Internet of Things Reliability Evaluation Method and Device Based on Minimum Trusted Path

Abstract

The present invention discloses an Internet of Things reliability evaluation method based on the minimum credible path: (1) Establish a network model according to the minimum requirement of the monitoring information volume in the target area; (2) Calculate the remaining energy of the nodes through the energy consumption model; (3) Calculate the link trust value through the link trust management model; (4) Construct the relationship matrix of the network model according to the remaining energy of the nodes and the trust value of the links; (5) Enumerate the credible multi-source shortest transmission paths according to the relationship matrix and calculate the probability of the successful operation of the network; (6) Calculate the network reliability (the average value of the successful operation probability). The method of the present invention proposes a lightweight link trust management mechanism, effectively excluding the influence of malicious nodes. A new and precise network reliability evaluation index is defined. The invention has greatly improved in terms of time and reliability accuracy. The present invention also provides a corresponding Internet of Things reliability evaluation device based on the minimum credible path.

Description

Technical Field

[0001] The present invention belongs to the technical field of the Internet of Things, and more specifically, relates to a method and device for evaluating the reliability of the Internet of Things based on the minimum trusted path. Background Art

[0002] The evaluation of the reliability of the Internet of Things is the core concern in the development of Internet of Things technology and an important indicator for judging whether the Internet of Things can meet the task of collecting and monitoring target information in real time and accurately. Node failures caused by energy consumption, environmental factors, malicious attacks, and other factors will greatly reduce the security of data transmission and affect the reliability of the network. Therefore, the reliability of the network must be considered during network deployment and maintenance.

[0003] The difficulties in evaluating the reliability of the Internet of Things are mainly reflected in three aspects. First, the accuracy of non-precise reliability evaluation methods is lacking, and precise methods cannot comprehensively consider various influencing factors. How to construct a reliability evaluation method that considers various influencing factors and is relatively precise is a research difficulty. Second, there are many influencing factors in the transmission link. Selecting key influencing factors can reduce the influence of interference factors such as energy consumption, environmental factors, and malicious attacks, and ultimately achieve reliable data transmission. Third, the definition of reliability evaluation indicators. Reliability indicators need to reasonably consider various factors that affect reliability in order to objectively and comprehensively evaluate the network reliability. Summary of the Invention

[0004] In view of the above deficiencies or improvement requirements of the prior art, the present invention proposes a method for evaluating the reliability of the Internet of Things based on the minimum trusted path, the purpose of which is to evaluate the ability of the Internet of Things to operate efficiently and reliably in a complex application environment. By constructing an energy consumption model and a lightweight trust management model, a relationship matrix reflecting the reliable connection between nodes is established. The paths enumerated using the relationship matrix exclude the influence of interference factors such as energy depletion, environmental factors, and malicious attacks and are all reliable.

[0005] To achieve the above object, according to one aspect of the present invention, there is provided a method for evaluating the reliability of the Internet of Things based on the minimum trusted path, including the following steps:

[0006] (1) Establish a network model according to the minimum requirement of the amount of information monitored in the target area;

[0007] (2) Calculate the remaining energy of the node through the energy consumption model;

[0008] (3) Calculate the link trust value through the trust management model;

[0009] (4) Construct a relationship matrix of the network model according to the remaining energy of the node and the trust value of the link;

[0010] (5) Enumerate the reliable multi-source shortest transmission paths according to the relationship matrix, and calculate the probability of successful network operation;

[0011] (6) Repeat steps (2)-(5) until the preset sampling times N are reached, and calculate the network reliability (the average value of the probability of successful operation).

[0012] In one embodiment of the present invention, in the step (2), it is assumed that the node v i The energy consumption calculation formula when sending and receiving k bit data and the distance is d m is:

[0013]

[0014]

[0015]

[0016] Among them, is the energy consumed for transmission, is the energy consumed for reception, E elec represents the energy consumed by the sensor for processing each bit of data, and ε fs represents the energy consumed by the power amplifier for processing each bit in the free space fading channel model.

