[0133] Further, in an embodiment of the present application, after the node classification result corresponding to the first node is obtained, the trust state in the node list can be updated according to the node classification result and an updated node list can be obtained; that is, the trust state of the first node in the node list is determined according to the node classification result of the first node.

[0134] For example, in an embodiment of the present application, the node classification result of the first node is the trusted node category, and the trust state of the first node in the node list is the trusted node category.

[0135] Further, in an embodiment of the present application, in addition to updating the node list according to the node classification result corresponding to the first node, the second node can also send a message to at least one first node at a preset time interval and update the node list according to the feedback of the at least one first node; wherein the size of the preset time interval is not limited in the present application.

[0136] For example, in an embodiment of the present application, the second node sends a hello message to at least one first node in the cluster at a preset time interval, the at least one first node feeds back to the second node, if the second node does not receive the feedback of one of the first nodes after a preset time, the trust state of this first node in the node list is updated, for example, the trust state is updated to poor trust, thereby obtaining an updated node list.

[0137] Step 203c, determining the target node based on the updated node list.

[0138] In an embodiment of the present application, after the second node updates the trust state in the node list according to the node classification result and obtains an updated node list, the second node can determine the target node based on the updated node list.

[0139] It should be noted that in the embodiments of the present application, when determining the target node, it is necessary to determine based on the updated node list, that is, it is necessary to determine the target node based on the latest trust state corresponding to the first node.

[0140] Specifically, in the embodiments of the present application, the method for the second node to determine the target node based on the updated node list comprises: determining a propagation requirement; wherein the propagation requirement represents a requirement for the trust state of the target node; and then screening the target node from the updated node list according to the propagation requirement.

[0141] Further, in the embodiments of the present application, the method for the second node to perform node classification processing on the first node corresponding thereto according to the predicted trust value to obtain the node classification result corresponding to the first node, that is, the method proposed in step 203a can comprise the following steps:

[0142] Step 203a1, comparing the predicted trust value with a preset trust threshold to obtain a comparison result, and realizing node classification processing.

[0143] In the embodiments of the present application, the second node performs node classification processing on the first node corresponding thereto according to the predicted trust value to obtain the node classification result corresponding to the first node. Specifically, the second node can first compare the predicted trust value with a preset trust threshold to obtain a comparison result, and realize node classification processing.

[0144] It can be understood that in the embodiments of the present application, the preset trust threshold is used for node classification processing; the second node compares the predicted trust value of the first node with the preset trust threshold to obtain a comparison result.

[0145] Step 203a2, if the comparison result is that the predicted trust value is less than the preset trust threshold, the node classification result is a malicious node category.

[0146] In the embodiments of the present application, after the second node compares the predicted trust value with the preset trust threshold to obtain a comparison result and realizes node classification processing, if the comparison result is that the predicted trust value is less than the preset trust threshold, the node classification result is a malicious node category.

[0147] It can be understood that in the embodiments of the present application, if the comparison result is that the predicted trust value is less than the preset trust threshold, the first node can be determined as a malicious node category.

[0148] Step 203a3, if the comparison result is that the predicted trust value is greater than or equal to the preset trust threshold, the node classification result is a trusted node category.

[0149] In the embodiments of the present application, after the second node compares the predicted trust value with the preset trust threshold, obtains a comparison result, and realizes node classification processing, if the comparison result is that the predicted trust value is greater than or equal to the preset trust threshold, the node classification result is the trusted node category.

[0150] It can be understood that, in the embodiments of the present application, if the comparison result is that the predicted trust value of the first node is greater than or equal to the preset trust threshold, the node classification result of the first node can be determined as the trusted node category.

[0151] It should be noted that, in the embodiments of the present application, the malicious node category and the trusted node category are only two possible cases of the node classification result, and the node classification result is not limited to these two categories. The type of the node classification result can be set according to the network deployment scene and user demand, and the present application does not make any limitation.

