Robust networking method for scale-free Internet of Things based on quantum multiverse co-evolution

Abstract

The present invention relates to a robust networking method for a scale-free Internet of Things based on quantum multiverse co-evolution. By introducing quantum bits to replace classical bits as a carrier for topology optimization, the scale-free network topology is encoded as a quantum chromosome through the quantum bits, and operations such as quantum gates, quantum measurements and quantum multiverse collaboration are used to complete the optimization process of topological robustness, so as to explore more diverse evolutionary directions under limited evolutionary algebras, thereby jumping out of local optimality, and solving the problem that topology optimization falls into local optimal solutions due to the loss of genetic diversity; and through the collaborative mechanism between multiverses, the robustness of the scale-free Internet of Things topology to malicious attacks is further improved on the basis of ensuring the evolutionary convergence efficiency.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things, and in particular to a scale-free Internet of Things robust networking method based on quantum multiverse co-evolution. Background Art

[0002] As a new product of the information age, the Internet of Things technology is gradually developing and expanding in scale, and the fields it covers are becoming more and more extensive, such as smart medical care, smart car networking, smart cities, smart homes and smart wearable devices. Among them, the key foundation for the formation of the Internet of Things is a large number of sensor nodes.

[0003] In the Internet of Things, stable connections between sensor nodes are an important basis for efficient data collection and intelligent control. However, due to energy limitations, environmental interference and deliberate sabotage, the robustness of the topology formed by the interconnection of IoT devices is reduced. In addition, the degree of nodes in most networks shows a power-law distribution characteristic, that is, most of the connections are concentrated on a few nodes, which is consistent with the scale-free model of complex networks. This model has good tolerance to random failures, but is vulnerable to malicious attacks. Therefore, improving the resistance of scale-free networks to malicious attacks is an important breakthrough point for optimizing topological robustness.

[0004] In order not to change the robustness to random failures, it is necessary to ensure that the scale-free characteristics before and after topology optimization do not change. The way to achieve this effect is to perform edge swap operations without changing the degree of each node. Of course, for example, hill climbing, simulated annealing, and the latest multi-population evolutionary algorithms can enhance the robustness of network topology through the evolution of population chromosomes. They use classic bits 0 or 1 to construct vectors for topology optimization. However, in these methods for network topology optimization, only one topological state can be obtained for each calculation, which severely limits the exploration of more diverse evolutionary directions and easily causes the topological robustness evolution process to fall into local optimality. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a scale-free Internet of Things robust networking method based on quantum multiverse co-evolution in view of the above-mentioned prior art. The scale-free Internet of Things robust networking method based on quantum multiverse co-evolution encodes the scale-free network topology into quantum chromosomes through quantum bits, and uses quantum gates, quantum measurements, and quantum multiverse collaboration to complete the optimization process of topological robustness, solving the problem that topological optimization falls into a local optimal solution due to the loss of genetic diversity.

[0006] The technical solution adopted by the present invention to solve the above technical problems is: a scale-free Internet of Things robust networking method based on quantum multiverse co-evolution, characterized in that it includes the following steps:

[0007] Step 1, extracting the connection relationship between each sensor node in the Internet of Things, and constructing an initial scale-free network topology based on the extracted connection relationship;

[0008] Step 2, sequentially numbering all sensor nodes forming the initial scale-free network topology, and converting the initial scale-free network topology into an adjacency matrix based on the adjacency relationship between the numbered sensor nodes; wherein each position point in the adjacency matrix has a row number and a column number;

[0009] Step 3: Expand the upper triangular matrix of the adjacency matrix into a row vector, and make a judgment on whether the two sensor nodes corresponding to the row number and column number of each position point in the row vector meet the preset conditions:

[0010] When the preset condition is met, the position point is deleted, and the row vector obtained after the deletion is used as the shortened row vector, and the process goes to step 4; otherwise, the current row vector is used as the shortened row vector, and the process goes to step 4; wherein the preset condition is that the two sensor nodes corresponding to the row number and column number of the position point are not within the communication range of each other and no connection is established between the two sensor nodes;

[0011] Step 4, quantizing and encoding the connection state values ​​of the corresponding position points in the shortened row vector respectively to obtain a quantum chromosome corresponding to the shortened row vector; wherein each quantum chromosome includes two gene chains;

[0012] Step 5, initializing quantum chromosomes according to a preset number, and randomly dividing all the initialized quantum chromosomes into a preset number of quantum universes;

[0013] Step 6, using quantum measurement methods to calculate the fitness value of the topological structure corresponding to each quantum chromosome in each quantum universe, and using the obtained fitness value as an indicator to characterize the robustness of the topological structure;

[0014] Step 7, taking the quantum chromosome corresponding to the topological structure with the maximum fitness value in each quantum universe as the evolution direction of all quantum chromosomes in the quantum universe where the quantum chromosome is located and making all quantum chromosomes perform quantum rotation towards the evolution direction;

[0015] Step 8: Perform a crossover operation on all quantum chromosomes in each quantum universe after quantum rotation, and perform a mutation operation on each quantum chromosome after the crossover operation.

[0016] Step 9, respectively calculating the fitness value of the topological structure corresponding to each quantum chromosome in each quantum universe after the mutation operation, and selecting the maximum fitness value from all the obtained fitness values, and taking the quantum universe where the quantum chromosome corresponding to the topological structure with the maximum fitness value is located as the master universe of all quantum universes, and taking the remaining quantum universes as slave universes, and co-evolving between the master universe and the slave universes;

[0017] Step 10, iteratively execute the co-evolution process of steps 6 to 9 until the number of executions reaches a preset number of iterations, then terminate the co-evolution, obtain multiple fitness values, and use the topological structure corresponding to the maximum fitness value among the multiple fitness values ​​as the final networking structure of the Internet of Things.

[0018] Improved, in the scale-free Internet of Things robust networking method based on quantum multiverse co-evolution, in step 1, each sensor node selects two adjacent neighbor nodes to establish a connection according to the priority connection principle; wherein, for all neighbor nodes of each sensor node, the neighbor node with a larger degree has a greater probability of being selected to establish a connection with the sensor node.