[0017] In one embodiment of the present invention, the step (3) specifically includes the following sub-steps:

[0018] (3.1) Update the trust factors of the single-hop link according to the multi-source path of the transmission information: consistency factor, successful packet sending rate, security level, behavior parameters;

[0019] (3.2) Update the direct trust value D T (n) = AP SL (ω1|CF i,j (n)| + ω2|SPSR i,j (n)|), where ω1 and ω2 are the trust factor weights, and ω1 + ω2 = 1;

[0020] (3.3) Update the comprehensive trust value of the link T(n) = β1D T (n) + β2D T (n - 1) where β1 and β2 are the historical trust value weight and the current trust factor weight respectively, and β1 + β2 = 1.

[0021] In one embodiment of the present invention, in the step (3.1), the link trust factor calculation formula from node v i to node v j is as follows:

[0022] Consistency factor: Where CP i,j represents the number of data consistent messages between nodes i and j, and NCP i,j represents the number of messages with inconsistent data between nodes i and j;

[0023] Successful packet sending rate: Where SP i,j represents the number of successfully sent messages, and SF i,j represents the number of messages with sending failures;

[0024] Security level: When the security requirement is high, SL = 3; when it is relatively low, SL = 1; under normal circumstances, SL = 2;

[0025] Behavior parameter: Where, S i,j represents the number of successes, F i,j represents the number of failures, and n represents the nth sampling period.

[0026] In an embodiment of the present invention, the relationship matrix in step (4) is an N×N matrix (N is the number of nodes in the network model), and the matrix elements are 0 and 1. 1 indicates that there is a trusted link between two nodes; 0 indicates the contrary. The existence of a trusted link needs to simultaneously meet the following conditions:

[0027] Condition (1)

[0028] Condition (2)

[0029] Condition (3) T i,j ≥T0

[0030] Where E res,i represents the remaining energy of the node, represents the energy required to transmit data, represents the energy required for communication when the node distance is the communication radius R c at that time, represents the energy required to receive data, T i,j represents the link trust value from node j to node i in the trust matrix, and T0 represents the trust threshold. Therefore, the formula for the relationship matrix is:

[0031]

[0032] In an embodiment of the present invention, step (5) specifically includes the following sub-steps:

[0033] (5.1) Enumerate those with sensing functions and meeting the condition: T I (Ci ) = ∑δ v ≥A req Non-redundant node combination. Each node in the node combination is in the Active state. Among them, T I is the total information volume of the node combination, C i is the node combination, δ v is the information volume detected by a single node, A req is the minimum information volume required by the network model.

[0034] (5.2) Obtain all non-redundant minimum paths MPs from each node to the sink node in the network model through the depth-first search method according to the relationship matrix (saved in ascending order of the number of hops).

[0035] (5.3) Enumerate the multi-source shortest paths corresponding to the node combination. For the node combination C i , we respectively merge the MPs of each node in the node combination, and remove the redundant paths from the merged result to obtain the multi-source shortest path set MSPs’ corresponding to the node combination Ci.

[0036] (5.4) For each path in MSP’ in step (5.3), find the nodes in the Relay state among them, then remove the nodes in the Relay state from the MPs of each node in the node combination respectively, then re-merge, remove the redundant paths, and finally obtain the new multi-source shortest path MSPs”. {MSPs’} + {MSPs”} is the complete multi-source shortest path set MSPs. The multi-source shortest path sets corresponding to other node combinations can be obtained in the same way.

[0037] (5.5) Convert the MSPs in step (5.4) into cube form and sort them in descending order of the path dimension (the dimension is the number of full coordinates in the path).

