Chain-like cluster-intra routing formation method based on constrained minimum spanning tree

Abstract

The application discloses a chain cluster in-route forming method, device, medium and program product based on a restrained minimum spanning tree, and the method comprises the following steps: step 1, determining the distribution of sensor nodes and sink nodes, and determining the wireless sensor network layering; step 2, obtaining the energy consumption and position information of each layer cluster in-node; step 3, cluster head node election; step 4, forming the cluster in-initial route according to the restrained minimum spanning tree algorithm; step 5, optimizing the cluster in-initial route by using the adjacent node exchange algorithm and the link cross detection elimination algorithm; step 6, cluster in and inter-cluster data transmission; and step 7, repeating steps 2 to 6 until the wireless sensor network cannot continue to work. The application forms the chain route by using the restrained minimum spanning tree, realizes the cluster in-energy load balancing, and can prolong the wireless sensor network life cycle.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to a method, apparatus, medium, and program product for forming intra-cluster routes based on a constrained minimum spanning tree. Background Technology

[0002] With the booming development of the Internet of Things (IoT), higher demands are being placed on the realization of massive intelligent data sensing. Some new technologies, such as autonomous driving, license plate recognition, and collision detection, largely rely on high-precision and low-latency data acquisition. Wireless sensor networks (WSNs) are a crucial component of intelligent data sensing. WSNs are characterized by low deployment costs and fast networking speeds, and have been widely applied in environmental monitoring, smart buildings, disaster relief, healthcare, workforce monitoring, and military surveillance. For example, in intelligent transportation systems, sensor networks are used for automatic monitoring and fault detection, helping to reduce manual inspections, lower maintenance costs, and improve safety and reliability. In smart medicine, by deploying wireless physiological sensor nodes, certain physiological parameters of the body are collected to obtain information about the patient's condition, enabling the efficient development of reasonable treatment plans. In environmental monitoring, sensor networks can be used for target tracking. Deployed sensors can collect various information about any moving target within a wireless sensor network, and this is commonly used in the military field.

[0003] Generally, sensor networks consist of a large number of battery-powered sensor nodes (hereinafter referred to as nodes) with limited computing and communication capabilities. Due to their small size and low cost, nodes are often deployed in harsh and complex environments. However, in some complex environments, charging or replacing their batteries once deployed is impractical. In modern informatics, to reduce deployment costs, sensor networks are required to operate stably for extended periods. If some nodes run out of power, the data acquisition accuracy in that area will be significantly reduced. When the proportion of nodes that have run out of power reaches a certain threshold, it may cause the network topology to disconnect, severely impacting network stability and increasing communication overhead. Furthermore, in the modern information industry, especially in smart cities and cloud computing applications, reducing communication costs is of paramount importance. Therefore, how to improve network energy efficiency and extend network lifespan has always been a focus of attention for both academia and industry.

[0004] Designing energy-efficient routes is one of the most effective ways to improve the energy efficiency of wireless sensor networks. Routing protocols for wireless sensor networks are generally divided into two main categories: 1) flat routing protocols and 2) hierarchical routing protocols. In flat routing protocols, nodes transmit data through direct communication. Flat routing protocols are simple to implement but only suitable for small-scale networks. For large-scale networks, hierarchical routing protocols are a more suitable choice. Hierarchical routing protocols utilize clustering mechanisms to logically divide the entire network into multiple independent clusters. Data collected by each cluster is aggregated through the cluster head node (CH) and transmitted to the sink node (Sink). Based on data aggregation, the amount of data and the energy consumption for data transmission can be significantly reduced. Therefore, hierarchical routing protocols can effectively extend the network's lifetime.

[0005] In the process of implementing the technical solutions of the embodiments of the present invention, the inventors of this application have discovered at least the following technical problems in the prior art:

[0006] Hierarchical routing protocols typically feature multi-hop data transmission and reverse funnel traffic distribution, which inevitably leads to an imbalance in energy consumption. Specifically, sensors closer to the sink node consume energy much faster than those farther away, a phenomenon known as the "hotspot problem." This imbalance in energy consumption can cause premature failures of critical nodes, ultimately leading to network fragmentation.

[0007] In summary, existing technologies have the technical problem of failing to balance the energy consumption of communication within a cluster. Summary of the Invention

[0008] This invention provides a method, device, medium, and program product for forming chain-like intra-cluster routes based on constrained minimum spanning tree, which solves the technical problem of the inability to balance the energy consumption of intra-cluster communication in the prior art.