[0019] Further improvement, in the scale-free Internet of Things robust networking method based on quantum multiverse co-evolution, in the adjacency matrix of step 2, the position point state where there is a connection between the two sensor nodes corresponding to the row number and column number is marked as 1, the position point state where the two sensor nodes corresponding to the row number and column number are within the communication range of each other and no connection is established is marked as 0, and the position point state where the two sensor nodes corresponding to the row number and column number are not within the communication range of each other and no connection is established is marked as 0.

[0020] Furthermore, in the scale-free Internet of Things robust networking method based on quantum multiverse co-evolution, in step 2, each position point state in the row vector is taken as a quantum bit, and the state of the quantum bit is marked as [α i ,β i ], and each quantum bit is in a superposition state of the first ground state and the second ground state, the first ground state is marked as |0>, and the second ground state is marked as |1>. The superposition state of the quantum bit is expressed as follows:

[0021]

[0022] Among them, α represents the probability amplitude of the quantum bit collapsing into the first ground state |0>, and β represents the probability amplitude of the quantum bit collapsing into the second ground state |1>.

[0023] Improved, in the scale-free Internet of Things robust networking method based on quantum multiverse co-evolution, in step 5, the quantum chromosomes are initialized according to the preset number in the quantized coding manner in step 4.

[0024] As a further improvement, in the scale-free Internet of Things robust networking method based on quantum multiverse co-evolution, the process of using quantum measurement method to respectively calculate the fitness value of the topological structure corresponding to each quantum chromosome in each quantum universe includes the following steps a1 to a6:

[0025] Step a1: Take each quantum chromosome in each quantum universe as the processing object, and for each topological edge in the topology corresponding to the quantum chromosome, select the sensor nodes that can exchange with the topological edge; wherein the selected sensor nodes cannot be connected to any sensor node forming the topological edge, and any topological edge of the quantum chromosome is marked as L mn , m is the edge L that forms the topology mn A sensor node, n is the topological edge L mn Another sensor node of the sensor node m; the selected sensor nodes are selected from the neighbor node lists of sensor node m and sensor node n respectively;

[0026] Step a2, obtaining an independent edge that can perform an edge exchange operation based on the screened sensor nodes; wherein, when any sensor node that is screened and located in the neighbor node list of the sensor node m is connected to any sensor node that is screened and located in the neighbor node list of the sensor node n, the edge formed by the connection between the two sensor nodes is the independent edge;

[0027] Step a3, based on the obtained independent edges, all possible edge exchange situations are listed; wherein, once the two end nodes of any independent edge are within the communication range of the two sensor nodes of the topological edge in step a1, there are three situations for the exchange of any independent edge with the topological edge in step a1; once the two end nodes of any independent edge are within the communication range of any sensor point of the two end nodes of the topological edge in step a1, there are two situations for the exchange of any independent edge with the topological edge in step a1;

[0028] Step a4, calculating the probability of each edge exchange situation according to the quantum probability amplitude corresponding to the new edge formed after the edge exchange, and determining the edge exchange scheme using the roulette method;

[0029] Step a5, modifying the state value of the connection state corresponding to the relevant edge on the optimal quantum chromosome individual according to the determined edge exchange scheme; wherein the optimal quantum chromosome individual is the quantum chromosome corresponding to the topological structure with the maximum fitness value, and the state value when the connection state is in the state of edge disconnection is marked as 0, and the state value when the connection state is in the state of edge connection is marked as 1;

[0030] Step a6, calculating the fitness value of the topological structure corresponding to the quantum chromosome processed in step a4 based on the maximum connected component method; wherein the fitness value of the topological structure corresponding to any quantum chromosome is marked as R:

[0031]

[0032] Wherein, N represents the total number of sensor nodes on the topological structure corresponding to any quantum chromosome, and MCC(k) represents the number of sensor nodes in the maximum connected subgraph after the kth attack.

[0033] Improved, in the scale-free Internet of Things robust networking method based on quantum multiverse co-evolution, in step 7, the execution process of the quantum rotation operation is as follows: the state of any quantum chromosome in the quantum universe is used as a quantum bit, and the quantum bit is converted from one superposition state to another superposition state; wherein the conversion formula of the quantum bit between the two superposition states is as follows:

[0034]

[0035]

[0036] θ=s×△θ;f j ∈[f min ,f max ]; j∈[1,J];

[0037] Among them, [α, β] T represents the quantum state of the qubit before the conversion, [α', β'] T represents the quantum state of the quantum bit after conversion, θ represents the rotation angle, which includes the rotation angle value △θ and direction s, θ max is the maximum limit of the rotation angle, θ min is the minimum limit of the rotation angle, f j represents the fitness value of the topological structure corresponding to the current j-th quantum chromosome, f max represents the maximum fitness value of the current j-th quantum chromosome in the quantum universe, f min It represents the minimum fitness value of the current j-th quantum chromosome in the quantum universe, and J is the total number of quantum chromosomes in the quantum universe.

[0038] Further improvement, in the scale-free Internet of Things robust networking method based on quantum multiverse co-evolution, in step 8, the process of performing crossover operation on all quantum chromosomes in each quantum universe is as follows: steps c1 to c5:

[0039] Step c1, taking a preset number of quantum chromosomes with a preset length value as an object group, dividing a preset number of genes on each quantum chromosome in each object group into a group, and obtaining multiple genomes;

[0040] Step c2, taking any genome as the processing object, and making a judgment based on the subscript value of each gene in the genome:

[0041] When the subscript value of the gene is less than or equal to the preset number, the first number of movement of the gene downward is calculated, and the process proceeds to step c3; otherwise, the remainder of the subscript value of the gene and the preset number is calculated, and the process proceeds to step c4; wherein the value of the first number of movement is the difference between the preset number and the value 1;

[0042] Step c3, replacing the gene with the gene at the corresponding position after the gene is moved according to the first movement times;

[0043] Step c4, make a judgment based on the remainder obtained:

[0044] When the remainder is equal to 0, the gene is not moved; otherwise, the gene is moved down by the second movement number, and it is replaced with the gene at the corresponding position after the movement; wherein the value of the second movement number is the difference between the remainder and the value 1;

[0045] Step c5, traverse and process the remaining genomes in the manner of steps c2 to c4 until the genes in all genomes have completed the replacement operation.