[0038] (5.6) Obtain the disjoint terms of each multi-source shortest path MSP. First, through the distance formula: D (P,Q) = |AND(P, Q)|, where D represents the distance between paths P and Q, AND represents the logical AND operation, and |·| represents the number of zero coordinates (0000). Finally, use the disjoint sharp product (#) to enumerate the disjoint terms. The formula for the disjoint sharp product is as follows:

[0039]

[0040] where P, Q represent paths, D (P,Q) represents the distance between paths P and Q, N represents the number of nodes, W n The calculation formula is as follows:

[0041] W1 = AND(P1, NOT(Q1)) - P2 - P3 - … - PN

[0042] W2 = AND(P1, Q1) - AND(P2, NOT(Q2)) - P3 - … - P N

[0043] ……………

[0044] W N = AND(P1, Q1) - AND(P2, Q2) - … - AND(P N , NOT(Q N ))

[0045] wherein, P N , Q N represent the Nth node in the path, and NOT represents logical NOT operation. When zero coordinates appear in Wn, W n needs to be deleted.

[0046] (5.7) After all disjoint term sets DT in a network are successfully obtained, the calculation formula for the network successful operation probability P(success) is as follows:

[0047] P(success) = ∑P(DT i ) i = 1, 2, … |DT|

[0048] P(DT i ) = ΠP v v = 1, 2, … V

[0049] DT represents the disjoint term set, |DT| represents the number of disjoint terms in DT, P(·) represents the probability of an event occurring, V represents the total number of nodes (excluding the convergence node), and P v represents the probability of the state of node v.

[0050] In an embodiment of the present invention, the calculation formula for the network reliability in step (6) is as follows:

[0051]

[0052] wherein, |DT| represents the number of disjoint terms in DT, P(·) is the probability of an event occurring, V represents the number of nodes (excluding the convergence node), Pv represents the probability of the state of node v, and N represents the total number of samplings during the network operation.

[0053] According to another aspect of the present invention, there is also provided an Internet of Things reliability evaluation device based on the minimum credible path, including at least one processor and a memory, the at least one processor and the memory are connected through a data bus, the memory stores instructions executable by the at least one processor, and after the instructions are executed by the processor, they are used to complete the above-mentioned Internet of Things reliability evaluation method based on the minimum credible path.

[0054] Generally speaking, compared with the prior art through the above technical solution conceived by the present invention, the following beneficial effects are obtained:

[0055] (1) The present invention constructs a lightweight trust management model; the present invention selects four factors, namely the consistency factor, the successful packet sending rate, the security level, and the behavior parameter, to calculate the direct trust value, and the comprehensive trust value takes into account the current direct trust value and the historical direct trust value; when there are malicious nodes in the link, the comprehensive trust value will rapidly decrease until it is less than the threshold, and this link is determined to be an untrusted link; under normal circumstances, the link trust value will gradually increase and approach 1 infinitely; this model can effectively eliminate the influence of malicious attacks.

[0056] (2) The present invention constructs a new relationship matrix reflecting the reliable connection situation; this algorithm takes into account factors such as node energy, malicious attacks, and environmental interference, comprehensively considers the results of the energy consumption model and the trust management model, constructs an N×N relationship matrix, and all paths obtained through this matrix are reliable.

[0057] (3) The present invention defines a new reliability evaluation index; this index combines the advantages of the precise method and the non-precise method, comprehensively considers the realistic interference factors, and compared with the non-precise method, the precision has been greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flowchart of the Internet of Things reliability evaluation method based on the minimum credible path in an embodiment of the present invention;

[0059] Figure 2 is a schematic diagram showing the influence of the number of nodes on the network reliability in an embodiment of the present invention and the prior inventions (TPRE, MNRE);

[0060] Figure 3 is a schematic diagram showing the influence of the number of nodes on the running time in an embodiment of the present invention and the prior inventions (TPRE, MNRE);

[0061] Figure 4 is a schematic diagram showing the influence of the information ratio on the network reliability of different numbers of nodes in an embodiment of the present invention (TPRE);

[0062] Figure 5It is a schematic diagram showing the influence of the information ratio on the running time in network models with different numbers of nodes in the embodiment of the present invention (TPRE);

[0063] Figure 6 It is a schematic diagram showing the influence of the intrusion rate on the network reliability in the embodiments of the present invention and the existing inventions (TPRE, MNRE);

