Wireless sensor network multi-hop routing method based on hiking algorithm

Abstract

The invention provides a wireless sensor network multi-hop routing method based on a hiking algorithm, and the method comprises the steps: carrying out the optimization of an initial clustering center of a fuzzy C-means clustering algorithm through employing the hiking algorithm, and dividing a network into a plurality of clusters with the same size through employing the fuzzy C-means clustering algorithm based on the optimized initial clustering center; selecting a cluster head for the cluster; constructing an inter-cluster data transmission path based on the selected cluster head; and constructing the wireless sensor network based on the selected cluster head and the inter-cluster data transmission path. A fuzzy C-means clustering algorithm is improved by adopting a hiking algorithm, and nodes in the network are clustered by using the improved fuzzy C-means clustering algorithm, so that a more uniform clustering effect is achieved, a node transmission path of the three-dimensional wireless sensor network is more reasonable, and the purpose of reducing energy consumed by the nodes is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of wireless communications, and in particular relates to a multi-hop routing method of a wireless sensor network based on a hiking algorithm. Background Art

[0002] Compared with traditional wired sensor networks, wireless sensor networks (WSNs) have simple node deployment and lower monitoring costs. Therefore, most of the current structural health monitoring is done by deploying various wireless sensors and forming WSNs in a self-organizing manner to obtain and transmit indicators such as strain, temperature, wind speed and load, and at the same time, data reflecting the operating status of various civil structures can be sent to the base station (BS). Various sensors in WSNs are usually equipped with batteries with limited capacity to provide the energy required for environmental perception and data transmission, and most of the sensors are deeply buried in the concrete cast bridge soil, and cannot be powered by secondary energy. At present, most of the research on WSNs is conducted in two-dimensional models, while civil structures in real use scenarios have three-dimensional characteristics, and the location distribution of sensor nodes varies. In addition, the data transmission path in the actual environment is more complex, and the network faces more serious energy consumption imbalance problems. The energy management strategy designed based on the two-dimensional plane cannot be directly used in three-dimensional wireless sensor networks. Summary of the invention

[0003] In view of the problems existing in the prior art, the present invention provides a multi-hop routing method for wireless sensor networks based on a hiking algorithm, which at least partially solves the problem that the energy of a three-dimensional wireless sensor network cannot be managed in the prior art.

[0004] The embodiment of the present invention provides a multi-hop routing method for a wireless sensor network based on a walking algorithm, comprising:

[0005] The hiking algorithm is used to optimize the initial clustering center of the fuzzy C-means clustering algorithm. Based on the optimized initial clustering center, the fuzzy C-means clustering algorithm is used to divide the network into several clusters of equal size.

[0006] Select a cluster head for the cluster;

[0007] Constructing inter-cluster data transmission paths based on the selected cluster heads;

[0008] A wireless sensor network is constructed based on the selected cluster heads and inter-cluster data transmission paths.

[0009] Optionally, the method of dividing the network into a number of clusters of equal size using a fuzzy C-means clustering algorithm based on the optimized initial cluster centers includes:

[0010] The objective function is continuously minimized until it reaches the convergence condition to obtain the final clustering result. The objective function is:

[0011] ,

[0012] Among them, M represents the entire sample set, K is the number of clusters, and U is a A two-dimensional matrix, the matrix elements The value of represents the probability that the sample point i is divided into the jth cluster, C is the cluster center set, Represents the distance between sample point i and the center of the jth cluster.

[0013] Optionally, the sum of the elements in each column of the two-dimensional matrix U is 1.

[0014] Optionally, select a cluster head for the cluster, including:

[0015] Considering the energy index and distance index of the node, the entropy weight method is used to calculate the score of the candidate cluster head, and the node with the highest score is set as the cluster head.

[0016] Optionally, the energy index is a ratio of the residual energy of the current node to the average residual energy of surrounding neighboring nodes;

[0017] The distance indicator is the average distance within the cluster.

[0018] Optionally, the calculation formula for calculating the score of the candidate cluster head using the entropy weight method is:

[0019] ,

[0020] in and are energy index and distance index respectively. and is the weight used when calculating the node score.

[0021] Optionally, the and It is calculated based on the information entropy of the distance factor and the information entropy of the energy factor.