[0046] Further, in the scale-free Internet of Things robust networking method based on quantum multiverse co-evolution, in step 8, the process of performing mutation operation on each quantum chromosome after the crossover operation is: performing mutation operation on the quantum chromosome based on the quantum NOT gate; wherein, the evolution direction of the quantum chromosome is completely reversed by exchanging the probability amplitude of the corresponding quantum bit for the mutation of the quantum chromosome;

[0047] Among them, the quantum NOT gate formula is as follows:

[0048]

[0049] The mutation probability of the jth quantum chromosome is calculated as follows:

[0050]

[0051] Among them, P mu (j) is the mutation probability of the jth quantum chromosome, P bm It is the lower bound of the probability of quantum chromosome mutation, and △P represents the increment of the probability of quantum chromosome mutation.

[0052] Further improved, in the scale-free Internet of Things robust networking method based on quantum multiverse co-evolution, in step 9, the co-evolution process between the master universe and the slave universe includes the following steps:

[0053] Step d1, for each slave universe, sort all quantum chromosomes in the slave universe according to the order of the topological structure fitness values ​​corresponding to the quantum chromosomes, and obtain the quantum chromosome sequence corresponding to the slave universe;

[0054] Step d2, selecting a preset proportion of non-optimal quantum chromosomes in the obtained quantum chromosome sequence and performing a quantum rotation in the direction of the optimal quantum chromosome in the master universe to obtain a rotated slave universe; wherein the optimal quantum chromosome is the quantum chromosome corresponding to the topological structure with the maximum fitness value in the same quantum universe;

[0055] Step d3, selecting the best quantum chromosome from all quantum chromosomes in all slave universes; wherein all slave universes include the rotated slave universes;

[0056] Step d4, taking the selected slave universe where the optimal quantum chromosome is located as the best slave universe;

[0057] Step d5, for any main universe, sort all quantum chromosomes in the main universe according to the order of the topological structure fitness values ​​corresponding to the quantum chromosomes, and obtain the quantum chromosome sequence corresponding to the main universe;

[0058] Step d6, selecting the preset proportion of non-optimal quantum chromosomes in the obtained quantum chromosome sequence, and performing a quantum rotation in the direction of the optimal quantum chromosome in the best slave universe to obtain a rotated main universe.

[0059] Compared with the prior art, the advantages of the present invention are:

[0060] First, the scale-free IoT robust networking method of the invention introduces quantum bits to replace classical bits as the carrier of topology optimization, encodes the scale-free network topology into quantum chromosomes through quantum bits, and uses quantum gates, quantum measurements, and quantum multiverse collaboration to complete the optimization process of topological robustness. Compared with the traditional evolutionary process, the introduction of quantum superposition states allows an exponential number of topological connection states to be operated in one iteration, ensuring the exploration of more diverse evolutionary directions under a finite number of evolutionary algebras, thereby solving the problem of the evolutionary process falling into a local optimal solution due to the loss of individual diversity.

[0061] Secondly, the invention also achieves the goal of further improving the robustness of the scale-free Internet of Things topology against malicious attacks while ensuring the efficiency of evolutionary convergence through a collaborative mechanism between multiverses. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the process of a robust networking method of scale-free Internet of Things based on quantum multiverse co-evolution in an embodiment of the present invention;

[0063] Figure 2 It is a schematic diagram of the conversion process between the initial scale-free network topology, the adjacency matrix and the shortened row vector in an embodiment of the present invention;

[0064] Figure 3 This is a diagram representing a quantum superposition state;

[0065] Figure 4 Schematic diagram of quantum crossover and mutation operations of quantum chromosome;

[0066] Figure 5 Schematic diagram of the quantum measurement process for scale-free network topology;

[0067] Figure 6 Schematic diagram of the co-evolution of the quantum multiverse. DETAILED DESCRIPTION

[0068] The present invention is further described in detail below with reference to the accompanying drawings.

[0069] This embodiment provides a scale-free Internet of Things robust networking method based on quantum multiverse co-evolution. Figure 1 As shown, the robust networking method of scale-free Internet of Things based on quantum multiverse co-evolution of this embodiment includes the following steps:

[0070] Step 1: extract the connection relationship between each sensor node in the Internet of Things, and construct an initial scale-free network topology based on the extracted connection relationship; wherein, in this embodiment, the constructed initial scale-free network topology (also called initial topology) is as follows: Figure 2 As shown;

[0071] In step 1, each sensor node selects two neighboring nodes adjacent to it to establish a connection according to the priority connection principle; wherein, for all neighboring nodes of each sensor node, the neighboring node with a larger degree has a greater probability of being selected to establish a connection with the sensor node;

[0072] Step 2, sequentially numbering all sensor nodes forming the initial scale-free network topology, and converting the initial scale-free network topology into an adjacency matrix based on the adjacency relationship between the numbered sensor nodes; wherein each position point in the adjacency matrix has a row number and a column number;

[0073] For example, suppose there are 6 sensor nodes forming the initial scale-free network topology. The numbers corresponding to these 6 sensor nodes are i, j, k, m, n and p respectively. The adjacency matrix formed based on the adjacency relationship between the 6 sensor nodes after these numbers is shown in Figure 2 As shown;

[0074] It should be noted that in the above adjacency matrix, the position point state where the two sensor nodes corresponding to the row number and column number are connected is marked as 1, the position point state where the two sensor nodes corresponding to the row number and column number are within each other's communication range and no connection is established is marked as 0, and the position point state where the two sensor nodes corresponding to the row number and column number are not within each other's communication range and no connection is established is marked as 0;

[0075] For example, in Figure 2 In the adjacency matrix shown, a connection is established between node i and node j, so the position point A ij The state at is marked as 1; there is no connection between node i and node m, so the position point A im The state at is marked as 0; other cases can be deduced in the same way; among them, Figure 2 The "0" shown in the figure means that the two sensor nodes are not connected, and the "1" means that the two sensor nodes are connected;

[0076] Step 3: Expand the upper triangular matrix of the adjacency matrix into a row vector, and make a judgment on whether the two sensor nodes corresponding to the row number and column number of each position point in the row vector meet the preset conditions:

[0077] When the preset condition is met, the position point is deleted, and the row vector obtained after the deletion is used as the shortened row vector, and the process goes to step 4; otherwise, the current row vector is used as the shortened row vector, and the process goes to step 4; wherein the preset condition is that the two sensor nodes corresponding to the row number and column number of the position point are not within the communication range of each other and no connection is established between the two sensor nodes;

[0078] In this embodiment, after the judgment process based on the above preset conditions, the situation of the upper triangular matrix of the adjacency matrix and corresponding to the position point to be deleted is shown in Figure 2 The deleted lines in the upper triangular matrix represented by ; the shortened row vector obtained after the judgment and processing based on the above preset conditions is shown in Figure 2 As shown; for example, Figure 2 The “0” in the adjacency matrix shown is deleted by the strikethrough “\”;

[0079] Step 4, quantizing and encoding the connection state values ​​of the corresponding position points in the shortened row vector respectively to obtain a quantum chromosome corresponding to the shortened row vector; wherein each quantum chromosome includes two gene chains;

[0080] In this embodiment, the quantization encoding process is as follows: let each position point state in the row vector be a quantum bit, and the state of the quantum bit is marked as [α i ,β i ] T , and each quantum bit is in a superposition state of the first ground state and the second ground state, the first ground state is marked as |0>, and the second ground state is marked as |1>. The superposition state of the quantum bit is expressed as follows:

[0081]

[0082] Among them, α represents the probability amplitude of the quantum bit collapsing into the first ground state |0>, and β represents the probability amplitude of the quantum bit collapsing into the second ground state |1>;

[0083] The quantum chromosome (including two gene chains) corresponding to the shortened row vector obtained after quantum coding is shown in Figure 2 As shown, it refers to the two gene chains after the shortened row vector is "converted", which are the gene chain corresponding to the first ground state |0> and the gene chain corresponding to the second ground state |1>. The gene chain corresponding to the first ground state |0> contains 9 genes α1:α9; the gene chain corresponding to the second ground state |1> contains 9 genes β1:β9;

[0084] Step 5, initializing the quantum chromosomes in the quantization coding method in step 4 according to the preset number, and randomly dividing all the initialized quantum chromosomes into a preset number of quantum universes; wherein the preset number of chromosomes and the preset number of quantum universes here can be set as needed;

[0085] For example, the preset number of chromosomes set in this embodiment is 100, and the preset number is 5, that is, 100 quantum chromosomes are initialized in the quantization encoding method in step 4, and these 100 quantum chromosomes are randomly divided into 5 quantum universes, each of which contains 20 quantum chromosomes;

[0086] Step 6, using quantum measurement methods to calculate the fitness value of the topological structure corresponding to each quantum chromosome in each quantum universe, and using the obtained fitness value as an indicator to characterize the robustness of the topological structure;

[0087] Specifically, the process of using the quantum measurement method to calculate the fitness value of the topological structure corresponding to each quantum chromosome in each quantum universe includes the following steps a1 to a6:

[0088] Step a1: Take each quantum chromosome in each quantum universe as the processing object, and for each topological edge in the topology corresponding to the quantum chromosome, select the sensor nodes that can exchange with the topological edge; wherein the selected sensor nodes cannot be connected to any sensor node forming the topological edge, and any topological edge of the quantum chromosome is marked as L mn , m is the edge L that forms the topology mn A sensor node, n is the topological edge L mn Another sensor node of the sensor node m; the selected sensor nodes are selected from the neighbor node lists of sensor node m and sensor node n respectively;

[0089] For example, for the 20 quantum chromosomes in the first quantum universe, the 10th quantum chromosome in the first quantum universe is taken as the processing object, and a topological edge L in the topology corresponding to the 10th quantum chromosome is mn (This topological edge is formed by connecting sensor node m and sensor node n) as the topological edge for edge exchange with the selected sensor node, the sensor node (for example, sensor node p) located on the 10th quantum chromosome and not connected to sensor node m or sensor node n is selected, then the selected sensor node is the one that can be connected to the topological edge L mn sensor nodes that perform the exchange;

[0090] Step a2, obtaining an independent edge that can perform an edge exchange operation based on the screened sensor nodes; wherein, when any sensor node that is screened and located in the neighbor node list of the sensor node m is connected to any sensor node that is screened and located in the neighbor node list of the sensor node n, the edge formed by the connection between the two sensor nodes is the independent edge;

[0091] For example, assuming that all sensor nodes screened in step a1 include sensor nodes p and j, and sensor node p in the neighbor node list of sensor node m is connected to sensor node j in the neighbor node list of sensor node n, then the edge L formed by the connection between sensor node p and sensor node j is pj That is, it is an independent edge;

[0092] Step a3, based on the obtained independent edges, all possible edge exchange situations are listed; wherein, once the two end nodes of any independent edge are within the communication range of the two sensor nodes of the topological edge in step a1, there are three situations for the exchange of any independent edge with the topological edge in step a1; once the two end nodes of any independent edge are within the communication range of any sensor point of the two end nodes of the topological edge in step a1, there are two situations for the exchange of any independent edge with the topological edge in step a1;

[0093] Step a4, calculating the probability of each edge exchange situation according to the quantum probability amplitude corresponding to the new edge formed after the edge exchange, and determining the edge exchange scheme by using the roulette method; wherein, in this embodiment, the probability of each edge exchange situation can be calculated by using the existing conventional method; here, determining the edge exchange scheme based on the roulette method is also an existing common technical means;

[0094] Step a5, modifying the state value of the connection state corresponding to the relevant edge on the optimal quantum chromosome individual according to the determined edge exchange scheme; wherein the optimal quantum chromosome individual is the quantum chromosome corresponding to the topological structure with the maximum fitness value, and the state value when the connection state is in the state of edge disconnection is marked as 0, and the state value when the connection state is in the state of edge connection is marked as 1;

[0095] Step a6, calculating the fitness value of the topological structure corresponding to the quantum chromosome processed in step a4 based on the maximum connected component method; wherein the fitness value of the topological structure corresponding to any quantum chromosome is marked as R:

[0096]

[0097] Where N represents the total number of sensor nodes on the topological structure corresponding to any quantum chromosome, MCC(k) represents the number of sensor nodes in the maximum connected subgraph after the kth attack; the range of R value is (0, 0.5); when the network topology is a star network, the R value reaches the minimum value, close to 0; when the network topology is a fully connected network, the R value reaches the maximum value, close to 0.5; the formula here is It can be used as the fitness function for topology optimization problems;