[0152] For example, in the embodiments of the present application, a trust management module can be set to realize node classification processing. Figure 4 The implementation process of the method for determining a routing path proposed in the embodiments of the present application is shown in the figure Figure Four As shown in the figure Figure 4 The working process of the trust management module can be as follows: first, initialization is performed, and then after the predicted trust value of the first node is obtained, the predicted trust value can be compared with the preset trust threshold. If the predicted trust value is less than the preset trust threshold, the first node can be determined as the malicious node category. If the predicted trust value is greater than or equal to the preset trust threshold, the first node can be determined as the trusted node category.

[0153] Further, in the embodiments of the present application, the method for determining a target node based on the updated node list by the second node, i.e. the method proposed in step 203c, can include the following steps:

[0154] Step 203c1, determining a propagation requirement; wherein the propagation requirement represents a requirement for the trust state of the target node.

[0155] In the embodiments of the present application, the second node determines the target node based on the updated node list, and specifically, the second node can first determine the propagation requirement; wherein the propagation requirement represents a requirement for the trust state of the target node.

[0156] It should be noted that, in the embodiments of the present application, the propagation requirement represents a requirement for the trust state of the target node; that is, when determining a routing path, the requirement for the trust state of the target node in the routing path can be first determined, so as to select the target node according to the propagation requirement.

[0157] Step 203c2, screening target nodes from the updated node list according to the propagation requirement.

[0158] In the embodiments of the present application, after determining the propagation requirement, the second node can screen target nodes from the updated node list according to the propagation requirement.

[0159] It can be understood that in the embodiments of the present application, according to the propagation requirement, the first nodes whose updated trust states in the updated node list meet the propagation requirement are screened from the updated node list based on the updated trust states of the at least one first node in the updated node list, and these first nodes are determined as target nodes.

[0160] For example, in the embodiments of the present application, the propagation requirement is that the trust states of the target nodes are all in the trusted node category, then the first nodes whose trust states are in the trusted node category are screened from the updated node list as target nodes according to the propagation requirement.

[0161] Further, in the embodiments of the present application, the at least one first node includes a first sub-node and a second sub-node; the second sub-node is a node different from the first sub-node in the at least one first node; after the second node predicts the respective prediction trust values of the at least one first node based on the final trust value and the prediction calculation model, i.e., after step 202, the second node can further include the following steps:

[0162] Step 204, sending the prediction trust value corresponding to the second sub-node to the first sub-node, so that the first sub-node updates the trusted neighbor list according to the prediction trust value corresponding to the second sub-node.

[0163] In the embodiments of the present application, after the second node predicts the respective prediction trust values of the at least one first node based on the final trust value and the prediction calculation model, the second node can send the prediction trust value corresponding to the second sub-node to the first sub-node, so that the first sub-node updates the trusted neighbor list according to the prediction trust value corresponding to the second sub-node.

[0164] It should be noted that in the embodiments of the present application, each first node in the cluster can maintain a trusted neighbor list, and each first node can update the trusted neighbor list according to the prediction trust values of other first nodes different from it.

[0165] For example, in the embodiments of the present application, the first nodes include A, B, C, D, and E; A is regarded as a first sub-node, and B, C, D, and E are regarded as second sub-nodes; the second node, i.e., the head node, can send the predicted trust values of B, C, D, and E to A, so that A can update the trusted neighbor list of A according to the predicted trust values of B, C, D, and E; similarly, if B is regarded as a first sub-node, the second node can send the predicted trust values of A, C, D, and E to B, so that B can update the trusted neighbor list of B according to the predicted trust values of A, C, D, and E.

[0166] Further, in the embodiments of the present application, the first nodes can also exchange hello messages regularly to track their neighbor nodes.