[0009] One embodiment of the present invention provides a method for forming intra-cluster routes based on a constrained minimum spanning tree. The method includes: Step 1, determining the distribution of sensor nodes and sink nodes, and determining the hierarchical structure of the wireless sensor network; Step 2, obtaining the energy consumption and location information of nodes within each cluster layer; Step 3, electing cluster head nodes; Step 4, forming initial intra-cluster routes according to the constrained minimum spanning tree algorithm; Step 5, optimizing the initial intra-cluster routes using a neighbor node exchange algorithm and a link cross-detection and elimination algorithm; Step 6, transmitting data within and between clusters; Step 7, repeating steps 2 to 6 until the wireless sensor network can no longer operate.

[0010] Optionally, determining the distribution of sensor nodes and aggregation nodes, and determining the layering of the wireless sensor network, specifically involves: dividing the rectangular network into multiple clusters of the same size using a grid-like partitioning method; determining that the sensor nodes are randomly deployed; determining that the aggregation node is located at the center of the network topology; and determining the layering of the wireless sensor network.

[0011] Optionally, obtaining the energy consumption and location information of nodes within each cluster layer specifically involves obtaining the energy consumption and location information of nodes within each cluster layer that are broadcast.

[0012] Optionally, the cluster head node election specifically involves electing the node with the highest energy within the cluster as the cluster head node.

[0013] Optionally, the step of forming the initial intra-cluster route according to the constrained minimum spanning tree algorithm specifically involves forming the initial intra-cluster route according to the constrained minimum spanning tree algorithm based on Kuruskar.

[0014] Optionally, the intra-cluster and inter-cluster data transmission specifically includes: during intra-cluster data transmission, each cluster member participates in data fusion and transmits the data to the cluster head node through a predetermined route; during inter-cluster data transmission, if it is a cluster head node in the first layer, the data is directly transmitted to the aggregation node; otherwise, the cluster head node selects the cluster head node of the adjacent inner layer located in the current layer as the next-hop cluster head node.

[0015] Optionally, the cluster head node selects the cluster head node of the adjacent inner layer of the current layer as the next hop cluster head node, specifically: based on the distance and remaining energy, the cluster head node selects the cluster head node of the adjacent inner layer of the current layer as the next hop cluster head node.

[0016] On the other hand, embodiments of the present invention also provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a chain cluster routing formation method based on a constrained minimum spanning tree.

[0017] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of a chain cluster routing method based on a constrained minimum spanning tree.

[0018] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of a chain cluster routing method based on a constrained minimum spanning tree.

[0019] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0020] A chain-like intra-cluster routing method based on constrained minimum spanning tree is disclosed. The method includes: Step 1, determining the distribution of sensor nodes and sink nodes, and determining the hierarchical structure of the wireless sensor network; Step 2, obtaining the energy consumption and location information of nodes within each cluster layer; Step 3, electing the cluster head node; Step 4, forming initial intra-cluster routes according to the constrained minimum spanning tree algorithm; Step 5, optimizing the initial intra-cluster routes using a neighbor node exchange algorithm and a link cross-detection and elimination algorithm; Step 6, transmitting data within and between clusters; Step 7, repeating steps 2 to 6 until the wireless sensor network can no longer operate. This invention utilizes constrained minimum spanning tree to form chain-like routes, which can effectively alleviate the uneven network load within the cluster, achieve energy load balancing within the cluster, solve the technical problem of the inability to balance energy consumption in intra-cluster communication in existing technologies, and extend the lifespan of the wireless sensor network. Attached Figure Description

[0021] Figure 1 This is a flowchart of a chain cluster intra-routes formation method based on a constrained minimum spanning tree according to an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of a wireless sensor network model in one embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of a method for forming and optimizing initial routes within a cluster according to an embodiment of the present invention;

[0024] Figure 4 This is a comparison diagram of intra-cluster routing in one embodiment of the present invention and routing formed by other traditional algorithms;

[0025] Figure 5 This is a schematic diagram illustrating the effect of the neighbor node swapping algorithm in one embodiment of the present invention;

[0026] Figure 6 This is a schematic diagram illustrating the effect of the link cross-detection and elimination algorithm in one embodiment of the present invention;

[0027] Figure 7 This is a schematic diagram of a vector crossing in one embodiment of the present invention. Detailed Implementation

[0028] This invention provides a method, device, medium, and program product for forming chain-like intra-cluster routes based on constrained minimum spanning tree, which solves the technical problem of the inability to balance the energy consumption of intra-cluster communication in the prior art.