[0098] Specifically, in this embodiment, Figure 5 Taking the initial network topology with 10 nodes as an example, we need to perform the following steps (1) to (5) for each edge in the network. Here we take the edge mn as an example to illustrate the specific operation process:

[0099] (1) Select edge mn and determine the nodes m and n at both ends;

[0100] (2) Determine the candidate edges. First, select the nodes that are not directly connected to m or n from the neighboring nodes of m and n, then m corresponds to nodes b and e, and n corresponds to nodes b, c, and e; then, select a node from the selected node sets of m and n respectively, and determine whether there is a connection between them. The edges be and bc that meet the above requirements are the candidate edges of edge mn;

[0101] (3) Determine the total edge exchange between all candidate edges and edge mn. Since nodes b and e are both within the communication range of m and n, there are three ways to exchange edge be and edge mn, represented by cases C1, C2, and C3; node c is only within the communication range of node n, so there are only two ways to exchange edge bc and edge mn, represented by cases C4 and C5;

[0102] (4) Calculate the probability of each edge exchange in step (3). For each edge exchange, find the corresponding position in the quantum chromosome. The existence of these two edges means that the genes at these two positions are collapsed to |1>. Assume that the two edges in case 3 correspond to [α4, β4] in the quantum chromosome. T and [α6,β6] T , then the probability that these two edges exist is |β4| 2 With |β6| 2 By calculating the probability of occurrence of the five situations C1 to C5, a side exchange situation can be selected by the roulette method;

[0103] (5) Use the value of quantum measurement to replace the corresponding value in the current optimal individual. Assuming we choose case C3, the values ​​corresponding to the edges mn and be in the optimal individual change from 1 to 0, and the values ​​corresponding to the edges nb and em change from 0 to 1. Restored to the topological structure, that is, disconnect the edges mn and be, and connect the edges me and bn.

[0104] The above is the complete process of quantum measurement for edge mn. By traversing each edge in the network and executing the above 5 steps for each edge, you can get the individual after quantum measurement. This quantum measurement method can ensure that the degree of each node does not change after measurement.

[0105] Step 7, taking the quantum chromosome corresponding to the topological structure with the maximum fitness value in each quantum universe as the evolution direction of all quantum chromosomes in the quantum universe where the quantum chromosome is located and making all quantum chromosomes perform quantum rotation towards the evolution direction;

[0106] Specifically, the quantum rotation operation is based on a quantum rotation gate and a rotation angle. The quantum rotation gate can be mathematically represented by a matrix. It is a unitary transformation. The quantum bit can be converted from one superposition state to another superposition state through the rotation gate and the rotation angle.

[0107] The execution process of the quantum rotation operation here is as follows: the state of any quantum chromosome in the quantum universe is used as a quantum bit, and the quantum bit is converted from one superposition state to another superposition state; wherein, the conversion formula of the quantum bit between two superposition states is as follows:

[0108]

[0109]

[0110] θ=s×△θ;f j ∈[f min ,f max ]; j∈[1,J];

[0111] Among them, [α, β] T represents the quantum state of the qubit before the conversion, [α', β'] T represents the quantum state of the quantum bit after conversion, θ represents the rotation angle, which includes the rotation angle value △θ and direction s, θ max is the maximum limit of the rotation angle, θ min is the minimum limit of the rotation angle, f j represents the fitness value of the topological structure corresponding to the current j-th quantum chromosome, f max represents the maximum fitness value of the current j-th quantum chromosome in the quantum universe, f min It represents the minimum fitness value of the current j-th quantum chromosome in the quantum universe, J is the total number of quantum chromosomes in the quantum universe; the direction of the rotation angle is [α, β] T The coordinate system quadrant of the vector formed and the fitness value comparison between the current quantum chromosome and the current optimal quantum chromosome are determined, and there are two directions: clockwise rotation and counterclockwise rotation; the range of the rotation angle is determined by (θ min ,θ max ) to limit, the size of the rotation angle is linearly related to the fitness value of the topological structure corresponding to the current quantum chromosome; for the excellent quantum chromosome corresponding to the topological structure with higher fitness, a larger rotation angle is assigned to accelerate the approach to the optimal solution; for the poor quantum chromosome corresponding to the topological structure with lower fitness, a smaller rotation angle is assigned to adjust the search state as quickly as possible; among them, see Figure 3 As shown, it means that the quantum state of a quantum chromosome |0> rotates to the state And the quantum state of the quantum chromosome |1> rotates to the state Schematic diagram after

[0112] Step 8, performing a crossover operation on all quantum chromosomes in each quantum universe after quantum rotation, and performing a mutation operation on each quantum chromosome after the crossover operation;

[0113] Since quantum chromosomes store probability values, the crossover operation based on partial gene fragments taken for two traditional chromosomes is not applicable to quantum chromosomes. The reason is that the probability amplitudes of the corresponding positions of the two quantum chromosomes are not much different. This kind of crossover of traditional chromosomes does not have much benefit for the evolution process.

[0114] In this embodiment, the process of performing a crossover operation on all quantum chromosomes in each quantum universe is as follows: steps c1 to c5:

[0115] Step c1, taking a preset number of quantum chromosomes with a preset length value as an object group, dividing a preset number of genes on each quantum chromosome in each object group into a group, and obtaining multiple genomes;

[0116] Step c2, taking any genome as the processing object, and making a judgment based on the subscript value of each gene in the genome:

[0117] When the subscript value of the gene is less than or equal to the preset number, the first number of movement of the gene downward is calculated, and the process proceeds to step c3; otherwise, the remainder of the subscript value of the gene and the preset number is calculated, and the process proceeds to step c4; wherein the value of the first number of movement is the difference between the preset number and the value 1;

[0118] Step c3, replacing the gene with the gene at the corresponding position after the gene is moved according to the first movement times;

[0119] Step c4, make a judgment based on the remainder obtained:

[0120] When the remainder is equal to 0, the gene is not moved; otherwise, the gene is moved down by the second movement number, and it is replaced with the gene at the corresponding position after the movement; wherein the value of the second movement number is the difference between the remainder and the value 1;

[0121] Step c5, traverse and process the remaining genomes in the manner of steps c2 to c4 until the genes in all genomes have completed the replacement operation.