[0022] Optionally, the step of constructing an inter-cluster data transmission path based on the selected cluster head includes:

[0023] The fitness function is designed based on the network life and the energy consumption balance between nodes, and the fitness function is solved by the hiking algorithm to obtain the optimal intra-cluster routing, thereby obtaining the inter-cluster data transmission path.

[0024] The fitness function for:

[0025]

[0026] in is the energy consumption factor, is the load factor, is the energy consumption balance factor, , , is the weight coefficient.

[0027] Optionally, the step of constructing a wireless sensor network based on the selected cluster heads and inter-cluster data transmission paths includes:

[0028] According to the remaining energy of the cluster head and the degree of energy consumption balance of the nodes in the network, it is determined whether to re-divide the cluster structure and select a new cluster head. The threshold calculation formula is:

[0029] ,

[0030] in, represents the residual energy of node j, is the number of nodes in the i-th cluster, To adjust the coefficient of the overall level of the threshold, is the standard deviation of the node’s residual energy, is the weight.

[0031] Optionally, the step of constructing a wireless sensor network based on the selected cluster heads and inter-cluster data transmission paths includes:

[0032] The ordinary nodes in each cluster transmit the collected data to the cluster head in their corresponding time slots. The cluster head compresses the data within the cluster and transmits the data to the base station according to the optimal inter-cluster data transmission path. When all nodes have completed the data transmission, the network automatically enters the next round of data transmission.

[0033] The invention provides a wireless sensor network multi-hop routing method based on the hiking algorithm. The hiking algorithm is used to improve the fuzzy C-means clustering algorithm, and the improved fuzzy C-means clustering algorithm is used to cluster the nodes in the network to achieve a more uniform clustering effect, so that the transmission path of the three-dimensional wireless sensor network nodes is more reasonable, thereby achieving the purpose of reducing the energy consumption of the nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0035] Figure 1 A schematic diagram of a three-dimensional WSN network model disclosed in an embodiment of the present invention;

[0036] Figure 2 It is a principle block diagram of a first-order radio model disclosed in an embodiment of the present invention;

[0037] Figure 3 A diagram of an adjacent cluster structure disclosed in an embodiment of the present invention;

[0038] Figure 4 It is a schematic diagram of the image of THF disclosed in an embodiment of the present invention;

[0039] Figure 5 A flowchart of the HOA disclosed in the embodiment of the present invention;

[0040] Figure 6 It is a working sequence diagram of the HOA-based multi-hop routing protocol disclosed in an embodiment of the present invention;

[0041] Figure 7 The present invention discloses a flow chart of a multi-hop routing method for wireless sensor networks based on a hiking algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0043] It should be clear that the following embodiments of the present invention are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work belong to the scope of protection of the present invention.

[0044] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. It should be understood by those skilled in the art based on the invention that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0045] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention. The illustrations only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0046] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0047] BWSN: Bridge Wireless Sensor Network,

[0048] WSN: Wireless Sensor Network,

[0049] HOA: Hiking Optimization Algorithm,

[0050] FCM: Fuzzy C-means algorithm,

[0051] BS: Base Station;

[0052] CH: cluster head.

[0053] This embodiment aims at the problem of limited node energy and uneven energy consumption in wireless sensor networks (WSNs), and proposes a multi-hop routing protocol based on the hiking optimization algorithm (HOA) and the fuzzy C-means algorithm (FCM) to achieve the purpose of balancing node energy consumption and improving network life. In the clustering stage, HOA is used to optimize the initial clustering center of FCM, so that the network clustering is more uniform. In order to more objectively and fairly select a reasonable cluster head for each cluster, the entropy weight method is used to comprehensively consider the node residual energy and the average distance within the cluster to score the node, and the node with the highest score is set as the cluster head. In the inter-cluster routing stage, HOA is used to comprehensively consider energy consumption, load and energy consumption balance to search for the best path between the cluster head and the base station. In addition, in order to further improve the energy efficiency of the proposed protocol, a re-clustering mechanism is proposed to control the frequency of updating the network cluster structure. Compared with other routing methods proposed in recent years, the present invention can effectively balance node energy consumption and extend the network life of WSN.

