Multi-objective optimization and fuzzy reasoning WSN (Wireless Sensor Network) safe clustering method and related device

Abstract

The invention provides a multi-objective optimization and fuzzy reasoning WSN security clustering method and a related device, and belongs to the field of Internet of Things. The method comprises the following steps: selecting a plurality of candidate cluster heads from a plurality of wireless sensors by the electronic equipment through a multi-objective optimization algorithm; attribute information of a plurality of candidate cluster heads is obtained, and the attribute information of each candidate cluster head comprises a trust index of the candidate cluster head and fitness information corresponding to a multi-objective optimization algorithm; selecting an optimal cluster head from the plurality of candidate cluster heads according to the attribute information of the plurality of candidate cluster heads; wherein the optimal cluster head is used for relaying communication data of the plurality of wireless sensors. Therefore, when the optimal cluster head is selected, the credibility index of the candidate cluster head is considered, and the index represents the security of the candidate cluster head, so that the security of the wireless communication network is improved by the optimal cluster head.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things, and more specifically, to a WSN secure clustering method and related devices based on multi-objective optimization and fuzzy reasoning. Background Art

[0002] Wireless Sensor Network (WSN) is a data-centric self-organizing network. In addition to dynamic routing and forwarding functions, any intermediate node must also have certain data fusion processing and self-energy management functions. Therefore, WSN protocol solutions generally focus on finding a balance between technical indicators such as low network energy consumption, dynamic scalability, and service quality.

[0003] like Figure 1 As shown in the figure, cluster routing is an effective solution to the energy saving problem of WSN. By dividing the wireless sensor nodes into several different clusters 12, each cluster 12 is controlled and managed by a cluster head 13 (hereinafter also represented as CH). The cluster head 13 communicates with the base station 11 (hereinafter also represented as BS), and the remaining wireless sensors transmit information to the base station 11 through the cluster head 13. Therefore, the cluster routing algorithm can effectively reduce the energy consumption of WSN, extend the network life, and improve the network reliability. At the same time, with the vigorous development of swarm intelligence technology, deep integration of it with WSN routing technology can effectively solve the problem of reasonable selection of cluster heads.

[0004] At present, the cluster head election process is defined as a multi-objective optimization problem, and a multi-objective optimization algorithm is used to control the cluster head selection and achieve good results. However, although the current research has excellent performance in reducing network energy consumption and extending network life cycle, it ignores network security issues. Summary of the invention

[0005] In order to overcome at least one of the deficiencies in the prior art, the present application provides a WSN secure clustering method and related devices based on multi-objective optimization and fuzzy reasoning, specifically including:

[0006] In a first aspect, the present application provides a WSN secure clustering method based on multi-objective optimization and fuzzy reasoning, the method comprising:

[0007] Select multiple candidate cluster heads from multiple wireless sensors through a multi-objective optimization algorithm;

[0008] Acquire attribute information of a plurality of candidate cluster heads, wherein the attribute information of each candidate cluster head includes its own trust index and fitness information corresponding to the multi-objective optimization algorithm;

[0009] According to the attribute information of the plurality of candidate cluster heads, an optimal cluster head is selected from the plurality of candidate cluster heads, wherein the optimal cluster head is used to relay the communication data of the plurality of wireless sensors.

[0010] In combination with the optional implementation manner of the first aspect, the selecting a plurality of candidate cluster heads from a plurality of wireless sensors by using a multi-objective optimization algorithm includes:

[0011] Consider each of the wireless sensors as a traveler and initialize the location information of each traveler;

[0012] According to the current location information of each of the travelers, obtaining the current fitness information of each of the travelers;

[0013] According to the current fitness information of each of the walkers, update the current Pareto solution set;

[0014] According to the congestion degree of the current Pareto solution set, the position vector required by the hiking optimization algorithm is obtained;

[0015] According to the position vector, updating the current position information of each of the travelers through a hiking optimization algorithm;

[0016] If the iteration limit is not reached, the process returns to execute the process of obtaining the current fitness information of each traveler according to the current position information of each traveler until the iteration limit is reached, and then multiple travelers in the current Pareto solution set are used as the multiple candidate cluster heads.

[0017] In combination with the optional implementation manner of the first aspect, the fitness information of each traveler includes its own remaining energy index, a distance index between itself and the base station, and a frequency index of itself being selected as a candidate cluster head.

[0018] In conjunction with the optional implementation manner of the first aspect, updating the current Pareto solution set according to the current fitness information of each of the walkers includes:

[0019] Determine the current non-dominated solution according to the current fitness information of each of the walkers;

[0020] If the current Pareto solution set is an empty set, add the current non-dominated solution to the current Pareto solution set;

[0021] If the current Pareto solution set is not an empty set, the current non-dominated solution is added to the current Pareto solution set, and the non-dominated solutions in the current Pareto solution set that meet the preset elimination conditions are deleted.

[0022] In combination with the optional implementation manner of the first aspect, the obtaining of the trust indicators of the plurality of candidate cluster heads includes:

[0023] For each of the candidate cluster heads, determining at least one common sensor other than the multiple candidate cluster heads from the multiple wireless sensors;

[0024] Determine at least one adjacent sensor within a preset distance from the candidate cluster head from the at least one common sensor;

[0025] Acquire a first trust value between the candidate cluster head and each of the adjacent sensors, and obtain an aggregate trust value based on the first trust value between the candidate cluster head and each of the common sensors;

[0026] Acquire a second trust value between the candidate cluster head and other candidate cluster heads;

[0027] The aggregated trust value and the second trust values ​​between the other candidate cluster heads are weighted to obtain the trust index of the candidate cluster head.