[0064] Figure 7 It is a schematic diagram showing the change of trust values of normal links and malicious links in the embodiment of the present invention (TPRE). Detailed implementation manners

[0065] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0066] The following first explains and describes the technical terms of the present invention:

[0067] Euclidean distance: It measures the absolute distance between two points or vectors in a multi-dimensional space, that is, the square root of the difference between vectors. The Euclidean distance from point A(a x ,a y ) to point B(b x ,b y ) is

[0068] Node information volume: The amount of information collected by nodes with sensing functions.

[0069] Network successful operation probability: The probability that there is a path meeting the requirements in a network. It characterizes the ability of a network to complete tasks.

[0070] Adjacent node: A node whose Euclidean distance from a node is within its communication range R c is an adjacent node.

[0071] Multi-source shortest path: The minimum non-redundant path for a node combination meeting the information volume requirement to successfully transmit information to the sink node.

[0072] Network sampling: By the relationship matrix of the current network, enumerate the multi-source shortest paths meeting the requirements, and calculate the probability of the successful operation of the current network.

[0073] Full coordinate: Each bit in the cubic form of the node state is 1, which is called a full coordinate.

[0074] Zero coordinate: Each bit in the cubic form of the node state is 0, which is called a zero coordinate.

[0075] Information ratio: The ratio of the amount of information required by the network and the amount of information of the node, rounded up. The formula is:

[0076] The solutions to the difficulties existing in the prior art are as follows:

[0077] For the first difficulty, the precise reliability evaluation method mainly uses the minimum path or minimum cut method, and the non-precise methods include Monte Carlo, artificial intelligence, etc. Selecting a precise method based on the minimum path and combining it with a non-precise method can obtain an evaluation method that combines precision and consideration of various real factors and can be well applied in practice. For the second difficulty, we constructed a lightweight trust management model and energy consumption model, and established a relationship matrix reflecting reliable connection relationships based on the data of the two models, which can reduce the interference of malicious nodes, environmental factors, energy consumption, etc. and improve the reliability of the network. For the third difficulty, reliability is the average value of the successful operation probability of the network during network operation. This indicator comprehensively considers various interference factors and improves both in terms of time and reliability.

[0078] As Figure 1 shown, the Internet of Things coverage vulnerability repair method based on reinforcement learning of the present invention includes the following steps:

[0079] (1) Establish a network model according to the minimum requirement of monitoring information volume in the target area;

[0080] (2) Calculate the remaining energy of the node through the energy consumption model. Assume that the node v i sends and receives k bit of data, and the energy consumption calculation formula in the case of the Euclidean distance of d m is:

[0081]

[0082]

[0083]

[0084] Among them, is the energy consumed by transmission, is the energy consumed by reception, E elec represents the energy consumed by the sensor for processing each bit of data, and ε fs represents the energy consumed by the power amplifier for processing each bit in the free space fading channel model.

[0085] (3) Calculate the link trust value through the trust management model. Specifically, it includes the following sub-steps:

[0086] (3.1) Update the trust factors of single-hop links according to the multi-source paths of transmitted information: consistency factor, successful packet transmission rate, security level, and behavior parameters. Among them, for the link from node v i to node v j the calculation formula for the link trust factor is as follows:

[0087] Consistency factor: where CP i,j represents the number of data consistent packets between nodes i and j, and NCP i,j represents the number of data inconsistent packets between nodes i and j;

[0088] Successful packet transmission rate: where SP i,j represents the number of successfully sent packets, and SF i,j represents the number of packets with transmission failures;

[0089] Security level: When the security requirement is high, SL = 3; when it is relatively low, SL = 1; under normal circumstances, SL = 2;

[0090] Behavior parameter: where, S i,j represents the number of successes, F i,j represents the number of failures, and n represents the nth sampling period.