[0054] This embodiment proposes a multi-hop routing protocol based on HOA for three-dimensional WSN to reduce and balance the energy consumption of nodes, thereby extending the network life. First, this embodiment considers the impact of the number of cluster heads (CH) on the performance of the routing algorithm, analyzes the node energy consumption in the three-dimensional WSN, and provides a calculation method for the optimal number of cluster heads (C_op) in the network. Secondly, this embodiment controls the clustering frequency through a re-clustering mechanism based on CH energy to avoid nodes using most of their energy to receive and send various control information in the clustering process. Then, HOA is used to improve the fuzzy C-means clustering (FCM), and the improved FCM is used to cluster the nodes in the network to achieve a more uniform clustering effect. Then, in order to further reduce the node energy consumption, a CH election mechanism is designed by comprehensively considering the node residual energy and the average distance from the ordinary node in the cluster to the CH. Finally, the inter-cluster path problem in WSN is defined as a KP knapsack problem, and the objective function is designed considering the network life, node load balance and node energy consumption balance, and HOA is used to solve it. The main innovations of this embodiment are as follows:

[0055] (1) The optimal number of CHs in multi-hop scenario is calculated and given.

[0056] (2) In the network clustering stage, the FCM based on HOA optimization is used to cluster the network to make the clustering more uniform, thereby balancing the energy consumption of nodes in WSN. Then, the entropy weight method is used independently in each cluster to comprehensively consider the node residual energy and the average distance within the cluster to construct a node scoring mechanism, and the node with the highest score is set as the CH to select the CH more objectively and stably.

[0057] (3) In the process of inter-cluster multi-hop routing optimization, network lifetime, node load balance and node energy consumption balance are taken as optimization objectives and solved using HOA.

[0058] (4) A CH energy threshold re-clustering mechanism is designed to reduce various overheads in the clustering process. Unlike the traditional routing algorithm that updates the cluster structure and elects new CH nodes in each round, this embodiment monitors the remaining energy of the CH nodes in the network and updates the cluster structure only when it is below a certain threshold, thereby reducing the energy consumed by the nodes for receiving and sending control information during the clustering process.

[0059] The technical problems to be solved in this embodiment mainly include the following points:

[0060] (1) Calculate the total number of cluster heads in the network when the total energy consumption of the network is the lowest in three-dimensional space under theoretical conditions.

[0061] (2) Based on the idea of ​​clustering first, the improved FCM is used to divide the nodes in the network into several clusters of equal size.

[0062] (3) In the cluster head election stage, the cost function is constructed by comprehensively considering the node residual energy and the average distance from the ordinary nodes in the cluster to the cluster head node. The final score of the candidate cluster head is objectively and fairly calculated through the entropy weight method, and the node with the highest score is selected as the cluster head.

[0063] (4) In order to prolong the network lifetime while balancing node energy consumption, the proposed algorithm takes the total path energy consumption, node energy consumption variance, and node load entropy as the objective function for constructing the inter-cluster data transmission path and solves it through HOA.

[0064] (5) In order to avoid nodes using a large amount of energy to receive and forward various control information during the clustering process, a re-clustering mechanism is proposed to control the frequency of cluster structure updates. A new cluster will be built only when the remaining energy of the cluster head node in the network is lower than the threshold, which effectively reduces the energy consumption of the nodes.

[0065] like Figure 1 As shown, in the three-dimensional WSN model disclosed in this embodiment, N nodes are deployed in a to monitor the bridge structure in three dimensions.

[0066] like Figure 2 As shown, this embodiment uses a first-order radio model. The energy consumed by a sensor node to send k bits of data to a node d meters away is for:

[0067] ,

[0068] The energy consumed by a node to receive k bits of data for:

[0069]

[0070] in, The energy consumed by the transmitting and receiving circuits to process a unit bit of data; is the amplifier power consumption under the free space channel model; is the amplifier power consumption under the multipath fading model; is the distance threshold. When the transmission distance is less than When , the free space channel model is used, otherwise the multipath fading model is used. .

[0071] Optimal number of cluster heads:

[0072] Assume that N nodes are evenly distributed in the space with length, width and height respectively. , , In a BWSN, a node generates l bit of data per round, the number of CHs in the network is C, and the space occupied by each cluster is LWH / C. Considering the large span of BWSN, CH transmits data to BS in a multi-hop manner, and nodes in the cluster transmit data to CH in a single-hop manner. When nodes are evenly distributed, their density function is .