[0028] In combination with the optional implementation manner of the first aspect, the obtaining the first trust value between the candidate cluster head and each of the adjacent sensors includes:

[0029] For each of the adjacent sensors, obtaining multiple trust values ​​of the candidate cluster head and the adjacent sensor in multiple dimensions;

[0030] The multiple trust values ​​are weighted to obtain a first trust value between the candidate cluster head and the adjacent sensor.

[0031] In combination with the optional implementation manner of the first aspect, selecting the best cluster head from the plurality of candidate cluster heads according to the attribute information of the plurality of candidate cluster heads includes:

[0032] Fuzzifying the attribute information of the plurality of candidate cluster heads;

[0033] Matching the fuzzy processed attribute information of each candidate cluster head with a preset fuzzy rule base to obtain a fuzzy opportunity value of each candidate cluster head;

[0034] The best cluster head is obtained according to the fuzzy chance value of each candidate cluster head.

[0035] In a second aspect, the present application also provides a WSN security clustering device for multi-objective optimization and fuzzy reasoning, the device comprising:

[0036] A candidate cluster head module is used to select multiple candidate cluster heads from multiple wireless sensors through a multi-objective optimization algorithm;

[0037] A cluster head attribute module, used to obtain attribute information of a plurality of candidate cluster heads, wherein the attribute information of each candidate cluster head includes its own trust index and fitness information corresponding to the multi-objective optimization algorithm;

[0038] The cluster head screening module is used to select the best cluster head from the plurality of candidate cluster heads according to the attribute information of the plurality of candidate cluster heads, wherein the best cluster head is used to relay the communication data of the plurality of wireless sensors.

[0039] In conjunction with the optional implementation manner of the second aspect, the candidate cluster head module is further specifically used for:

[0040] Consider each of the wireless sensors as a traveler and initialize the location information of each traveler;

[0041] According to the current location information of each of the travelers, obtaining the current fitness information of each of the travelers;

[0042] According to the current fitness information of each of the walkers, update the current Pareto solution set;

[0043] According to the congestion degree of the current Pareto solution set, the position vector required by the hiking optimization algorithm is obtained;

[0044] According to the position vector, updating the current position information of each of the travelers through a hiking optimization algorithm;

[0045] If the iteration limit is not reached, the process returns to execute the process of obtaining the current fitness information of each traveler according to the current position information of each traveler until the iteration limit is reached, and then multiple travelers in the current Pareto solution set are used as the multiple candidate cluster heads.

[0046] In combination with the optional implementation manner of the second aspect, the fitness information of each traveler includes its own remaining energy index, a distance index between itself and the base station, and a frequency index of itself being selected as a candidate cluster head.

[0047] In conjunction with the optional implementation manner of the second aspect, the candidate cluster head module is further specifically used for:

[0048] Determine the current non-dominated solution according to the current fitness information of each of the walkers;

[0049] If the current Pareto solution set is an empty set, add the current non-dominated solution to the current Pareto solution set;

[0050] If the current Pareto solution set is not an empty set, the current non-dominated solution is added to the current Pareto solution set, and the non-dominated solutions in the current Pareto solution set that meet the preset elimination conditions are deleted.

[0051] In conjunction with the optional implementation manner of the second aspect, the cluster head attribute module is further specifically used for:

[0052] For each of the candidate cluster heads, determining at least one common sensor other than the multiple candidate cluster heads from the multiple wireless sensors;

[0053] Determine at least one adjacent sensor within a preset distance from the candidate cluster head from the at least one common sensor;

[0054] Acquire a first trust value between the candidate cluster head and each of the adjacent sensors, and obtain an aggregate trust value based on the first trust value between the candidate cluster head and each of the common sensors;

[0055] Acquire a second trust value between the candidate cluster head and other candidate cluster heads;

[0056] The aggregated trust value and the second trust values ​​between the other candidate cluster heads are weighted to obtain the trust index of the candidate cluster head.

[0057] In conjunction with the optional implementation manner of the second aspect, the cluster head attribute module is further specifically used for:

[0058] For each of the adjacent sensors, obtaining multiple trust values ​​of the candidate cluster head and the adjacent sensor in multiple dimensions;

[0059] The multiple trust values ​​are weighted to obtain a first trust value between the candidate cluster head and the adjacent sensor.

[0060] In conjunction with the optional implementation manner of the second aspect, the cluster head screening module is further specifically used for:

[0061] Fuzzifying the attribute information of the plurality of candidate cluster heads;

[0062] Matching the fuzzy processed attribute information of each candidate cluster head with a preset fuzzy rule base to obtain a fuzzy opportunity value of each candidate cluster head;

[0063] The best cluster head is obtained according to the fuzzy chance value of each candidate cluster head.

[0064] In a third aspect, the present application further provides a storage medium storing a computer program, and when the computer program is executed by a processor, the WSN secure clustering method of multi-objective optimization and fuzzy reasoning is implemented.

[0065] In a fourth aspect, the present application further provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the WSN secure clustering method of multi-objective optimization and fuzzy reasoning is implemented.