[0029] This invention provides a chain-like intra-cluster routing method based on constrained minimum spanning trees, which can effectively alleviate the problem of uneven network load within a cluster and improve the lifecycle of wireless sensor networks. Examples are presented using Matlab r2018a for technical simulation, conducting experiments with different numbers of nodes to illustrate the method and compare it with other classic clustering methods. It should be noted that this invention is not limited to the applications supported by the aforementioned devices and samples; it is also applicable to any device with equivalent properties that can achieve the functions described above, and in real-world environments.

[0030] like Figure 1 As shown, the embodiment mainly includes the following steps:

[0031] Step 1: Determine the distribution of sensor nodes and aggregation nodes to define the hierarchical structure of the wireless sensor network.

[0032] Models of wireless sensor networks, such as Figure 2 As shown, the rectangular network is divided into multiple clusters of equal size using a grid-like partitioning method. The sink node is located at the center of the network topology, and the sensor nodes (hereinafter referred to as nodes) are deployed randomly.

[0033] Step 2: Obtain the energy consumption and location information of each cluster head node.

[0034] Cluster nodes can communicate their device information to the wireless sensor network via broadcast or other message methods.

[0035] The energy consumption of wireless sensor networks mainly consists of two parts: the energy cost of data transmission and reception. Specifically, the energy cost of transmitting a k-bit data packet is approximately E... Tx and receiving E Rx The energy consumption calculation method is expressed by Equation 1 and Equation 2 respectively:

[0036]

[0037] E Rx =k*E elec Formula 2

[0038] Among them, E elec The energy consumed by a node in receiving or transmitting each bit of data is represented by d, where d is the transmission distance between the receiver and transmitter. The threshold distance d0, defined to determine the transmission model, is calculated as shown in Equation 3:

[0039]

[0040] Where, ε fs and ε mpHere, d0 represents the energy loss factor for the free-space model and the multipath fading model, respectively, and is typically 87.7m. When d < d0, the transmission follows the free-space model; otherwise, it follows the multipath fading model. To minimize energy consumption during transmission, this invention employs the free-space model.

[0041] In intra-cluster communication, the energy consumption of node j in cluster i depends on whether the node is a leaf node or a cluster head node, and is calculated as follows:

[0042]

[0043] Where LF and HD represent the sets of leaf nodes and cluster head nodes, respectively, d i (j, j+1) represents the distance between node j in the i-th cluster and its next hop node j+1.

[0044] Because this invention uses a chain topology for intra-cluster communication, for M i A cluster of nodes, with M forwarding and receiving operations within the cluster in each round. i -1. According to Equation 4, the energy consumption of intra-cluster communication is calculated as follows:

[0045]

[0046] In inter-cluster communication, energy loss mainly occurs between adjacent cluster head nodes. Besides the cluster head nodes acting as leaf nodes, which only need to participate in data transmission, other cluster head nodes also need to participate in data reception and transmission. Therefore, the inter-cluster communication energy consumption of cluster i can be expressed as:

[0047]

[0048] Among them, l i It is the distance from cluster i to the next hop cluster i+1.

[0049] Therefore, in each round, the total energy consumption of the entire network can be obtained as:

[0050]

[0051] Step 3, Cluster head node election.

[0052] This invention uses the default cluster head selection method, where the node with the highest energy within the cluster becomes the cluster head. However, in practical applications, other algorithms can be used for cluster head election, such as the classic LEACH protocol, HEED protocol, and R-LEACH protocol; this invention is not limited to these. The classic LEACH protocol involves each node randomly selecting a value between 0 and 1. If the selected value is less than a certain threshold, that node becomes the cluster head. Depending on the method of threshold calculation, the HEED protocol considers the remaining energy levels and average minimum reachable power (AMRP) of each node, ensuring uniform cluster head selection. Similarly, the R-LEACH protocol comprehensively considers the optimal values ​​of the node's initial energy, remaining energy, and the number of cluster heads for cluster head rotation. Furthermore, the cluster head election algorithms of these protocols can be combined with the formation method proposed in this paper.

[0053] Step 4: Form initial intra-cluster routes according to the constrained minimum spanning tree algorithm.