[0122] For example, in step 8, we take a pair [α i ,β i ] T As a whole A i, we can get 9 genes, namely A1,…,A9; and use the three quantum chromosomes A, B, and C to explain the full interference crossover. Since the number of quantum chromosomes involved in the crossover is 3, we can divide the 3 genes into a group based on the number of quantum chromosomes. Among them:

[0123] For quantum chromosome A:

[0124] When a gene A on the quantum chromosome A i When the subscript i is not greater than 3, that is, i≤3, the gene A i Move down i-1 times. For example, the gene A2 with subscript 2 on quantum chromosome A, since the subscript 2 of gene A2 is not greater than 3, the gene A2 is moved down 1 time to replace the gene B2 with the corresponding subscript position on quantum chromosome B.

[0125] When a gene A on the quantum chromosome A i When the subscript i is greater than 3, that is, i>3, calculate the remainder K of the subscript i and 3:

[0126] If the calculated remainder K is equal to 0, then the gene A i Do not move; if the calculated remainder K is not equal to 0, gene A i Move down K-1 times. For example, if the remainder of gene A6 and 3 is 0, then gene A6 does not move; if the remainder of gene A5 and 3 is 2, then gene A5 moves down 1 time to replace gene B5 at the corresponding position on quantum chromosome B. Repeat the calculation and replacement process until all genes on the three quantum chromosomes are exchanged according to the above rules, which makes all quantum chromosomes in the quantum universe participate in the crossover process. Intuitively, after full interference crossover, the genes on the same quantum chromosome are distributed diagonally in all quantum chromosomes participating in the crossover.

[0127] Quantum mutation uses quantum NOT gates to completely reverse the evolutionary direction of an individual, that is, to exchange the probability amplitudes of the two basic states. Figure 4 Taking A6 in the figure as an example, the positions of α6 and β6 are swapped to complete the mutation process.

[0128] In this embodiment, the mutation operation can slightly disrupt the current evolutionary direction of each quantum chromosome in the quantum universe to help the evolving individuals jump out of the local optimum. The process of performing the mutation operation on each quantum chromosome after the crossover operation is as follows: performing the mutation operation on the quantum chromosome based on the quantum NOT gate; wherein, the evolutionary direction of the quantum chromosome is completely reversed by exchanging the probability amplitude of the corresponding quantum bit for the mutation of the quantum chromosome;

[0129] Among them, the quantum NOT gate formula is as follows:

[0130]

[0131] The mutation probability of the jth quantum chromosome is calculated as follows:

[0132]

[0133] Among them, P mu (j) is the mutation probability of the jth quantum chromosome, P bm is the lower bound of the probability of quantum chromosome mutation, △P represents the increment of the probability of quantum chromosome mutation; when the fitness value of the topological structure corresponding to a quantum chromosome is close to the fitness value of the topological structure corresponding to the optimal individual, the mutation probability of the quantum chromosome decreases; otherwise, the mutation probability of the quantum chromosome increases. The advantage of this setting is that the quantum chromosome corresponding to the topological structure with lower fitness is more likely to mutate, so as to increase the diversity of genetic direction;

[0134] Step 9, respectively calculating the fitness value of the topological structure corresponding to each quantum chromosome in each quantum universe after the mutation operation, and selecting the maximum fitness value from all the obtained fitness values, and taking the quantum universe where the quantum chromosome corresponding to the topological structure with the maximum fitness value is located as the master universe of all quantum universes, and taking the remaining quantum universes as slave universes, and co-evolving between the master universe and the slave universes;

[0135] Assume that after the mutation operation:

[0136] The 20 quantum chromosomes in the first quantum universe U1 are quantum chromosome 1, quantum chromosome 2, ..., quantum chromosome 20. The fitness values ​​of the topological structures corresponding to these 20 quantum chromosomes are marked as R respectively. 1,1 , R 1,2 , …, R 1,19 and R 1,20 ;

[0137] The 20 quantum chromosomes in the second quantum universe U2 are quantum chromosome 1, quantum chromosome 2, ..., quantum chromosome 20. The fitness values ​​of the topological structures corresponding to these 20 quantum chromosomes are marked as R respectively. 2,1 , R 2,2 , …, R 2,19 and R 2,20 ;

[0138] …;

[0139] The 20 quantum chromosomes in the fifth quantum universe U5 are quantum chromosome 1, quantum chromosome 2, ..., quantum chromosome 20. The fitness values ​​of the topological structures corresponding to these 20 quantum chromosomes are marked as R respectively. 5,1 , R 5,2 , …, R 5,19 and R 5,20 ;

[0140] Assume that after comparison, the fitness value R 2,19 is the 100 fitness values ​​obtained (R 1,1 , R 1,2 , …, R 5,19 and R5, 20 ), then it will have the fitness value R 2,19 The second quantum universe U2 where the 39th quantum chromosome corresponding to the topological structure is located is the main universe of the five quantum universes, and the remaining four quantum universes (U1, U3, U4 and U5) are the slave universes of the five quantum universes;

[0141] Step 10, iteratively execute the co-evolution process of steps 6 to 9 until the number of executions reaches a preset number of iterations, then terminate the co-evolution, obtain multiple fitness values, and use the topological structure corresponding to the maximum fitness value among the multiple fitness values ​​as the final networking structure of the Internet of Things.

[0142] In this embodiment, the quantum multiverses co-evolve with a cycle of 5 generations. This embodiment is explained by taking 5 quantum universes, each with 20 quantum chromosomes, and the ratio of quantum chromosomes participating in the co-evolution is 0.2 as an example.

[0143] Compared with the traditional method of fixing one universe as the master universe and other universes as slave universes, the scale-free IoT robust networking method in this embodiment selects a master universe and a corresponding slave universe in each iteration process, thereby realizing dynamic switching of the master universe (that is, the master universe is not fixed).

[0144] In addition, in order to prevent the master universe from falling into a local optimum, the scale-free IoT robust networking method of this embodiment also selects a master universe from these slave universes when updating other slave universes. As for the method of selecting a master universe from these slave universes, please refer to the method for selecting the master universe in step 9.