[0066] Compared with the prior art, this application has the following beneficial effects:

[0067] The present application provides a WSN secure clustering method and related devices of multi-objective optimization and fuzzy reasoning. The electronic device selects multiple candidate cluster heads from multiple wireless sensors through a multi-objective optimization algorithm; obtains attribute information of multiple candidate cluster heads, wherein the attribute information of each candidate cluster head includes its own trust index and fitness information corresponding to the multi-objective optimization algorithm; selects the best cluster head from the multiple candidate cluster heads according to the attribute information of the multiple candidate cluster heads; wherein the best cluster head is used to relay the communication data of multiple wireless sensors. In this way, the trust index of the candidate cluster head is taken into account when selecting the best cluster head, and the index characterizes the security of the candidate cluster head. Therefore, the best cluster head improves the security of the wireless communication network. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0069] Figure 1 A schematic diagram of the principle of cluster routing provided in an embodiment of the present application;

[0070] Figure 2 A flowchart of a WSN secure clustering method based on multi-objective optimization and fuzzy reasoning provided in an embodiment of the present application;

[0071] Figure 3 A function curve of the Tobler walking function provided in an embodiment of the present application;

[0072] Figure 4 A schematic diagram of the principle of non-dominated sorting provided in an embodiment of the present application;

[0073] Figure 5 A schematic diagram of the structure of the fuzzy reasoning system provided in the embodiment of the present application;

[0074] Figure 6A-6E A schematic diagram of the relationship between fuzzy sets and linguistic variables provided in an embodiment of the present application;

[0075] Figure 7The matching rules in the fuzzy rule base provided in the embodiment of the present application;

[0076] Figure 8 A schematic diagram of the structure of a WSN secure clustering device for multi-objective optimization and fuzzy reasoning provided in an embodiment of the present application;

[0077] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0079] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0080] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0081] In the description of the present application, it should be noted that the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. In addition, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0082] Based on the above statement, as introduced in the background technology, although the current research has excellent performance in reducing network energy consumption and extending network life cycle, it ignores network security issues.

[0083] Based on the discovery of the above technical problems, the inventors have proposed the following technical solutions to solve or improve the above problems through creative work. It should be noted that the defects in the solutions in the above prior art are the results obtained by the inventors after practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed in the embodiments of the present application for the above problems below should all be the contributions made by the inventors to the present application in the process of invention and creation, and should not be understood as technical contents known to those skilled in the art.

[0084] In view of this, this embodiment provides a WSN secure clustering method of multi-objective optimization and fuzzy reasoning. The electronic device selects multiple candidate cluster heads from multiple wireless sensors through a multi-objective optimization algorithm; obtains attribute information of multiple candidate cluster heads, wherein the attribute information of each candidate cluster head includes its own trust index and fitness information corresponding to the multi-objective optimization algorithm; selects the best cluster head from the multiple candidate cluster heads according to the attribute information of the multiple candidate cluster heads; wherein the best cluster head is used to relay the communication data of multiple wireless sensors. In this way, the trust index of the candidate cluster head is taken into account when selecting the best cluster head, and the index characterizes the security of the candidate cluster head. Therefore, the best cluster head improves the security of the wireless communication network.

[0085] It should be noted that the electronic device implementing the method may be a server or a base station that is communicatively connected to a plurality of wireless sensors. The server may be a single server or a server group. The server group may be centralized or distributed (for example, the server may be a distributed system). In some embodiments, the server may be local or remote relative to the user terminal. In some embodiments, the server may be implemented on a cloud platform; as an example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, etc., or any combination thereof. In some embodiments, the server may be implemented on an electronic device having one or more components.

[0086] To make the solution provided by this embodiment clearer, the following uses a base station as an electronic device for implementing the method, and combines Figure 2 Each step is described in detail. However, it should be understood that the operations of the flowchart may not be implemented in order, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art may add one or more other operations to the flowchart, or remove one or more operations from the flowchart under the guidance of the content of this application. Figure 2 As shown, the method includes:

[0087] S1, select multiple candidate cluster heads from multiple wireless sensors through a multi-objective optimization algorithm.

[0088] It should be noted that the above-mentioned multiple wireless sensors can be distributed in the same pre-divided network partition. The number of grid partitions is consistent with the number of optimal cluster heads, and the number can be calculated or set.

[0089] In addition, the above-mentioned multi-objective optimization algorithm can adopt the currently mature algorithms. For example, R. Sheeja et al. proposed an adaptive black hole tuna optimization algorithm (ABTSO), which combines the black hole algorithm (BHO) and the tuna swarm optimization algorithm (TSO). In the cluster establishment stage, Huo et al. formed a candidate cluster head set based on the principle of the average energy of the surviving nodes in the region; then, different objective functions were constructed according to the three factors of the residual energy, distance and node density of each region, and the multi-objective optimization algorithm NSGA-II and the particle swarm optimization algorithm (PSO) were used to solve the optimal solution set of the Pareto solution set; then, the entropy method was used to dynamically assign weights to each objective function, and the optimal solution set of the Pareto solution set was scored, and finally the Pareto solution set with the highest score was determined as the final cluster head of each region. In addition, Lu et al. proposed a method for dynamically selecting the optimal cluster head, using the multi-objective mayfly optimization algorithm (NSMMA), and introduced the idea of ​​a virtual complete graph to construct the transmission path between clusters, thereby significantly extending the stability period of the network.