[0054] The energy consumption of routing within a cluster was analyzed using a graph theory model. In sensor networks, each cluster can be viewed as an undirected, fully connected graph. Let G... i (Vi, Ei) represents the graph formed by the corresponding clusters, V i Let Ei represent the set of all sensor nodes within the cluster, and let Ei represent the set of edges between any two nodes within the cluster. i (a, b)a, b∈V i This represents the edge weight of the graph, whose value is set to be equal to the routing metric, and is calculated as follows:

[0055] W i (a, b) = |d i (a, b)| 2 Formula 8

[0056] Where, |di(a,b)| 2 This represents the square of the distance between nodes a and b. The energy consumption of transmission can be obtained by performing a simple linear transformation on Equation 1. Specifically, the calculation method for communication energy consumption is as follows:

[0057] E T (a, b) = k*E elec +k*ε fs *W(a, b) Equation 9

[0058] Based on the routing metric proposed above, Equation 5 can be rewritten as:

[0059]

[0060] in It is an M i ×Mi A 0-1 matrix. If node k is the next hop of node j, then (j, k) = 1, otherwise 0. For a given cluster, the number of nodes M i Always fixed. Full convergence mode ensures that the size of each packet is fixed, therefore 2(M i -1)*k * E elec and k * E ls In Equation 10, all values ​​are fixed. To further simplify the problem, the constants in Equation 10 are removed, and the remaining part is denoted as... The calculation method is as follows:

[0061]

[0062] Similarly, for any solution Using Formula Nine By performing a linear transformation, the corresponding intra-cluster communication energy consumption can be obtained. Therefore, determining the optimization problem and solution of intra-cluster routing is crucial. The optimization problems have the same solution. This means finding a chain-like route such that every node in the cluster is on the route path, which has the same definition as the shortest Hamiltonian path.

[0063] Note that the Hamiltonian path itself is a constrained spanning tree, where the degree of each node cannot exceed 2. Therefore, solving the shortest Hamiltonian path problem is equivalent to finding a constrained minimum spanning tree (MST). Constructing an MST typically employs the Kuruskar algorithm and the Prim algorithm. Due to the degree constraint on the spanning tree nodes in the Hamiltonian path, the Prim algorithm often degenerates into a traditional greedy algorithm. A traditional greedy algorithm is exemplified by the PEGASIS (Power-Efficient Gathering in Sensor Information Systems) algorithm. PEGASIS is a classic chain-topology-based protocol that, based on the principle of proximity, starts with the sensor node farthest from the base station and selects the sensor node closest to the current node as the next hop, until a chain-like path is formed. Figure 4 Two different path generation algorithms are given, where the nth edge represents the edge selected in the nth round. For example... Figure 4 As shown in (a), in traditional path generation algorithms, each node only connects to its nearest neighbor. Figure 4(a) Any edge and its adjacent edges are always numbered consecutively, which is typical of a greedy algorithm. However, a significant drawback of this algorithm is that the number of available edges decreases towards the end of the path generation process. For example, edge 6 is considered last, and it only has edges e(f, g) and e(g, a) available. This easily leads to long links and intersections, causing unnecessary energy loss. To address this, this invention employs the CMST path generation algorithm based on Kruskal. The main idea is to enumerate all edges within the cluster in ascending order according to the routing metric. If the two nodes of an edge do not belong to the same connected component, they are merged and added to the same connected component, and the edge is added to the solution set. Continue this process until all nodes belong to the same connectivity class. Note that during this process, the degree of each node must not exceed 2. For example... Figure 4 As shown in (b), CMST can alleviate the long-link problem in traditional greedy algorithms to a certain extent and has better performance.

[0064] Please also refer to Figure 1 and Figure 3 Step 5: Optimize the initial intra-cluster routing using the neighbor node switching algorithm and the link cross detection elimination algorithm.

[0065] In the neighbor node swapping algorithm, please also refer to Figure 1 and Figure 5 Let e(a, b) denote the inequality between nodes a and b, when the following inequalities hold:

[0066] W(i-2,i-1)+W(i-1,i)+W(i,i+1)>W(i-2,i)+W(i,i-1)+W(i-1,i+1)

[0067] This means that by swapping the positions of nodes i and i-1 in the solution, a shorter path can be obtained; that is, the next hop of node i-2 to i changes from nodes i-1, i, i+1 to nodes i, i+1, i-1. For example... Figure 5 As shown, through the neighbor node swapping algorithm, Figure 5 (b) path length ratio Figure 5 (a) has a shorter path length.

[0068] In link cross-link detection and elimination algorithms, the general cross-link detection algorithm calculates the coordinates of the intersection point of the lines containing the two edges of e(i, i+1) and e(j, j+1), and then determines whether the intersection point lies on either edge. This method involves complex operations such as matrix multiplication, matrix inversion, and matrix rank calculation. This invention proposes a simple cross-link detection algorithm based on computational geometry, the specific method of which is as follows:

[0069] Please also refer to Figure 1 and Figure 7 ,if Across Then vector and In vector On both sides, which means, Similarly, if Across So there is. Notice and They cannot be on the same line.