[0167] In summary, in the embodiments of the present application, the second node can use the method of combining the autoregressive moving average model with the generalized conditional heteroscedasticity model to predict the trusted degree of the nodes at the next time according to the final trust value, and the autocorrelation and burst aggregation characteristics in the trust data are considered; the ARMA model and the MRS-ZD-EGARCH model are combined in the present application, the advantages of the respective models are utilized, and the defect that the prediction effect of the GARCH model on the change data is poor is compensated; at the same time, the ARMA+MRS-ZD-EGARCH model is utilized, and the final trust value is combined, the predicted trust value of each first node at the next time can be predicted one or more steps in advance, the trust is accurately quantified, and thus the trusted measurement of the nodes is better realized. Therefore, the nodes that are reliable in a future period of time are selected to form a routing path, which improves the security of the routing on the one hand and reduces the relative amount of calculation on the other hand; in addition, the model has a small demand for data amount and is lightweight in calculation, and has flexibility and accuracy in the management of the trust of the Internet of Things-mobile ad hoc network.

[0168] The embodiment of the present application provides a routing path determination method, a node, a terminal and a storage medium. A first node determines an indirect trust value corresponding to a neighboring node based on trust evidence of the neighboring node; wherein the trust evidence is obtained based on traffic information of the neighboring node; wherein the neighboring node comprises at least one; a final trust value of the first node at a current time is determined according to the indirect trust value, and the final trust value is sent to a second node, so that the second node predicts a predicted trust value of the first node at a next time according to the final trust value, and determines a routing path based on the predicted trust value. The second node obtains the final trust value of the current time sent by at least one first node respectively; a predicted trust value corresponding to at least one first node is predicted based on the final trust value and a prediction calculation model; wherein the predicted trust value is a prediction of the trust value of the first node at the next time; a target node is determined from the at least one first node based on the predicted trust value, and a routing path is determined based on the target node. That is, in the present application, the first node which is not a head node in the cluster can determine the indirect trust value according to the trust evidence of the neighboring node, and then determine the final trust value at the current time according to the indirect trust value, the second node which is the head node in the cluster can predict the trust value of the first node at the next time according to the final trust value and the prediction calculation model by receiving the final trust value of the first node, and finally determine the routing path according to the predicted trust value of the first node. Therefore, it can be seen that the final trust value is determined in combination with the indirect trust value corresponding to the neighboring node, which can effectively improve the accuracy of the final trust value, and then the predicted trust value of the first node at the next time is predicted based on the prediction calculation model and the final trust value, which can more accurately predict the node trust value, realize the trust measurement of the node, and effectively improve the calculation effect of the trusted routing. The routing path determined according to the predicted trust value can effectively improve the reliability and security of the routing path.

[0169] Embodiment three

[0170] Based on the above embodiment, in another embodiment of the present application, Figure 5 The first node provided in the embodiment of the present application has a component structure diagram as shown in the figure, Figure 5 The first node 10 provided in the embodiment of the present application can comprise a first determination unit 11, a first sending unit 12 and a monitoring unit 13,

[0171] The first determination unit 11 is configured to determine an indirect trust value corresponding to a neighboring node based on trust evidence of the neighboring node; wherein the trust evidence is obtained based on traffic information of the neighboring node; and the neighboring node comprises at least one.

[0172] The first sending unit 12 is configured to determine a final trust value of the first node at a current time according to the indirect trust value, and send the final trust value to a second node, so that the second node predicts a predicted trust value of the first node at a next time according to the final trust value, and determines a routing path based on the predicted trust value.

[0173] Further, the first determining unit 11 is specifically configured to acquire traffic information of the adjacent nodes within a preset time, and perform statistical processing on network parameters according to the traffic information to obtain the trust evidence, wherein the trust evidence is a statistical quantity of different network parameters, and determine a prior probability corresponding to each network parameter according to the trust evidence, and use a first calculation model to predict the prior probability corresponding to each network parameter to obtain the indirect trust value.