[0090] During the research, it was found that the hiking optimization algorithm (HOA), as a global optimization algorithm, is based on the Tobler's Hiking Function (THF), also known as the Tobler walking equation, proposed by Swiss geographer Waldo Tobler in 1993. This function describes the mathematical model of the relationship between human movement speed and terrain slope. Its specific mathematical form is:

[0091]

[0092] In the formula, w i,t represents the walking speed of the ith traveler at time t (in km / h), s i,t is the slope of the terrain, calculated by the following expression:

[0093]

[0094] In the formula, dh and dx represent altitude and distance respectively, θ i,t is the slope of the terrain or trail, and its value range is [0,50°].

[0095] like Figure 3 As shown in the figure, it can be seen that as the terrain slope increases, the traveler's movement speed slows down. The negative exponential term in the equation captures this relationship. It should be noted that this function provides an estimate of walking speed and is based on an average assumption. Individual differences, physical fitness, road conditions, and other factors may affect the actual walking speed. Nevertheless, the Tobler walking function is widely used in geographic information systems (GIS) and related fields to simulate pedestrian movement and accessibility analysis.

[0096] Specifically, the hiking optimization algorithm uses the social thinking of the traveler group and the personal cognition of the individual traveler to update the location. The current speed update formula of the traveler in the hiking optimization algorithm is:

[0097] w i,t =w i,t-1 +r i,t (β best -α i,t β i,t )

[0098] Among them, r i,t is a random number in [0,1], w i,t and w i,t-1 Represent the current speed and initial speed of traveler i, β best is the leader's position, β i,t is the position of traveler i at time t, α i,t is the sweep coefficient of traveler i, and its value range is [1,3]. The sweep coefficient ensures that the traveler does not stray too far from the leader, can clearly see the direction of the leader, and can receive the leader’s signal.

[0099] The algorithm updates the parameter β in the above expression by considering the speed and position of the i-th traveler. i,t+1 , the expression is:

[0100] β i,t+1 =β i,t +w i,t

[0101] Therefore, like most metaheuristic optimization algorithms, the hiking optimization algorithm uses a random initialization technique to initialize the positions of the agents. The exploration and exploitation tendencies of the hiking optimization algorithm are affected by the ripple coefficient, which affects the distance between the leader and other travelers, and the slope of the terrain or trail affects the speed of the travelers. When the range of the ripple coefficient increases, the hiking optimization algorithm tends to the exploitation phase, and when the range of the ripple coefficient decreases, it promotes the exploration phase. In addition, a decrease in the slope will cause the hiking optimization algorithm to tend to the exploitation phase.

[0102] Therefore, the hiking optimization algorithm is a global optimization algorithm that makes full use of the experience of hikers in crossing mountains and climbing peaks. At the beginning of the hike, the initial position of the traveler is determined, and the new position of the traveler after a given time is estimated with the help of the Tobler hiking function. The physical condition of all travelers is evaluated, and the one with the best physical fitness becomes the leader; therefore, the position of the leader is rechecked in each iteration to ensure that the most suitable traveler serves as the leader each time.

[0103] As described above, the walking tour optimization algorithm is a global optimization algorithm and therefore cannot handle multi-objective optimization problems. In this embodiment, selecting multiple candidate cluster points is precisely a multi-objective optimization problem. In order to make the walking tour optimization algorithm applicable to the problem to be solved by this application, non-dominated sorting is introduced, thereby proposing a multi-objective walking tour optimization algorithm based on non-dominated sorting, which is referred to as NSHOA in this embodiment.

[0104] Among them, non-dominated sorting is an environmental selection mechanism based on the dominance relationship between individuals. In this process, a dominance relationship may be formed between any two individuals in the population. Specifically, if individual p can outperform individual q in multiple objective functions and is not inferior to q in other objective functions, then it can be said that individual p dominates individual q, expressed as p < q, which has been processed. The establishment of this dominance relationship requires the following two conditions to be met:

[0105] (1) The values ​​of individual p on all objective functions are no less than the corresponding values ​​of individual q, ensuring that the comprehensive performance of p in multi-objective optimization is no less than that of q.

[0106] (2) The value of individual p on at least one objective function is strictly better than that of individual q, which reflects the advantage of p in some aspects.

[0107] Through non-dominated sorting, the convergence of the population can be significantly improved, so that the population can quickly search the Pareto frontier. The Pareto frontier is an ideal solution set in multiple objective optimization problems, representing solutions that cannot be further optimized between multiple objectives. After the population approaches the Pareto frontier, a reasonable diversity preservation strategy can be adopted, such as a method based on individual crowding, to ensure that the population can be evenly distributed on the Pareto frontier, thereby maintaining the diversity of solutions.

[0108] like Figure 4As shown in the figure, the process of environmental selection is shown. The curved solid line in the figure represents the real Pareto frontier of the problem to be optimized, and the circles represent the individuals in the population. Non-dominated sorting divides these individuals into different levels, and selects individuals layer by layer to enter the next generation according to the order of these levels; then, when the number of individuals in the next generation population exceeds the preset population size, the crowding degree of each individual is calculated. The calculation formula of crowding degree is:

[0109]

[0110] In the formula, Crow i represents the crowding degree of individual i, m is the target number, Represents the jth objective function of individual i+1. In this way, by comparing the size of the crowding degree, an appropriate number of individuals are selected from high to low to enter the next generation of the population, which not only ensures the uniform distribution of the population on the Pareto frontier, but also maintains the diversity of the population, providing an effective strategy for solving multi-objective optimization problems.