[0073] The energy consumption of any i-th cluster per round Energy consumption by CH and energy consumption of ordinary nodes The mathematical expression is composed of the formula Given.

[0074] ,

[0075] The calculation method of is shown in formula (4), where is the energy consumed by the member node in the cluster to send l bit of data to the corresponding CH, is the number of member nodes in the cluster.

[0076] ,

[0077] In order to calculate the distance from the common node in each cluster to the CH, the cluster space is transformed into a sphere of equal volume, and the corresponding radius is It is given by formula (5). At this time, the square of the average distance between any node in the cluster and CH can be obtained by formula (6). Then Substituting into formula (4), it can be rearranged into formula (7).

[0078] ,

[0079] ,

[0080] ,

[0081] The CH node not only needs to send the data in the cluster to the destination node, but also needs to be responsible for forwarding the data of the i+1th,…,Cth nodes. The energy consumption of the i-th CH is defined as , calculated as follows:

[0082] ,

[0083] in, Represents the distance between adjacent CHs. Since L is much larger than W and H in the network model, the network model is generally linear, and the adjacent cluster structure is as follows: Figure 3 As shown, it can be considered , then the energy consumption of each cluster per round can be calculated by formula (9).

[0084] ,

[0085] Energy consumption of the entire network , which is equal to the sum of the energy consumption of C clusters, is calculated as follows:

[0086] ,

[0087] Will Taking the derivative of C and setting it to 0, we can get the optimal number of cluster heads in the network. .

[0088] ,

[0089] That is:

[0090] ,

[0091] This embodiment mainly optimizes the routing protocol from three aspects: establishing cluster structure, electing CH, and optimizing data transmission mechanism, so as to extend the network life cycle of BWSN. Therefore, the problem to be solved can be defined as maximizing the network life of BWSN by dividing reasonable clusters, electing reasonable CH, and establishing appropriate data transmission paths. Its mathematical expression is as follows.

[0092] ,

[0093] Among them, CS, CH, and R represent the cluster structure in the network, the CH set, and the multi-hop transmission path between clusters, respectively. Indicates the time when the first node in the network dies.

[0094] Hiking Optimization Algorithm HOA: HOA was proposed by Sunday O. Oladejo et al. in 2024 and is inspired by the experience of hikers trying to climb mountains, hills, or rocky peaks. During the hike, hikers consciously or unconsciously take into account the steepness of the terrain, and they avoid steep terrain and trails to maintain their hiking speed. HOA simulates the characteristics of a hiking expedition with the ultimate goal of reaching the summit, and the hiking environment is a rugged terrain including local and global peaks. The situation experienced by hikers during the hike to the summit is similar to that of an agent trying to find the local or global optimum of an optimization problem.

[0095] The mathematical basis of HOA is the Tobler's Hiking Function (THF), also known as the Tobler walking equation, proposed by the famous Swiss geographer Waldo Tobler in 1993. This model is a mathematical model that describes the relationship between human movement speed and terrain slope. Its specific mathematical form is shown in formula (14).

[0096] (14),

[0097] in, represents the walking speed of traveler i at time t (in km / h), is the slope of the terrain, given by formula (15).

[0098] (15),

[0099] Among them, dh and dx represent altitude and distance respectively. is the slope of the terrain or trail, and its value range is [0,50°].

[0100] like Figure 4 As shown in Figure 1, 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.

[0101] HOA 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 HOA is:

[0102] (16),

[0103] in, is a random number in [0,1], and Represent the current speed and initial speed of traveler i respectively, For the leader's position, is the position of traveler i at time t, is the sweep factor (SF) of traveler i, and its value range is [1,3]. SF ensures that the traveler does not stray too far from the leader, can clearly see the direction of the leader, and receives the leader’s signal.

[0104] By considering the traveler's velocity in equation (14), the position update of traveler i is It is given by formula (17).

[0105] (17)

[0106] Like most metaheuristic optimization algorithms, HOA uses random initialization techniques to initialize the positions of the agents. The exploration and exploitation trends of HOA are affected by the sweep factor SF, which affects the distance between the leader and other travelers, and the slope of the terrain or trail affects the speed of travelers. When the SF range increases, HOA tends to the exploitation stage, and when the SF range decreases, it promotes the exploration stage. In addition, a decrease in the slope will cause HOA to tend to the exploitation stage.