[0174] Further, the first sending unit 12 is specifically configured to calculate an arithmetic mean value according to the indirect trust value corresponding to each adjacent node, and take the arithmetic mean value as the final trust value, or determine a weight parameter corresponding to the indirect trust value of each adjacent node, and calculate a weighted mean value according to each indirect trust value and the weight parameter corresponding to each indirect trust value, and take the weighted mean value as the final trust value of the first node at the current time.

[0175] Further, the first calculation model is a Bayesian decision theory model.

[0176] Further, the different network parameters include at least two of a correctly forwarded data packet, a discarded data packet and an error injected data packet.

[0177] The monitoring unit 13 is configured to perform monitoring processing on the routing path according to a preset time interval if the routing path contains the first node, and obtain a link result.

[0178] Further, the first sending unit 12 is further configured to send the link result to the second node if the link result is a routing path failure, so that the second node cancels the routing path according to the link result.

[0179] Figure 6 A schematic diagram of a composition structure of a second node according to an embodiment of the present application is shown in FIG. 2. Figure 6 As shown in FIG. 2, the second node 20 includes an acquisition unit 21, a prediction unit 22, a second determining unit 23 and a second sending unit 24.

[0180] The acquisition unit 21 is configured to acquire at least one final trust value of a current time sent by each first node.

[0181] The prediction unit 22 is configured to predict a predicted trust value corresponding to each of the first nodes based on the final trust value and a prediction calculation model, wherein the predicted trust value is a prediction of the trust value of the next time corresponding to the first node.

[0182] The second determination unit 23 is configured to determine a target node from the first nodes based on the predicted trust value, and determine a routing path based on the target node.

[0183] Further, the prediction calculation model is constructed based on an autoregressive moving average model and a generalized conditional heteroscedasticity model.

[0184] Further, the second determination unit 23 is further configured to determine a confidence interval of the predicted trust value after the prediction unit 22 predicts the predicted trust value corresponding to each of the first nodes based on the final trust value and the prediction calculation model, and determine a preset trust threshold corresponding to the predicted trust value based on the confidence interval.

[0185] Further, the prediction unit 22 is specifically configured to estimate a heteroscedastic time series by using a first-order generalized conditional heteroscedasticity model, and predict the predicted trust value based on a first-order autoregressive moving average model, the heteroscedastic time series and the final trust value, wherein coefficients of the first-order generalized conditional heteroscedasticity model and the first-order autoregressive moving average model are determined according to a maximum likelihood estimation method.

[0186] Further, the second determination unit 23 is specifically configured to perform node classification processing on the first nodes corresponding to the predicted trust value based on the predicted trust value, to obtain a node classification result corresponding to the first node, and perform updating processing on a trust state in a node list based on the node classification result, to obtain an updated node list, wherein the node list is composed of the first nodes and trust states corresponding to the first nodes, and the target node is determined based on the updated node list.

[0187] Further, the second determination unit 23 is further specifically configured to compare the predicted trust value with a preset trust threshold, to obtain a comparison result, and realize node classification processing, wherein if the comparison result is that the predicted trust value is less than the preset trust threshold, the node classification result is a malicious node category, and if the comparison result is that the predicted trust value is greater than or equal to the preset trust threshold, the node classification result is a trusted node category.

[0188] Further, the second determining unit 23 is also specifically configured to determine a propagation requirement, wherein the propagation requirement represents a requirement for the trust state of the target node; and the target node is filtered out from the updated node list according to the propagation requirement.

[0189] The at least one first node includes a first sub-node and a second sub-node; the second sub-node is a node different from the first sub-node in the at least one first node; and the second sending unit 24 is configured to send the predicted trust value corresponding to the second sub-node to the first sub-node after the prediction unit 22 predicts the predicted trust value corresponding to each of the at least one first node based on the final trust value and a prediction calculation model, so that the first sub-node updates the trusted neighbor list according to the predicted trust value corresponding to the second sub-node.

[0190] Further, the second sending unit 24 is also configured to send a message to the at least one first node at a preset time interval, and update the node list according to the feedback of the at least one first node.