[0111] Based on the description of the non-dominated sorting in the above embodiment, as an optional implementation of step S1, it includes:

[0112] S1-1, regard each wireless sensor as a traveler and initialize the location information of each traveler.

[0113] In addition, the population size and some other related parameters are initialized.

[0114] S1-2, according to the current location information of each traveler, obtain the current fitness information of each traveler.

[0115] The fitness information of each traveler includes its remaining energy index, the distance index between the traveler and the base station, and the frequency index of being selected as a candidate cluster head. The calculation methods of these indicators are described in detail below.

[0116] Remaining energy indicator

[0117] The residual energy can be regarded as an "indicator" of node energy consumption. In wireless sensor networks, it is quite necessary to use the residual energy of nodes as a cluster head election indicator. By selecting wireless sensors with higher residual energy as cluster heads, energy balance can be achieved, and the risk of uneven energy consumption and energy exhaustion of wireless sensors can be reduced, thereby extending the life of the entire network and improving the sustainability and stability of the network. Therefore, the greater the residual energy of the wireless sensor, the greater the probability of being elected as a cluster head. In this embodiment, the residual energy E of the i-th node is used as the cluster head. R With initial energy E iniTThe ratio of is used as the remaining energy index. In this embodiment, the sum of the remaining energy indexes of all wireless sensors is used as one of the optimization targets, and the expression is:

[0118]

[0119] Distance indicator

[0120] For each wireless sensor, its distance to the base station is directly related to the energy consumption of data transmission. Selecting nodes that are closer to the base station as cluster heads helps reduce the energy consumption of data transmission. Secondly, under normal circumstances, nodes that are closer to the base station have higher communication quality and smaller transmission delays. Therefore, selecting these nodes as cluster heads is conducive to improving the efficiency of data aggregation, reducing the redundancy and repetitiveness of data transmission, and thus reducing energy consumption. The closer the node is to the base station, the greater the probability of being elected as a cluster head. For any i-th node, the ratio of its distance to the base station to the average distance of all wireless sensors to the base station is used as the distance index. In this embodiment, the sum of the distance indexes of all wireless sensors is used as one of the optimization targets, and the expression is:

[0121]

[0122]

[0123] Frequency Index

[0124] In this embodiment, for each wireless sensor, the lower the historical frequency of its selection as a candidate cluster point, the greater the probability of being selected as a cluster head. In this embodiment, an initial value L is set for each wireless sensor. Bal =0.1, and the value increases by 0.1 in each subsequent round of the network. Bal The larger the value, the greater the chance that the node will become a cluster head. Once selected as a cluster head, its L Bal The value is reassigned to 0.1 in the next round of the network. Therefore, the current frequency index L of the i-th wireless sensor Bal The value can represent:

[0125]

[0126] According to the above three optimization objectives, the problem of selecting candidate cluster heads is expressed as a multi-objective minimization problem, and its mathematical form is as follows:

[0127]

[0128] Always, j represents a feasible solution, and Q represents a feasible solution set. In addition, it should be noted that in order to make NSHOA fully applicable to the problems involved in this embodiment, discrete variables can be mapped to continuous space by appropriate encoding methods. To this end, this embodiment adopts the following encoding method:

[0129] When selecting candidate cluster heads through NSHOA, the individuals are encoded according to the ID of each wireless sensor, that is, each dimension represents the decimal node number of an individual, and the individual dimension is N. Assume that the common node ON = {ON1, ON2, …, ON n}, then the encoding of the individual is any combination of ON elements. Therefore, each individual corresponds to a feasible solution to the candidate cluster head election problem in the monitoring area.

[0130] Based on the introduction of the current fitness information of each walker in the above embodiment, step S1 further includes:

[0131] S1-3, update the current Pareto solution set according to the current fitness information of each walker.

[0132] In an optional implementation, the base station can determine the current non-dominated solution based on the current fitness information of each walker; if the current Pareto solution set is an empty set, the current non-dominated solution is added to the current Pareto solution set; conversely, if the current Pareto solution set is not an empty set, the current non-dominated solution is added to the current Pareto solution set, and the non-dominated solutions in the current Pareto solution set that meet the preset elimination conditions are deleted.

[0133] For example, the base station saves the current non-dominated solution to the current Pareto solution set, and then removes the dominated solution; then, the congestion of each member in the Pareto solution set can be further calculated, and the member with the lowest congestion can be removed according to the congestion size; finally, non-dominated sorting can be performed according to the congestion mechanism, and the global optimal solution can be selected based on the sorting result, for example, retaining some members with a forward position.

[0134] Based on the updated Pareto solution set, step S1 further includes:

[0135] S1-4, according to the congestion of the current Pareto solution set, the position vector required by the hiking optimization algorithm is obtained.