[0107] like Figure 5 As shown in Figure 1, HOA 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 travelers is determined, and the new position of the travelers after a given time is estimated with the help of THF. The physical condition of all travelers is evaluated, and the one with the best physical condition becomes the leader. The position of the leader is rechecked in each iteration to ensure that the most suitable traveler is the leader each time.

[0108] Before the HOA-based multi-hop routing protocol (hereinafter referred to as the protocol) disclosed in this embodiment starts to execute, the BS broadcasts Init_Msg to the entire network. When the node receives the message, it forwards its own location information and energy information to the BS in a multi-hop manner. The BS establishes a network model based on the obtained node information and calculates . The proposed protocol is executed in rounds until all nodes in the network die. Each round mainly includes a setup phase and a stabilization phase. All tasks in the setup phase are completed by the BS, mainly including dividing the cluster structure, selecting CHs, determining the best path from CHs to BS, creating TDMA time slots and broadcasting control information to the network. The control information mainly includes member information in each cluster, the ID of CH, the inter-cluster routing table and the time slots allocated to the nodes. The stable phase refers to the nodes in the network transmitting monitoring data to the corresponding nodes in their respective time slots. In order to save energy, all nodes remain active only in the corresponding time slots and enter sleep mode at other times. The working schedule of the protocol is as follows Figure 6 shown.

[0109] Traditional routing protocols first determine the distribution of CHs in the network, and then ordinary nodes join the cluster closest to them. Although this method is simple to implement, the clustering effect of the network is extremely unstable and is easily affected by the distribution of CHs. When a large number of CHs are concentrated in a certain area, the entire network will be unevenly clustered, making the problem of uneven energy consumption between nodes more serious. In order to avoid this problem, Figure 7 As shown, this embodiment first uses the fuzzy C-means clustering algorithm (FCM) to divide the network into several clusters of equal size, and then selects a suitable CH for each cluster. It obtains the final clustering result by continuously minimizing the objective function until the convergence condition is reached. The definition of the objective function is shown in formula (18).

[0110] (18)

[0111] Among them, M represents the entire sample set, and K is the number of clusters. U is a A two-dimensional matrix, the matrix elements The value of represents the probability that the sample point i is classified into the jth cluster, and the sum of the elements in each column of the matrix is ​​1. C is the cluster center set. Represents the distance between sample point i and the center of the jth cluster.

[0112] When using FCM to cluster nodes in the network, an initial cluster center needs to be given, and the quality of the initial cluster center will directly affect the final clustering effect. In order to obtain a high-quality initial cluster center and make the clustering in the network more uniform, the proposed algorithm uses HOA to optimize the initial cluster center of FCM.

[0113] The CH node is not only responsible for integrating the data collected by ordinary nodes in the cluster, but also needs to transmit the integrated data to the BS. Its energy consumption is very rapid. In order to avoid its premature death, it is necessary to formulate a reasonable CH rotation mechanism to select high-quality nodes to become CHs. In order to improve the execution efficiency of the CH selection algorithm, this embodiment comprehensively considers the energy index and distance index of the node, and uses the entropy weight method to calculate the score of the candidate CH, and sets the node with the highest score as the CH.

[0114] The energy consumption rate of CH nodes is much faster than that of ordinary nodes in the cluster. Setting nodes with more residual energy as CH can avoid premature death of nodes and help extend the network life. In addition, the higher energy level of CH nodes can ensure the reliability of data transmission. The ratio of the residual energy of the current node to the average residual energy of the surrounding neighbor nodes is used as the energy index, which can be expressed by formula (19).

[0115] (19)

[0116] The common nodes in the cluster forward the sensing data to the CH node in a single-hop manner. When the average distance from all common nodes in the entire cluster to the CH is small, the energy consumption of the common nodes when forwarding data can be effectively reduced. Therefore, the average distance within the cluster is used as the distance indicator, and the calculation method is as follows:

[0117] (20)

[0118] The calculation method of the node score within the cluster is shown in formula (21).

[0119] (twenty one),

[0120] in, and are energy index and distance index respectively, and their calculation methods are shown in formula (19) and formula (20). and Determine the energy index and the weight of the energy index when calculating the score of node i, satisfying Before using formula (26) to calculate the node score, the energy factor and distance factor need to be normalized so that they maintain the same magnitude. The specific normalization operation is shown in formula (22) and formula (23).