[0191] Figure 7 The first terminal according to the embodiment of the present application is shown in a schematic diagram of a composition structure of the first terminal. The first terminal runs a first node. As shown in the figure, the first terminal 10 according to the embodiment of the present application can further include a first processor 14, a first memory 15 storing instructions executable by the first processor 14, and further include a first communication interface 16 and a first bus 17 for connecting the first processor 14, the first memory 15 and the first communication interface 16. Figure 7

[0192] ​In the embodiments of the present application, the first processor 14 can be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, or a microprocessor. It can be understood that, for different devices, the electronic device used to implement the functions of the processor can also be other devices, and the embodiments of the present application are not limited specifically. The first processor 14 can further include a first memory 15, which can be connected with the first processor 14, wherein the first memory 15 is configured to store executable program codes, the program codes including computer operation instructions, and the first memory 15 can include a high-speed RAM memory and can also include a non-volatile memory, for example, at least two disk memories.

[0193] In the embodiments of the present application, the first bus 17 is configured to connect the first communication interface 16, the first processor 14, and the first memory 15, and enable mutual communication among these devices.

[0194] In the embodiments of the present application, the first memory 15 is configured to store instructions and data.

[0195] Further, in the embodiments of the present application, the first processor 14 is configured to determine an indirect trust value corresponding to a neighboring node based on trust evidence of the neighboring node, wherein the trust evidence is obtained based on traffic information of the neighboring node, and the neighboring node includes at least one of the following: a node in a same network as the first node, a node in a different network as the first node, and a node in a same network as the first node and in a different network as the first node.

[0196] According to the indirect trust value, a final trust value of the first node at a current time is determined, and the final trust value is sent to a second node, so that the second node predicts a predicted trust value of the first node at a next time according to the final trust value, and determines a routing path based on the predicted trust value.

[0197] In practical applications, the first memory 15 can be a volatile memory, such as a Random-Access Memory (RAM), or a non-volatile memory, such as a Read-Only Memory (ROM), a flash memory, a Hard Disk Drive (HDD) or a Solid-State Drive (SSD), or a combination of the above kinds of memories, and provides instructions and data to the first processor 14.

[0198] Figure 8 The second terminal according to the embodiments of the present application has a structure as shown in FIG. 2. The second terminal 20 according to the embodiments of the present application can further include a second processor 25, a second memory 26 storing executable instructions of the second processor 25, and a second communication interface 27. The second terminal 20 can further include a second bus 28 for connecting the second processor 25, the second memory 26 and the second communication interface 27. Figure 8

[0199] In the embodiments of the present application, the second processor 25 can be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, or a microprocessor. It can be understood that, for different devices, the electronic device for implementing the above processor functions can also be other, and the embodiments of the present application are not limited specifically. The second processor 25 can further include a second memory 26, which can be connected with the second processor 25. The second memory 26 is used for storing executable program codes, and the program codes include computer operation instructions. The second memory 26 can include a high-speed RAM memory, and can also include a non-volatile memory, such as at least two disk memories.

[0200] ​In the embodiments of the present application, the second bus 28 is used to connect the second communication interface 27, the second processor 25 and the second memory 26 and the mutual communication among these devices.

[0201] In the embodiments of the present application, the second memory 26 is used to store instructions and data.

[0202] Further, in the embodiments of the present application, the second processor 25 is used to acquire the final trust value of each of the at least one first node at the current time point.

[0203] Based on the final trust value and a prediction calculation model, a prediction trust value corresponding to each of the at least one first node is predicted; wherein the prediction trust value is a prediction of the trust value of the next time point corresponding to the first node.

[0204] Based on the prediction trust value, a target node is determined from the at least one first node, and a routing path is determined based on the target node.

[0205] In actual application, the second memory 26 can be a volatile memory such as a random access memory (RAM), or a non-volatile memory such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), or a combination of the above kinds of memories, and provides instructions and data to the second processor 25.

[0206] In addition, each functional module in the embodiments can be integrated in an analysis unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional module.