[0136] S1-5, based on the position vector, update the current position information of each traveler through the hiking optimization algorithm;

[0137] The hiking optimization algorithm has been introduced in detail in the above embodiment, which will not be repeated here. This implementation needs to continuously update the members in the Pareto solution set by iterating the above steps until the set iteration limit is reached. Therefore, if the iteration limit is not reached, return to execute S1-2 until the iteration limit is reached, and multiple travelers in the current Pareto solution set are used as multiple candidate cluster heads.

[0138] Combined with the attribute information of the multiple candidate cluster heads determined in step S1 above, continue to refer to Figure 2 , the method provided in this embodiment also includes:

[0139] S2, obtain attribute information of multiple candidate cluster heads.

[0140] The attribute information of each candidate cluster head includes its own trust index and fitness information corresponding to the multi-objective optimization algorithm. The fitness information has been described in detail in the above embodiment, and this embodiment will not be repeated. The trust index is described in detail below:

[0141] For each candidate cluster head, the base station determines at least one common sensor outside the multiple candidate cluster heads from the multiple wireless sensors; determines at least one adjacent sensor within a preset distance from the candidate cluster head from the at least one common sensor; obtains a first trust value between the candidate cluster head and each adjacent sensor, and obtains an aggregate trust value based on the first trust value between the candidate cluster head and each common sensor; obtains a second trust value between the candidate cluster head and other candidate cluster heads; and weights the aggregate trust value and the second trust value between the other candidate cluster heads to obtain a trust index of the candidate cluster head.

[0142] For each adjacent sensor, the base station obtains multiple trust values ​​between the candidate cluster head and the adjacent sensor in multiple dimensions; and weights the multiple trust values ​​to obtain a first trust value between the candidate cluster head and the adjacent sensor.

[0143] For example, using Trust i,j It represents the trust value of the i-th wireless sensor to the j-th wireless sensor. The higher the trust value, the higher the probability of being determined as the best cluster head. The expression is:

[0144]

[0145] Among them, m represents the evaluation dimension of the trust value, W v Represents the weight value of dimension v, requiring T v i,j represents the trust value of the i-th wireless sensor by the j-th node on the evaluation dimension v.

[0146] Based on the above trust value expression, for the jth candidate cluster head, the aggregate trust value of at least one adjacent sensor is expressed as TaggTC N , whose expression is:

[0147]

[0148] Where N N Indicates the number of adjacent sensors, Trust (CHN,j) Represents the trust value of the neighboring sensor CHN to the jth candidate cluster head.

[0149] Similarly, the second trust values ​​of other candidate cluster heads and the j-th candidate cluster head can be calculated. Finally, the trust index of the j-th candidate cluster head is expressed as:

[0150] f trust =W a ×Trust CH,j +W b ×TagTC N

[0151] Where W a With W b Represents the preset weight factor, satisfying W a +W b =1.

[0152] Based on the attribute information of the multiple candidate cluster heads introduced in the above embodiment, continue to refer to Figure 2 , the method further comprises:

[0153] S3, selecting the best cluster head from the multiple candidate cluster heads according to the attribute information of the multiple candidate cluster heads.

[0154] The best cluster head is used to relay the communication data of multiple wireless sensors. In an optional implementation, the base station can perform fuzzy processing on the attribute information of multiple candidate cluster heads; match the fuzzy processed attribute information of each candidate cluster head with a preset fuzzy rule base to obtain the fuzzy opportunity value of each candidate cluster head; and obtain the best cluster head according to the fuzzy opportunity value of each candidate cluster head.

[0155] For example, Figure 5 As shown in Figure 1, the fuzzy reasoning system includes a fuzzifier, an inference engine that uses a fuzzy rule base for reasoning, and a defuzzifier. Therefore, fuzzy logic uses fuzzy rules and fuzzy reasoning mechanisms for reasoning. A fuzzy rule is a rule composed of a condition part and a conclusion part, where the condition part contains a description of a fuzzy set and the conclusion part is also a fuzzy set. Reasoning is performed by matching the input fuzzy set with the fuzzy rule and generating a new fuzzy set based on the conclusion part of the rule.

[0156] See also Figure 6A-6E , which represents the input variable f energy The fuzzy set is Fig. 6A As shown, the linguistic variables of the fuzzy set are Low, Medium and High; it represents the input variable f distance The fuzzy set is Figure 6B As shown in Figure 1, the linguistic variables of the fuzzy set are Close, Adequate, and Far. frequence The fuzzy set is Figure 6C As shown in the figure, the linguistic variables of the fuzzy set are Small, Middle and Large. trust The fuzzy set is Fig.6D As shown in , the linguistic variables of the fuzzy set are Inferior, Average and Advanced. The fuzzy set representing the fuzzy opportunity value of the output variable is Fig. 6E As shown, the linguistic variables of the fuzzy set are vL, L, rL, mL, M, mH, rH, H, vH, which respectively represent very low, low, quite low, medium low, medium, medium high, quite high, very high, and very high. This embodiment mainly adopts triangular membership function, Gaussian membership function and trapezoidal membership function, and uses predefined if-then reasoning rules to handle uncertainty, so as to realize the calculation of fuzzy opportunity value.