[0121] (twenty two),

[0122] (twenty three),

[0123] In order to select the CH in the network more objectively and fairly, this embodiment determines the corresponding weights by using the entropy weight method. and The calculation method of is shown in formula (24) and formula (25).

[0124] (twenty four),

[0125] (25),

[0126] in and is the information entropy of the distance factor and the energy factor, and the calculation method is shown in formula (26) and formula (27).

[0127] (26),

[0128] (27)

[0129] BWSN has a large span, and CH nodes need to transmit inter-cluster data in a multi-hop manner. However, determining the best intra-cluster route is defined as an NP-Hard problem, and many existing routing protocols use heuristic optimization algorithms to solve this problem. In order to obtain a more energy-efficient inter-cluster data transmission path, the proposed protocol comprehensively considers the network life and the energy consumption balance between nodes to design the fitness function, and solves it through HOA.

[0130] The fitness function constructed in this embodiment It mainly includes energy consumption factor, load factor and energy consumption balance factor. The specific calculation method is shown in formula (28).

[0131] (28)

[0132] in is the energy consumption factor, is the load factor, is the energy consumption balancing factor. , , is the weight coefficient, satisfying .

[0133] 1) Energy consumption factor

[0134] Since the initial energy of nodes in WSN is limited and cannot be re-powered, building a more energy-efficient data transmission path will help extend the network life of WSN. As one of the objective functions for optimizing inter-cluster paths, the specific calculation method is shown in formula (29).

[0135] (29),

[0136] in Represents the data packet that needs to be forwarded by the i-th CH, Indicates the distance of CH data transmission, and are the communication parameters defined by the first-order radio model, Receive the data packet for cluster head i.

[0137] 2) Load factor

[0138] WSN needs to deploy a large number of sensor nodes to obtain more accurate bridge status data, which leads to serious load imbalance problems for nodes in the network. The CH close to the BS needs to forward the data of the CH far away from the BS, which will cause it to consume more energy and fail prematurely due to energy exhaustion. Cluster head load information entropy is often used to measure the balance of network load. The higher the information entropy, the more balanced the network load. In order to make the load between CHs more balanced, the proposed algorithm takes the cluster head load information entropy as another objective function. Its calculation formula is as follows, where represents the load of cluster head i.

[0139] (30),

[0140] 3) Energy consumption balance factor

[0141] The energy consumption variance between CHs is also a factor that must be considered when constructing a reasonable inter-cluster data transmission path. A smaller energy consumption variance indicates that the energy consumption of each node in the network is relatively uniform, which helps to maintain the balance of the entire network. A larger energy consumption variance may mean that the energy consumption of some nodes deviates from the mean and may require further adjustment to improve the overall performance of the network. The calculation method of CH energy consumption variance is shown in formula (31), where represents the energy consumption of cluster head i.

[0142] (31),

[0143] Clustering routing algorithms generally follow the "round" concept proposed by LEACH, and the cluster structure is updated in each round. Although this method enables the network to update the network topology in time when a node dies, ensures the connectivity of the network, and has high robustness, it also causes the nodes to use most of their energy to receive and send various clustering control information, resulting in low node energy utilization. In addition, most of the nodes in the WSN are embedded in the structure, the node position will not change easily, and the network topology is relatively stable. Therefore, the present invention proposes a re-clustering mechanism based on an adaptive threshold. According to the remaining energy of the CH and the degree of energy consumption balance of the nodes in the network, it is determined whether to re-divide the cluster structure and select a new CH. The specific threshold calculation method is shown in formula (32).

[0144] (32),

[0145] in, represents the residual energy of node j, is the number of nodes in the i-th cluster, Used to adjust the overall level of the threshold, with a value of 0.5. is the standard deviation of the node's residual energy, which can reflect the balance of node energy distribution in the current network. When it is large, it means that the node energy is unevenly distributed, and the current network cluster structure and CH distribution are unreasonable. Therefore, it is necessary to build a new cluster and select a new CH to ensure that nodes with lower energy can have a longer survival time, thereby improving the overall life and stability of the network.