[0207] If the integrated unit is implemented in the form of a software function module and is not sold or used as an independent product, it can be stored in a computer readable storage medium based on such understanding. The technical solutions of the embodiments essentially or the parts that contribute to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the embodiments. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0208] The embodiments of the present application provide a routing path determination method, a node, a terminal and a storage medium. A first node determines an indirect trust value corresponding to a neighboring node based on trust evidence of the neighboring node; wherein the trust evidence is obtained based on traffic information of the neighboring node; wherein the neighboring node includes at least one; according to the indirect trust value, a final trust value of the first node at a current time is determined, and the final trust value is sent to a second node, so that the second node predicts a predicted trust value of the first node at a next time according to the final trust value, and determines a routing path based on the predicted trust value. The second node obtains the final trust value of the current time sent by each of the at least one first node; predicts the predicted trust value corresponding to each of the at least one first node based on the final trust value and a prediction calculation model; wherein the predicted trust value is a prediction of the trust value of the first node at the next time; based on the predicted trust value, a target node is determined from the at least one first node, and a routing path is determined based on the target node. That is, in the present application, the first node which is a non-head node in the cluster can determine the indirect trust value according to the trust evidence of the neighboring node, and then determine the final trust value at the current time according to the indirect trust value. The second node which is a head node in the cluster can predict the trust value of the first node at the next time according to the final trust value and the prediction calculation model by receiving the final trust value of the first node, and finally determine the routing path according to the predicted trust value of the first node. As can be seen, the present application determines the final trust value in combination with the indirect trust value corresponding to the neighboring node, which can effectively improve the accuracy of the final trust value, and then predicts the predicted trust value of the first node at the next time based on the prediction calculation model and the final trust value, which can more accurately predict the node trust value, realize the trust measurement of the node, and effectively improve the calculation effect of the trusted routing. The routing path determined according to the predicted trust value can effectively improve the reliability and security of the routing path.

[0209] Those skilled in the art will appreciate that embodiments of the application can be further implemented in a computer program product tangibly embodied in a machine-readable storage medium (including but not limited to memory storage and / or optical storage) including one or more computer programs tangibly embodied therein. Such programs can be implemented in a high-level procedural and / or object-oriented programming and / or scripting languages and / or hybrid

[0210] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure One one or more computer programs tangibly embodied therein. Such programs can also be encoded and carried on a data carrier, device or computer readable medium for execution by, or to control the operation of, data processing apparatus. Figure One The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks.

[0211] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure One one or more computer programs tangibly embodied therein. Such programs can also be encoded and carried on a data carrier, device or computer readable medium for execution by, or to control the operation of, data processing apparatus. Figure One The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks.

[0212] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure One one or more computer programs tangibly embodied therein. Such programs can also be encoded and carried on a data carrier, device or computer readable medium for execution by, or to control the operation of, data processing apparatus. Figure One The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks.

[0213] The foregoing is merely exemplary and is not intended to limit the scope of the present application.

Claims

1. A method for determining a routing path, characterized in that, Applied to the first node, which is not the head node; the method includes: Based on trust evidence from neighboring nodes, an indirect trust value corresponding to the neighboring nodes is determined; wherein, the trust evidence is obtained based on the traffic information of the neighboring nodes; the neighboring nodes include at least one. Based on the indirect trust value, the final trust value of the first node at the current time is determined, and the final trust value is sent to the second node, so that the second node predicts the predicted trust value of the first node at the next time based on the final trust value and the prediction calculation model, and determines the routing path based on the predicted trust value; wherein, the predicted trust value is obtained by estimating the heteroscedastic time series using a first-order generalized conditional heteroscedasticity model, and predicting based on a first-order autoregressive moving average model, the heteroscedastic time series, and the final trust value, the coefficients of the first-order generalized conditional heteroscedasticity model and the first-order autoregressive moving average model are determined according to the maximum likelihood estimation method, the prediction calculation model is constructed based on the autoregressive moving average model and the generalized conditional heteroscedasticity model, and the second node is the head node in the same cluster as at least one first node.