[0157] Based on the above description, in this embodiment, 81 inference rules are established according to 4 fuzzy input variables, so that the fuzzy opportunity value of the fuzzy output variable can be obtained through rule reasoning. In addition, this embodiment also adopts the area center (COA) defuzzification method, even if all candidate cluster heads have the same linguistic variables, different clear values ​​between 0-1 will be output. It should be noted that fuzzy rules are usually constructed and defined by domain experts or empirical knowledge. Specifically, empirical knowledge is used to define fuzzy rules, and its principle is that the candidate cluster head with greater residual energy, closer distance to the base station, higher node density, and lower frequency of node selection as cluster head has greater probability of being confirmed as the final cluster head. These 81 inference rules can be found in Figure 7 The table shown, in which rule 63 in the table is a positive example and the negative one is rule 1.

[0158] like Figure 7 As shown in Figure 2, after actual measurement, it was found that the attributes of the candidate cluster head affect its chance of becoming the final cluster head. Figure 7 The rules between the four attributes are generated in pairs, which reflects the relationship between each two attributes and the fuzzy chance. It is not difficult to see that the candidate cluster head with a higher fuzzy chance has a higher priority to become the final cluster head.

[0159] Therefore, the WSN secure clustering method of multi-objective optimization and fuzzy reasoning provided in this embodiment defines cluster head election as a multi-objective optimization problem, proposes a multi-objective hiking optimization algorithm (NSHOA) based on non-dominated sorting, and solves the Pareto solution set, that is, the candidate cluster head set, by comprehensively considering multiple performance status indicators of the node itself; then, the node trust index is introduced to use the fuzzy reasoning system to select the final cluster head to form the optimal clustering, while balancing the energy consumption of nodes in the entire network and extending the network life cycle, achieving the purpose of secure clustering and ensuring network security.

[0160] Based on the same inventive concept as the WSN secure clustering method of multi-objective optimization and fuzzy reasoning provided in this embodiment, this embodiment also provides a WSN secure clustering device of multi-objective optimization and fuzzy reasoning, which includes at least one software function module that can be stored in a memory or fixed in an electronic device in the form of software. The processor in the electronic device is used to execute the executable module stored in the memory. For example, the software function module and computer program included in the device. Please refer to Figure 8 , functionally speaking, the device may include:

[0161] The candidate cluster head module 21 is used to select multiple candidate cluster heads from multiple wireless sensors through a multi-objective optimization algorithm;

[0162] The cluster head attribute module 22 is used to obtain attribute information of multiple candidate cluster heads, wherein the attribute information of each candidate cluster head includes its own trust index and fitness information corresponding to the multi-objective optimization algorithm;

[0163] The cluster head screening module 23 is used to select the best cluster head from the multiple candidate cluster heads according to the attribute information of the multiple candidate cluster heads, wherein the best cluster head is used to relay the communication data of the multiple wireless sensors.

[0164] In this embodiment, the candidate cluster head module 21 is used to implement Figure 2 In step S1, the cluster head attribute module 22 is used to implement Figure 2 In step S2, the cluster head screening module 23 is used to implement Figure 2 Therefore, the detailed description of each module mentioned above can refer to the specific implementation of the corresponding step. In addition, since the invention has the same concept as the WSN security clustering method of multi-objective optimization and fuzzy reasoning, the WSN security clustering device of multi-objective optimization and fuzzy reasoning can also implement other steps or sub-steps of the method through the above modules, which will not be described in detail in this embodiment.

[0165] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0166] It should also be understood that if the above implementation is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application.

[0167] Therefore, this embodiment also provides a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, the WSN secure clustering method of multi-objective optimization and fuzzy reasoning provided in this embodiment is implemented. Among them, the storage medium can be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc., which can store program codes.

[0168] This embodiment provides an electronic device for implementing a WSN secure clustering method based on multi-objective optimization and fuzzy reasoning. Fig. 9 As shown, the electronic device includes a processor 32 and a memory 31. In addition, the memory 31 stores a computer program, and the processor implements the WSN secure clustering method of multi-objective optimization and fuzzy reasoning provided in this embodiment by reading and executing the computer program corresponding to the above implementation in the memory 31.

[0169] Continue to see Fig. 9 The electronic device further includes a communication unit 33. The memory 31, the processor 32 and the communication unit 33 are electrically connected to each other directly or indirectly through a system bus 34 to achieve data transmission or interaction.

[0170] The memory 31 may be an information recording device based on any electronic, magnetic, optical or other physical principle, used to record execution instructions, data, etc. In some embodiments, the memory 31 may be, but is not limited to, a volatile memory, a non-volatile memory, a storage drive, etc.

[0171] In some embodiments, the volatile memory may be a random access memory (RAM); in some embodiments, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), a flash memory, etc.; in some embodiments, the storage drive may be a disk drive, a solid-state drive, any type of storage disk (such as a CD, a DVD, etc.), or a similar storage medium, or a combination thereof, etc.

[0172] The communication unit 33 is used to send and receive data through a network. In some embodiments, the network may include a wired network, a wireless network, a fiber optic network, a telecommunication network, an intranet, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a wide area network (WAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network, or a near field communication (NFC) network, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include a wired or wireless network access point, such as a base station and / or a network switching node, through which one or more components of the service request processing system may be connected to the network to exchange data and / or information.

[0173] The processor 32 may be an integrated circuit chip having a signal processing capability, and the processor may include one or more processing cores (e.g., a single-core processor or a multi-core processor). By way of example only, the processor may include a central processing unit (CPU), an application specific integrated circuit (ASIC), an application specific instruction set processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), or a microprocessor, or any combination thereof.