[0145] Specifically in this embodiment, in step 9, the co-evolution process between the master universe and the slave universe includes the following steps d1 to d6:

[0146] Step d1, for each slave universe, sort all quantum chromosomes in the slave universe according to the order of the topological structure fitness values ​​corresponding to the quantum chromosomes, and obtain the quantum chromosome sequence corresponding to the slave universe;

[0147] Step d2, selecting a preset ratio of non-optimal quantum chromosomes in the obtained quantum chromosome sequence, and performing a quantum rotation in the direction of the optimal quantum chromosome in the master universe, to obtain a rotated slave universe; wherein the optimal quantum chromosome is the quantum chromosome corresponding to the topological structure with the maximum fitness value in the same quantum universe; the preset ratio here can be set as needed, for example, also set to 0.2;

[0148] Step d3, selecting the best quantum chromosome from all quantum chromosomes in all slave universes; wherein all slave universes include the rotated slave universes;

[0149] Step d4, taking the selected slave universe where the optimal quantum chromosome is located as the best slave universe;

[0150] Step d5, for any main universe, sort all quantum chromosomes in the main universe according to the order of the topological structure fitness values ​​corresponding to the quantum chromosomes, and obtain the quantum chromosome sequence corresponding to the main universe; for example, here all quantum chromosomes in the main universe can be sorted in ascending order of fitness values;

[0151] Step d6, selecting the preset proportion of non-optimal quantum chromosomes in the obtained quantum chromosome sequence, and performing a quantum rotation in the direction of the optimal quantum chromosome in the best slave universe to obtain a rotated main universe.

[0152] Specifically, Figure 6 This is the process of quantum multiverse co-evolution in this embodiment. This embodiment uses 5 universes to illustrate the complete process of co-evolution:

[0153] The first step is to calculate the fitness value of the topology corresponding to each quantum chromosome in the five universes. The larger the fitness value, the better the quantum chromosome.

[0154] The second step is to set the maximum fitness value in each universe as the fitness value of the topology corresponding to the universe, and sort all universes in descending order according to the fitness value. The first-ranked universe is set as the master universe, such as universe U2; the other universes are slave universes, and the second-ranked universe is selected as the best slave universe among the slave universes, such as universe U4;

[0155] The third step is to select a certain proportion of difference individuals from the universes U1, U3, U4, and U5 to perform a quantum rotation operation on the quantum chromosome corresponding to the maximum fitness value in the main universe U2;

[0156] The fourth step is to select the same proportion of difference quantum chromosomes from the main universe U2 and perform a quantum rotation according to the direction of the best individual in the best slave universe U4.

[0157] It should be noted that when the next round of co-evolution occurs between quantum multiverses, the master universe and the best slave universe need to be reselected based on the fitness value, that is, the master universe and the best slave universe are changing dynamically.

[0158] Although the preferred embodiments of the present invention are described in detail above, it should be clearly understood that various modifications and variations are possible for those skilled in the art. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A robust networking method for scale-free Internet of Things based on quantum multiverse co-evolution, characterized by: The steps include: Step 1, extracting the connection relationship between each sensor node in the Internet of Things, and constructing an initial scale-free network topology based on the extracted connection relationship; Step 2, sequentially numbering all sensor nodes forming the initial scale-free network topology, and converting the initial scale-free network topology into an adjacency matrix based on the adjacency relationship between the numbered sensor nodes; wherein each position point in the adjacency matrix has a row number and a column number; Step 3: Expand the upper triangular matrix of the adjacency matrix into a row vector, and make a judgment on whether the two sensor nodes corresponding to the row number and column number of each position point in the row vector meet the preset conditions: When the preset condition is met, the position point is deleted, and the row vector obtained after the deletion is used as the shortened row vector, and the process goes to step 4; otherwise, the current row vector is used as the shortened row vector, and the process goes to step 4; wherein the preset condition is that the two sensor nodes corresponding to the row number and column number of the position point are not within the communication range of each other and no connection is established between the two sensor nodes; Step 4, quantizing and encoding the connection state values ​​of the corresponding position points in the shortened row vector respectively to obtain a quantum chromosome corresponding to the shortened row vector; wherein each quantum chromosome includes two gene chains; Step 5, initializing quantum chromosomes according to a preset number, and randomly dividing all the initialized quantum chromosomes into a preset number of quantum universes; Step 6, using quantum measurement methods to calculate the fitness value of the topological structure corresponding to each quantum chromosome in each quantum universe, and using the obtained fitness value as an indicator to characterize the robustness of the topological structure; Step 7, taking the quantum chromosome corresponding to the topological structure with the maximum fitness value in each quantum universe as the evolution direction of all quantum chromosomes in the quantum universe where the quantum chromosome is located and making all quantum chromosomes perform quantum rotation towards the evolution direction; Step 8, performing a crossover operation on all quantum chromosomes in each quantum universe after quantum rotation, and performing a mutation operation on each quantum chromosome after the crossover operation; Step 9, respectively calculating the fitness value of the topological structure corresponding to each quantum chromosome in each quantum universe after the mutation operation, and selecting the maximum fitness value from all the obtained fitness values, and taking the quantum universe where the quantum chromosome corresponding to the topological structure with the maximum fitness value is located as the master universe of all quantum universes, and taking the remaining quantum universes as slave universes, and co-evolving between the master universe and the slave universes; Step 10, iteratively execute the co-evolution process of steps 6 to 9 until the number of executions reaches a preset number of iterations, then terminate the co-evolution, obtain multiple fitness values, and use the topological structure corresponding to the maximum fitness value among the multiple fitness values ​​as the final networking structure of the Internet of Things.

2. The robust networking method for scale-free Internet of Things based on quantum multiverse co-evolution according to claim 1 is characterized in that: In step 1, each sensor node selects two neighboring nodes adjacent to it to establish a connection according to the priority connection principle; wherein, for all neighboring nodes of each sensor node, the neighboring node with a larger degree has a greater probability of being selected to establish a connection with the sensor node.