[0091] (3.2) Update the direct trust value D T (n) = AP SL (ω1|CF i,j (n)| + ω2|SPSR i,j (n)|), where ω1 and ω2 are trust factor weights, and ω1 + ω2 = 1;

[0092] (3.3) Update the comprehensive trust value T(n) = β1D T (n) + β2D T (n - 1) where β1 and β2 are the historical trust value weight and the current trust factor weight respectively, and β1 + β2 = 1.

[0093] (4) Construct the relationship matrix of the network model based on the remaining energy of the nodes and the trust values of the links. The relationship matrix is an N×N matrix (N is the number of nodes in the network model), and the matrix elements are 0 and 1. 1 indicates that there is a trusted link between two nodes; 0 indicates otherwise. The existence of a trusted link needs to simultaneously meet the following conditions:

[0094] Condition (1)

[0095] Condition (2)

[0096] Condition (3) T i,j ≥ T0

[0097] where E res,i represents the remaining energy of the node, represents the energy consumed for data transmission, represents the energy consumed for communication when the node distance is the communication radius R c at this time, and represents the energy consumed for receiving data, T i,j represents the trust value calculated by node j monitoring node i in the trust matrix, and T0 represents the trust threshold. Therefore, the formula for the relationship matrix is:

[0098]

[0099] (5) Enumerate the credible multi-source shortest transmission paths according to the relationship matrix, and calculate the probability of successful network operation. It includes the following sub-steps:

[0100] (5.1) Enumerate the nodes with sensing functions and satisfying the condition: T I (C i ) = ∑δ v ≥ A req non-redundant node combinations. Each node in the node combination is in the Active state. Among them, T I is the total information volume of the node combination, C i is the node combination, δ v is the information volume detected by a single node, and A req is the minimum information volume required by the network model.

[0101] (5.2) Obtain all non-redundant minimum paths MPs (saved in ascending order of hop count) from each node in the network model to the sink node according to the relationship matrix through the depth-first search method.

[0102] (5.3) Enumerate the multi-source shortest paths corresponding to the node combinations. For the node combination Ci, we respectively merge the MPs of each node in the node combination, and remove the redundant paths from the merged result to obtain the multi-source shortest path set MSPs’ corresponding to the node combination Ci.

[0103] (5.4) For each path in MSP’ in step (5.3), find the nodes in the Relay state, then remove the nodes in the Relay state from the MPs of each node in the node combination respectively, then re-merge, remove the redundant paths, and finally obtain the new multi-source shortest path MSPs”. {MSPs’} + {MSPs”} is the complete multi-source shortest path set MSPs. The multi-source shortest path sets corresponding to other node combinations can be obtained in the same way.

[0104] (5.5) Convert the MSPs in step (5.4) into the form of a cube and sort them in descending order according to the path dimension (the dimension is the number of full coordinates in the path).

[0105] (5.6) Obtain the disjoint terms of each multi-source shortest path MSP. First, use the distance formula: D (P,Q) = |AND(P,Q)|, where D represents the distance between paths P and Q, AND represents the logical AND operation, and |·| represents the number of zero coordinates (0000). Finally, use the disjoint sharp product (#) to enumerate the disjoint terms. The formula for the disjoint sharp product is as follows:

[0106]

[0107] where P and Q represent paths, D (P，Q) represents the distance between paths P and Q, N represents the number of nodes, # represents the disjoint sharp product, and W n The calculation formula is as follows:

[0108] W1 = AND(P1, NOT(Q1)) - P2 - P3 - … - P N

[0109] W2 = AND(P1, Q1) - AND(P2, NOT(Q2)) - P3 - … - P N

[0110] ……………

[0111] W N = AND(P1, Q1) - AND(P2, Q2) - … - AND(P N , NOT(Q N ))

[0112] where P N and Q N represent the Nth node in the path, and NOT represents the logical NOT operation. When zero coordinates appear in W n , W n needs to be deleted.