[0174] Understandably, Fig. 9 The structure shown is for illustration only. The electronic device may also have Fig. 9 More or fewer components than shown, or with Fig. 9 Different configurations are shown. Fig. 9 The components shown may be implemented in hardware, software or a combination thereof.

[0175] It should be understood that the apparatus and method disclosed in the above-mentioned embodiments can also be implemented in other ways. The apparatus embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the apparatus, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0176] The above are only various implementations of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A WSN secure clustering method based on multi-objective optimization and fuzzy reasoning, characterized in that: The method comprises: Select multiple candidate cluster heads from multiple wireless sensors through a multi-objective optimization algorithm; Acquire attribute information of a plurality of candidate cluster heads, wherein the attribute information of each candidate cluster head includes its own trust index and fitness information corresponding to the multi-objective optimization algorithm; According to the attribute information of the plurality of candidate cluster heads, an optimal cluster head is selected from the plurality of candidate cluster heads, wherein the optimal cluster head is used to relay the communication data of the plurality of wireless sensors.

2. The WSN secure clustering method based on multi-objective optimization and fuzzy reasoning according to claim 1 is characterized in that: The method of selecting a plurality of candidate cluster heads from a plurality of wireless sensors by using a multi-objective optimization algorithm comprises: Consider each of the wireless sensors as a traveler and initialize the location information of each traveler; According to the current location information of each of the travelers, obtaining the current fitness information of each of the travelers; According to the current fitness information of each of the walkers, update the current Pareto solution set; According to the congestion degree of the current Pareto solution set, the position vector required by the hiking optimization algorithm is obtained; According to the position vector, updating the current position information of each of the travelers through a hiking optimization algorithm; If the iteration limit is not reached, the process returns to execute the process of obtaining the current fitness information of each traveler according to the current position information of each traveler until the iteration limit is reached, and then multiple travelers in the current Pareto solution set are used as the multiple candidate cluster heads.

3. The WSN secure clustering method based on multi-objective optimization and fuzzy reasoning according to claim 2 is characterized in that: The fitness information of each traveler includes its own remaining energy index, the distance index between the traveler and the base station, and the frequency index of being selected as a candidate cluster head.

4. The WSN secure clustering method based on multi-objective optimization and fuzzy reasoning according to claim 2 is characterized in that: The updating of the current Pareto solution set according to the current fitness information of each of the walkers includes: Determine the current non-dominated solution according to the current fitness information of each of the walkers; If the current Pareto solution set is an empty set, add the current non-dominated solution to the current Pareto solution set; If the current Pareto solution set is not an empty set, the current non-dominated solution is added to the current Pareto solution set, and the non-dominated solutions in the current Pareto solution set that meet the preset elimination conditions are deleted.

5. The WSN secure clustering method based on multi-objective optimization and fuzzy reasoning according to claim 1 is characterized in that: The obtaining of the trustworthiness indexes of the plurality of candidate cluster heads comprises: For each of the candidate cluster heads, determining at least one common sensor other than the multiple candidate cluster heads from the multiple wireless sensors; Determine at least one adjacent sensor within a preset distance from the candidate cluster head from the at least one common sensor; Acquire a first trust value between the candidate cluster head and each of the adjacent sensors, and obtain an aggregate trust value based on the first trust value between the candidate cluster head and each of the common sensors; Acquire a second trust value between the candidate cluster head and other candidate cluster heads; The aggregated trust value and the second trust values ​​between the other candidate cluster heads are weighted to obtain the trust index of the candidate cluster head.

6. The WSN secure clustering method based on multi-objective optimization and fuzzy reasoning according to claim 5 is characterized in that: The obtaining of a first trust value between the candidate cluster head and each of the adjacent sensors includes: For each of the adjacent sensors, obtaining multiple trust values ​​of the candidate cluster head and the adjacent sensor in multiple dimensions; The multiple trust values ​​are weighted to obtain a first trust value between the candidate cluster head and the adjacent sensor.

7. The WSN secure clustering method based on multi-objective optimization and fuzzy reasoning according to claim 1 is characterized in that: The selecting the best cluster head from the plurality of candidate cluster heads according to the attribute information of the plurality of candidate cluster heads comprises: Fuzzifying the attribute information of the plurality of candidate cluster heads; Matching the fuzzy processed attribute information of each candidate cluster head with a preset fuzzy rule base to obtain a fuzzy opportunity value of each candidate cluster head; The best cluster head is obtained according to the fuzzy chance value of each candidate cluster head.

8. A WSN secure clustering device based on multi-objective optimization and fuzzy reasoning, characterized in that: The device comprises: A candidate cluster head module is used to select multiple candidate cluster heads from multiple wireless sensors through a multi-objective optimization algorithm; A cluster head attribute module, used to obtain attribute information of a plurality of candidate cluster heads, wherein the attribute information of each candidate cluster head includes its own trust index and fitness information corresponding to the multi-objective optimization algorithm; The cluster head screening module is used to select the best cluster head from the plurality of candidate cluster heads according to the attribute information of the plurality of candidate cluster heads, wherein the best cluster head is used to relay the communication data of the plurality of wireless sensors.

9. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the WSN secure clustering method of multi-objective optimization and fuzzy reasoning according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the WSN secure clustering method of multi-objective optimization and fuzzy reasoning described in any one of claims 1-7 is implemented.

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