2. The method according to claim 1, characterized in that, The method of determining the indirect trust value corresponding to the neighboring nodes based on trust evidence includes: The traffic information of the adjacent nodes within a preset time period is obtained, and statistical processing of network parameters is performed based on the traffic information to obtain the trust evidence; wherein, the trust evidence is the statistical quantity of different network parameters. Based on the trust evidence, determine the prior probability corresponding to each network parameter; Using the first computational model, the prior probabilities corresponding to each network parameter are used to predict the indirect trust value.

3. The method according to claim 1 or 2, characterized in that, Determining the final trust value of the first node at the current moment based on the indirect trust value includes: Calculate the arithmetic mean of the indirect trust values ​​corresponding to each of the adjacent nodes, and use the arithmetic mean as the final trust value; or... Determine the weight parameters corresponding to the indirect trust value of each adjacent node; A weighted average is calculated based on each of the indirect trust values ​​and the corresponding weight parameters, and the weighted average is used as the final trust value of the first node at the current time.

4. The method according to claim 2 or 3, characterized in that, The first computational model is a Bayesian decision theory model.

5. The method according to claim 2, characterized in that, The different network parameters include at least two of the following: correctly forwarded packets, dropped packets, and incorrectly injected packets.

6. The method according to claim 1, characterized in that, The method includes: If the routing path contains the first node, the routing path is monitored at preset time intervals to obtain link results. If the link result indicates a routing path failure, the link result is sent to the second node so that the second node can cancel the routing path based on the link result.

7. A method for determining a routing path, characterized in that, The method is applied to a second node, which is the head node in the same cluster as at least one first node; the method includes: Obtain at least one final trust value sent by the first node at the current moment; Based on the final trust value and the prediction calculation model, predict the predicted trust value corresponding to at least one of the first nodes; wherein the predicted trust value is a prediction of the trust value corresponding to the first node at the next time step; wherein the prediction calculation model is constructed based on the autoregressive moving average model and the generalized conditional heteroscedasticity model. Specifically, based on the final trust value and the prediction calculation model, predicting the predicted trust value corresponding to at least one of the first nodes includes: Heteroscedastic time series are estimated using a first-order generalized conditional heteroscedasticity model; The prediction confidence value is obtained by making a prediction based on a first-order autoregressive moving average model, the heteroscedastic time series, and the final confidence value; wherein the coefficients of the first-order generalized conditional heteroscedasticity model and the first-order autoregressive moving average model are determined by the maximum likelihood estimation method. Based on the predicted trust value, a target node is determined from at least one of the first nodes, and a routing path is determined based on the target node.

8. The method according to claim 7, characterized in that, After predicting the predicted trust value corresponding to at least one of the first nodes based on the final trust value and the prediction calculation model, the method further includes: Determine the confidence interval of the predicted confidence value; Based on the confidence interval, a preset trust threshold corresponding to the predicted trust value is determined.

9. The method according to any one of claims 7 to 8, characterized in that, The step of determining the target node from at least one of the first nodes based on the predicted trust value includes: Based on the predicted trust value, the first node corresponding to it is classified to obtain the node classification result corresponding to the first node. The trust status of the node list is updated based on the node classification results to obtain an updated node list; wherein the node list consists of at least one first node and at least one trust status corresponding to the first node. The target node is determined based on the updated node list.

10. The method according to claim 9, characterized in that, The step of classifying the first node corresponding to the predicted trust value to obtain the node classification result corresponding to the first node includes: The predicted trust value is compared with a preset trust threshold to obtain the comparison result, thereby achieving node classification processing. If the comparison result shows that the predicted trust value is less than the preset trust threshold, then the node classification result is a malicious node category. If the comparison result shows that the predicted trust value is greater than or equal to the preset trust threshold, then the node classification result is a trusted node category.