[0113] (5.7) When all the disjoint term sets DT in a network are successfully obtained, the formula for calculating the network successful operation probability P(success) is as follows:

[0114] P (success) = ∑P(DT i ) i = 1, 2, … |DT|

[0115] P(DT i ) = ΠP v v = 1, 2, … V

[0116] DT represents the set of disjoint terms, |DT| represents the number of disjoint terms in DT, P(·) represents the probability of an event occurring, V represents the total number of nodes (excluding the sink node), and P v represents the probability of the state of node v.

[0117] (6) Repeat steps (2)-(5) until the preset sampling times n are reached, and calculate the network reliability (the average value of the successful operation probability). The calculation formula for the network reliability is as follows:

[0118]

[0119] where, |DT| represents the number of disjoint terms in DT, P(·) represents the probability of an event occurring, V represents the number of nodes (excluding the sink node), and P v represents the probability of the state of node v, and N represents the total number of sampling times during the network operation.

[0120] Such as Figure 2 is a schematic diagram showing the influence of the number of nodes on the network reliability in the embodiments of the present invention and the existing inventions (TPRE, MNRE). Such as Figure 3 is a schematic diagram showing the influence of the number of nodes on the running time in the embodiments of the present invention and the existing inventions (TPRE, MNRE). Such as Figure 4 is a schematic diagram showing the influence of the information ratio on the network reliability of networks with different numbers of nodes in the embodiment of the present invention (TPRE). Such as Figure 5 is a schematic diagram showing the influence of the information ratio on the running time in the network models with different numbers of nodes in the embodiment of the present invention (TPRE). Such as Figure 6 is a schematic diagram showing the influence of the intrusion rate on the network reliability in the embodiments of the present invention and the existing inventions (TPRE, MNRE). Such as Figure 7 is a schematic diagram showing the change of the trust values of normal links and malicious links in the embodiment of the present invention (TPRE). Figure 2-3 It proves the improvement of the present invention compared with the existing inventions in terms of reliability and running time. Figure 4-5 It proves that the information ratio is an important factor affecting the reliability and running time for the present invention. Figure 6-7 It proves the practicability of the trust management model in the present invention in terms of reliability evaluation and link trust value calculation.

[0121] Furthermore, the present invention also provides an Internet of Things reliability evaluation device based on the minimum trusted path, including at least one processor and a memory, the at least one processor and the memory are connected through a data bus, the memory stores instructions executable by the at least one processor, and after the instructions are executed by the processor, they are used to complete the Internet of Things reliability evaluation method based on the minimum trusted path.