3. The robust networking method for scale-free Internet of Things based on quantum multiverse co-evolution according to claim 1 is characterized in that: In the adjacency matrix of step 2, the position point status where the two sensor nodes corresponding to the row number and column number are connected is marked as 1, the position point status where the two sensor nodes corresponding to the row number and column number are within each other's communication range and no connection is established is marked as 0, and the position point status where the two sensor nodes corresponding to the row number and column number are not within each other's communication range and no connection is established is marked as 0.

4. The scale-free Internet of Things robust networking method based on quantum multiverse co-evolution according to claim 3 is characterized in that: In step 2, let each position point state in the row vector be a quantum bit, and the state of the quantum bit is marked as [α i ,β i ] T , and each quantum bit is in a superposition state of the first ground state and the second ground state, the first ground state is marked as |0>, and the second ground state is marked as |1>. The superposition state of the quantum bit is expressed as follows: |α| 2 +|β| 2 ＝1； Among them, α represents the probability amplitude of the quantum bit collapsing into the first ground state |0>, and β represents the probability amplitude of the quantum bit collapsing into the second ground state |1>.

5. The robust networking method for scale-free Internet of Things based on quantum multiverse co-evolution according to claim 1 is characterized in that: The process of using quantum measurement methods to calculate the fitness value of the topological structure corresponding to each quantum chromosome in each quantum universe includes the following steps a1 to a6: Step a1: Take each quantum chromosome in each quantum universe as the processing object, and for each topological edge in the topology corresponding to the quantum chromosome, select the sensor nodes that can exchange with the topological edge; wherein the selected sensor nodes cannot be connected to any sensor node forming the topological edge, and any topological edge of the quantum chromosome is marked as L mn , m is the edge L that forms the topology mn A sensor node, n is the topological edge L mn Another sensor node of the sensor node m; the selected sensor nodes are selected from the neighbor node lists of sensor node m and sensor node n respectively; Step a2, obtaining all independent edges that can perform edge exchange operations based on the screened sensor nodes; wherein, when any sensor node that is screened and located in the neighbor node list of sensor node m is connected to any sensor node that is screened and located in the neighbor node list of sensor node n, the edge formed by the connection between the two sensor nodes is the independent edge; Step a3, based on the obtained independent edges, list all possible edge exchange situations; Step a4, calculating the probability of each edge exchange situation according to the quantum probability amplitude corresponding to the new edge formed after the edge exchange, and determining the edge exchange scheme using the roulette method; Step a5, modifying the state value of the connection state corresponding to the relevant edge on the optimal quantum chromosome individual according to the determined edge exchange scheme; wherein the optimal quantum chromosome individual is the quantum chromosome corresponding to the topological structure with the maximum fitness value, and the state value when the connection state is in the state of edge disconnection is marked as 0, and the state value when the connection state is in the state of edge connection is marked as 1; Step a6, calculating the fitness value of the topological structure corresponding to the quantum chromosome processed in step a4 based on the maximum connected component method; wherein the fitness value of the topological structure corresponding to any quantum chromosome is marked as R: Wherein, N represents the total number of sensor nodes on the topological structure corresponding to any quantum chromosome, and MCC(k) represents the number of sensor nodes in the maximum connected subgraph after the kth attack.

6. The method for robust networking of scale-free Internet of Things based on quantum multiverse co-evolution according to claim 4 is characterized in that: In step 7, the execution process of the quantum rotation operation is as follows: the state of any quantum chromosome in the quantum universe is used as a quantum bit, and the quantum bit is converted from one superposition state to another superposition state; wherein the conversion formula of the quantum bit between two superposition states is as follows: θ=s×△θ;f j ∈[f min ,f max ];j∈[1,J]; Among them, [α, β] T represents the quantum state of the qubit before the conversion, [α', β'] T represents the quantum state of the quantum bit after conversion, θ represents the rotation angle, which includes the rotation angle value △θ and direction s, θ max is the maximum limit of the rotation angle, θ min is the minimum limit of the rotation angle, f j represents the fitness value of the topological structure corresponding to the current j-th quantum chromosome, f max represents the maximum fitness value of the current j-th quantum chromosome in the quantum universe, f min It represents the minimum fitness value of the current j-th quantum chromosome in the quantum universe, and J is the total number of quantum chromosomes in the quantum universe.

7. The method for robust networking of scale-free Internet of Things based on quantum multiverse co-evolution according to claim 6 is characterized in that: In step 8, the process of performing mutation operation on each quantum chromosome after the crossover operation is as follows: performing mutation operation on the quantum chromosome based on the quantum NOT gate; wherein, the evolution direction of the quantum chromosome is completely reversed by exchanging the probability amplitude of the corresponding quantum bit for the mutation of the quantum chromosome; Among them, the quantum NOT gate formula is as follows: The mutation probability of the jth quantum chromosome is calculated as follows: Among them, P mu (j) is the mutation probability of the jth quantum chromosome, P bm It is the lower bound of the probability of quantum chromosome mutation, and △P represents the increment of the probability of quantum chromosome mutation.

8. The robust networking method for scale-free Internet of Things based on quantum multiverse co-evolution according to claim 6 or 7, characterized in that: In step 9, the co-evolution process between the master universe and the slave universe includes the following steps: Step d1, for each slave universe, sort all quantum chromosomes in the slave universe according to the order of the topological structure fitness values ​​corresponding to the quantum chromosomes, and obtain the quantum chromosome sequence corresponding to the slave universe; Step d2, selecting a preset proportion of non-optimal quantum chromosomes in the obtained quantum chromosome sequence and performing a quantum rotation in the direction of the optimal quantum chromosome in the master universe to obtain a rotated slave universe; wherein the optimal quantum chromosome is the quantum chromosome corresponding to the topological structure with the maximum fitness value in the same quantum universe; Step d3, selecting the best quantum chromosome from all quantum chromosomes in all slave universes; wherein all slave universes include the rotated slave universes; Step d4, taking the selected slave universe where the optimal quantum chromosome is located as the best slave universe; Step d5, for any main universe, sort all quantum chromosomes in the main universe according to the order of the topological structure fitness values ​​corresponding to the quantum chromosomes, and obtain the quantum chromosome sequence corresponding to the main universe; Step d6, selecting the preset proportion of non-optimal quantum chromosomes in the obtained quantum chromosome sequence, and performing a quantum rotation in the direction of the optimal quantum chromosome in the best slave universe to obtain a rotated main universe.

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