11. The method according to claim 9 or 10, characterized in that, Determining the target node based on the updated node list includes: Determine the propagation requirements; wherein, the propagation requirements characterize the trust status requirements for the target node; According to the propagation requirements, the target node is selected from the updated node list.

12. The method according to claim 7, characterized in that, The at least one first node includes a first child node and a second child node; the second child node is a node among the at least one first node that is different from the first child node; After predicting the predicted trust value corresponding to at least one of the first nodes based on the final trust value and the prediction calculation model, the method further includes: The predicted trust value corresponding to the second child node is sent to the first child node, so that the first child node updates the trusted neighbor list according to the predicted trust value corresponding to the second child node.

13. The method according to claim 9, characterized in that, The method includes: Messages are sent to at least one of the first nodes at preset time intervals, and the node list is updated based on the feedback from the at least one of the first nodes.

14. A first node, characterized in that, The first node is not the head node; the first node includes a first determining unit and a first sending unit. The first determining unit is configured to determine the indirect trust value corresponding to the neighboring node based on the trust evidence of the neighboring node; wherein the trust evidence is obtained based on the traffic information of the neighboring node; the neighboring node includes at least one; The first sending unit is configured to determine the final trust value of the first node at the current time based on the indirect trust value, and send the final trust value to the second node, so that the second node can predict the predicted trust value of the first node at the next time based on the final trust value and the prediction calculation model, and determine the routing path based on the predicted trust value; wherein, the predicted trust value is obtained by estimating the heteroscedastic time series using a first-order generalized conditional heteroscedasticity model, and predicting based on a first-order autoregressive moving average model, the heteroscedastic time series, and the final trust value, the coefficients of the first-order generalized conditional heteroscedasticity model and the first-order autoregressive moving average model are determined according to the maximum likelihood estimation method, the prediction calculation model is constructed based on the autoregressive moving average model and the generalized conditional heteroscedasticity model, and the second node is the head node in the same cluster as at least one first node.

15. A second node, characterized in that, The second node is the head node in the same cluster as at least one first node; the second node includes an acquisition unit, a prediction unit, and a second determination unit. The acquisition unit is used to acquire the final trust value at the current moment sent by at least one of the first nodes. The prediction unit is configured to predict the predicted confidence value corresponding to at least one of the first nodes based on the final confidence value and the prediction calculation model; wherein the predicted confidence value is a prediction of the confidence value corresponding to the first node at the next time step; wherein the prediction calculation model is constructed based on an autoregressive moving average model and a generalized conditional heteroscedasticity model; wherein predicting the predicted confidence value corresponding to at least one of the first nodes based on the final confidence value and the prediction calculation model includes: estimating the heteroscedasticity time series using a first-order generalized conditional heteroscedasticity model; and making a prediction based on the first-order autoregressive moving average model, the heteroscedasticity time series, and the final confidence value to obtain the predicted confidence value; wherein the coefficients of the first-order generalized conditional heteroscedasticity model and the first-order autoregressive moving average model are determined using the maximum likelihood estimation method. The second determining unit is configured to determine a target node from at least one of the first nodes based on the predicted trust value, and to determine a routing path based on the target node.

16. A first terminal, characterized in that, The first terminal operates the first node as described in claim 14. The first terminal includes a first processor and a first memory storing instructions executable by the first processor. When the instructions are executed by the first processor, the method described in any one of claims 1-6 is implemented.

17. A second terminal, characterized in that, The second terminal operates the second node as described in claim 15, the terminal including a second processor and a second memory storing instructions executable by the second processor, wherein when the instructions are executed by the second processor, the method described in any one of claims 7-13 is implemented.

18. A computer-readable storage medium having a program stored thereon, applicable to a first terminal and a second terminal, wherein when the program is executed by a first processor, it implements the method as described in any one of claims 1-6; and when the program is executed by a second processor, it implements the method as described in any one of claims 7-13.

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