[0122] Those skilled in the art can easily understand that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An Internet of Things reliability evaluation method based on the minimum credible path, characterized in that Including the following steps: (1) Establish a network model according to the minimum requirement of monitoring information volume in the target area; (2) Calculate the remaining energy of the node through the energy consumption model; in the step (2), it is assumed that the node v i When sending and receiving kbit of data and the Euclidean distance is d m, the energy consumption calculation formula is: Among them, is the energy consumed for transmission, is the energy consumed for reception, represents the energy consumed by the sensor for processing each bit of data, represents the energy consumed by the power amplifier for processing each bit in the free space fading channel model; (3) Calculate the link trust value through the trust management model; the specific steps of step (3) include the following sub-steps: (3.1) Update the trust factors of single-hop links according to the multi-source paths of the transmitted information: consistency factor, successful packet transmission rate, security level, and behavior parameters; (3.2) Update the direct trust value of the link , where 、 are the trust factor weights, + = 1; (3.3) Update the comprehensive trust value of the link where β1 and β2 are the weights of historical trust values and current trust factors respectively, and β1 + β2 = 1; (4) Construct a relationship matrix of the network model based on the remaining energy of nodes and the trust value of links; the relationship matrix in step (4) is an N×N matrix, where N is the number of nodes in the network model, and the matrix elements are 0 and 1; 1 indicates that there is a trusted link between two nodes; 0 otherwise. The existence of a trusted link needs to simultaneously meet the following conditions: Condition (1) Condition (2) Condition (3) Among them represents the remaining energy of the node represents the energy required to transmit data represents when the node distance is the communication radius R c the energy required for communication represents the energy required to receive data represents the link trust value from node j to node i in the trust matrix represents the trust threshold, so the formula for the relationship matrix is ； (5) Enumerate the trusted multi-source shortest transmission paths according to the relationship matrix and calculate the probability of successful network operation; step (5) specifically includes the following sub-steps: (5.1) Enumerate node combinations that have sensing functions and meet the conditions: The node combinations are non-redundant, and each node in the node combination is in the Active state, where, is the total information volume of the node combination, C i is the node combination, is the information volume detected by a single node, A req is the minimum information volume required by the network model; (5.2) Obtain all non-redundant minimum paths MPs from each node in the network model to the sink node through the depth-first search method according to the relationship matrix, and save them in ascending order of hop count; (5.3) Enumerate the multi-source shortest paths corresponding to the node combinations. For the node combination C i , we respectively merge the MPs of each node in the node combination, and remove the redundant paths from the merged result to obtain the multi-source shortest path set MSPs' corresponding to the node combination Ci; (5.4) For each path in MSP’ in step (5.3), find the nodes in the Relay state, then remove the nodes in the Relay state from the MPs of each node in the node combination respectively, and then re-combine them to remove redundant paths. Finally, the new multi-source shortest path MSPs” is obtained. {MSPs’}+{MSPs”} is the complete set of multi-source shortest paths MSPs. The multi-source shortest path sets corresponding to other node combinations can be obtained in the same way; (5.5) Convert the MSPs in step (5.4) into a cube form and arrange them in descending order of path dimension, where the dimension is the number of full coordinates in the path; (5.6) Obtain the disjoint terms of each multi-source shortest path cube form MSP. First, through the distance formula: , where D represents the distance between paths P and Q, AND represents the logical AND operation, |•| represents the number of zero coordinates. Finally, use the disjoint sharp product to enumerate the disjoint terms. The formula for the disjoint sharp product is as follows: where P and Q represent paths, D (P, Q) represents the distance between paths P and Q, N represents the number of nodes, # represents the disjoint sharp product, W n The calculation formula is as follows: Among them, P N , Q N represent the Nth node in the path, and NOT represents the logical NOT operation. When zero coordinates appear in W n , W n needs to be deleted; (5.7) When all disjoint term sets DT in a network are successfully obtained, the calculation formula for the probability P of successful network operation is as follows: DT represents the set of disjoint terms, |DT| represents the number of disjoint terms in DT, P(•) represents the probability of an event occurring, V represents the total number of nodes excluding the sink node, and P v represents the probability of the state of node v; (6) Repeat steps (2)-(5) until the preset sampling times N are reached, and calculate the network reliability, that is, the average value of the probability of successful operation; the calculation formula for the network reliability in step (6) is as follows: Where, |DT| represents the number of disjoint terms in DT, P(•) represents the probability of an event occurring, V represents the number of nodes excluding the sink node, Pv represents the probability of the state of node v, and N represents the total number of samplings during network operation.

2. The method for evaluating the reliability of the Internet of Things based on the minimum credible path according to claim 1, wherein The trust factor calculation formula for the link from node v i to node v j is as follows: Consistency factor: , where represents the number of data consistent messages between nodes i and j, represents the number of messages with inconsistent data between nodes i and j; Successful packet transmission rate: , where represents the number of successfully sent packets, represents the number of packets with transmission failures; Security level: When the security requirement is relatively high, SL = 3; when it is relatively low, SL = 1; under normal circumstances, SL = 2; Behavior parameter: , where represents the number of successes, represents the number of failures, and n represents the nth sampling order.

3. An Internet of Things reliability evaluation device based on the minimum trusted path, characterized in that: It includes at least one processor and a memory, which are connected by a data bus. The memory stores instructions executable by the at least one processor. After being executed by the processor, the instructions are used to complete the Internet of Things reliability evaluation method based on the minimum trusted path described in any one of claims 1-2.

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