[0146] After determining the cluster topology and inter-cluster transmission path in the network, BS broadcasts Reqst_Msg to the entire network. When the node successfully receives the message, it means that the network operation enters the stable stage. In this stage, ordinary nodes in each cluster transmit the collected data to CH in their corresponding time slots. CHs compress the data in the cluster and transmit the data to BS according to the best inter-cluster transmission path. When all nodes complete the data transmission, the network automatically enters the next round.

[0147] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0148] In the present invention, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0149] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0150] It should also be noted that in the system and method of the present invention, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present invention.

[0151] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present invention is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0152] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0153] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A multi-hop routing method for wireless sensor networks based on a walking algorithm, characterized in that: include: The hiking algorithm is used to optimize the initial clustering center of the fuzzy C-means clustering algorithm. Based on the optimized initial clustering center, the fuzzy C-means clustering algorithm is used to divide the network into several clusters of equal size. Select a cluster head for the cluster; Constructing inter-cluster data transmission paths based on the selected cluster heads; A wireless sensor network is constructed based on the selected cluster heads and inter-cluster data transmission paths.

2. The multi-hop routing method for wireless sensor networks based on the walking algorithm according to claim 1 is characterized in that: The network is divided into several clusters of equal size using the fuzzy C-means clustering algorithm based on the optimized initial cluster centers, including: The objective function is continuously minimized until it reaches the convergence condition to obtain the final clustering result. The objective function is: ， Among them, M represents the entire sample set, K is the number of clusters, and U is a The two-dimensional matrix, matrix elements The value of represents the probability that the sample point i is divided into the jth cluster, C is the cluster center set, Represents the distance between sample point i and the center of the jth cluster.

3. The multi-hop routing method for wireless sensor networks based on the walking algorithm according to claim 2 is characterized in that: The sum of the elements in each column of the two-dimensional matrix U is 1.

4. The multi-hop routing method for wireless sensor networks based on the walking algorithm according to claim 1 is characterized in that: Select a cluster head for the cluster, including: Considering the energy index and distance index of the node, the entropy weight method is used to calculate the score of the candidate cluster head, and the node with the highest score is set as the cluster head.

5. The multi-hop routing method for wireless sensor networks based on the walking algorithm according to claim 4 is characterized in that: The energy index is the ratio of the remaining energy of the current node to the average remaining energy of the surrounding neighboring nodes; The distance indicator is the average distance within the cluster.

6. The multi-hop routing method for wireless sensor networks based on the walking algorithm according to claim 4 is characterized in that: The calculation formula for calculating the score of the candidate cluster head using the entropy weight method is: ， in and are energy index and distance index respectively. and is the weight used when calculating the node score.

7. The multi-hop routing method for wireless sensor networks based on the walking algorithm according to claim 6 is characterized in that: Said and It is calculated based on the information entropy of the distance factor and the information entropy of the energy factor.

8. The multi-hop routing method for wireless sensor networks based on the walking algorithm according to claim 1 is characterized in that: The method of constructing an inter-cluster data transmission path based on the selected cluster head includes: The fitness function is designed based on the network life and the energy consumption balance between nodes, and the fitness function is solved by the hiking algorithm to obtain the optimal intra-cluster routing, thereby obtaining the inter-cluster data transmission path. The fitness function for: in is the energy consumption factor, is the load factor, is the energy consumption balance factor, , , is the weight coefficient.

9. The multi-hop routing method for wireless sensor networks based on the walking algorithm according to claim 1, characterized in that: The construction of a wireless sensor network based on the selected cluster heads and inter-cluster data transmission paths includes: According to the remaining energy of the cluster head and the degree of energy consumption balance of the nodes in the network, it is determined whether to re-divide the cluster structure and select a new cluster head. The threshold calculation formula is: ， in, represents the residual energy of node j, is the number of nodes in the i-th cluster, To adjust the coefficient of the overall level of the threshold, is the standard deviation of the node’s residual energy, is the weight.

10. The multi-hop routing method for wireless sensor networks based on the walking algorithm according to claim 1, characterized in that: The construction of a wireless sensor network based on the selected cluster heads and inter-cluster data transmission paths includes: The ordinary nodes in each cluster transmit the collected data to the cluster head in their corresponding time slots. The cluster head compresses the data within the cluster and transmits the data to the base station according to the optimal inter-cluster data transmission path. When all nodes have completed the data transmission, the network automatically enters the next round of data transmission.

Images