[0070] If C1, C2, and C3 satisfy the following condition, then it means that there is an intersection between e(i, i+1) and e(j, j+1):

[0071]

[0072]

[0073]

[0074] like Figure 6 As shown in (a), in the initial solution P, there is an intersection between e(i, i+1) and e(j, j+1), where (i < j, i+1 < j). The step to eliminate the intersection is to rearrange the nodes from i+1 to j in reverse order. Figure 6 As shown in (b), P is adjusted from [1, ..., ii+1, i+2, ..., j-1, j, j+1, ..., Mi] to [1...ij, j-1, ..., i+2, i+1, j+1, ..., Mi]. This eliminates the overlap between e(i, i+1) and e(j, j+1).

[0075] Step 6: Intra-cluster and inter-cluster data transmission.

[0076] Data transmission consists of two parts: intra-cluster and inter-cluster data transmission. During intra-cluster data transmission, cluster members transmit data to the cluster head node via a predefined route. In this process, due to the adoption of a chain topology, each cluster member needs to participate in data fusion to ensure network load balancing. During inter-cluster data transmission, the cluster head node selects the most suitable next-hop cluster head node, requiring that the next-hop node be located in an adjacent inner layer of the current layer. If it is a cluster head node within the first layer, the data is directly transmitted to the sink node. The selection of the next-hop cluster head node is based on distance and remaining energy; cluster head nodes with shorter distances and more remaining energy are more likely to be selected as next-hop nodes.

[0077] Step 7: Repeat steps 2 through 6 until the wireless sensor network can no longer function.

[0078] The process involves repeatedly performing intra-cluster and inter-cluster transmission steps and rotating cluster head nodes to keep the entire wireless sensor network operational until it becomes unusable.

[0079] Another embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a chain cluster routing method based on a constrained minimum spanning tree.

[0080] Another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for forming intra-cluster routes based on a constrained minimum spanning tree.

[0081] Another embodiment of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of a chain cluster routing method based on a constrained minimum spanning tree.

[0082] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0083] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0086] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for forming intra-cluster routes in a chain-like structure based on a constrained minimum spanning tree, characterized in that, The method is used to achieve energy load balancing within a cluster and extend the lifetime of the wireless sensor network. The method includes: Step 1: Determine the distribution of sensor nodes and aggregation nodes to determine the layering of the wireless sensor network; Step 2: Obtain the energy consumption and location information of nodes within each cluster layer; Step 3, Cluster head node election; Step 4: Form initial intra-cluster routes according to the constrained minimum spanning tree algorithm based on Kuruskar, enumerate all edges in the cluster in ascending order according to the route metric, merge connected components and ensure that the degree of each node does not exceed 2. Step 5: Optimize the initial intra-cluster route using the neighbor node swapping algorithm and the link cross detection and elimination algorithm. The neighbor node swapping algorithm obtains a shorter path by swapping node positions when inequalities are met. The link cross detection and elimination algorithm is a simple cross detection algorithm based on computational geometry, which determines link crosses by vector cross product and eliminates crosses by reordering nodes. Step 6: Intra-cluster and inter-cluster data transmission; Step 7: Repeat steps 2 through 6 until the wireless sensor network can no longer function.

2. The method as described in claim 1, characterized in that, The determination of the distribution of sensor nodes and aggregation nodes, and the determination of the layering of the wireless sensor network, specifically involves: The rectangular network is divided into multiple clusters of the same size using a grid-like partitioning method. The sensor nodes are randomly deployed, the aggregation node is located at the center of the network topology, and the wireless sensor network is layered.

3. The method as described in claim 1, characterized in that, The specific steps for obtaining the energy consumption and location information of nodes within each cluster layer are as follows: We obtain the energy consumption and location information of each node within each cluster, which is transmitted via broadcast.

4. The method as described in claim 1, characterized in that, The cluster head node election is specifically as follows: The node with the highest energy within the cluster is elected as the cluster head node.

5. The method as described in claim 1, characterized in that, The intra-cluster and inter-cluster data transmission specifically includes: During intra-cluster data transmission, each cluster member participates in data fusion and transmits the data to the cluster head node through a predefined route; During inter-cluster data transmission, if the cluster head node is within the first layer, the data is directly transmitted to the sink node; otherwise, the cluster head node selects the cluster head node of the adjacent inner layer in the current layer as the next-hop cluster head node.

6. The method as described in claim 1, characterized in that, The cluster head node selects the cluster head node of the adjacent inner layer located in the current layer as the next-hop cluster head node, specifically: Based on distance and remaining energy, the cluster head node selects the cluster head node of the adjacent inner layer in the current layer as the next hop cluster head node.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

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