Data transmission method, data transmission device, electronic equipment and computer program product

Abstract

The invention discloses a data transmission method, a data transmission device, electronic equipment and a computer program product. The method comprises the following steps: acquiring a sensor node set of a wireless sensor network; a plurality of sensor nodes in the sensor node set are iteratively clustered for multiple times to obtain a plurality of clusters, and in each iteration clustering process, the clustering evaluation index of each sensor node is determined according to the residual energy and the distance equilibrium factor of each sensor node in the candidate node set, and the clustering evaluation index of each sensor node is determined according to the residual energy and the distance equilibrium factor of each sensor node in the candidate node set; the sensor node with the minimum clustering evaluation index is selected as a cluster head node of the current clustering, and the distance equilibrium factor is determined at least according to the distance between the sensor nodes; and transmitting the collected data of each sensor node to the cluster head node by the plurality of sensor nodes in the same cluster according to a multi-hop transmission mode. According to the invention, the technical problem that the traditional wireless sensor network cannot reduce the energy consumption while ensuring the data transmission quality and network coverage is solved.

Description

Technical Field

[0001] This invention relates to the field of communications, and more specifically, to a data transmission method, a data transmission device, an electronic device, and a computer program product. Background Technology

[0002] A Wireless Sensor Network (WSN) is a wireless, self-organizing sensor network composed of a large number of sensor nodes, widely used in various fields. Because each sensor node has limited energy, WSNs typically perform specific tasks collaboratively and transmit monitored or sensed data to a base station.

[0003] In existing wireless sensor networks, an indiscriminate data transmission strategy is often adopted, which does not fully consider the actual value of the data. This leads to delays in the transmission of critical data and bandwidth being occupied by low-value data for high-priority information, thereby affecting network timeliness and task response efficiency.

[0004] Furthermore, the limited battery capacity of WSNs cannot support the needs of long-term, large-scale data monitoring, making network power management a core issue in wireless sensor network design. Excessive power consumption leads to nodes running out of energy prematurely, reducing data transmission reliability, shortening network lifespan, and impacting overall network stability and availability. Therefore, reducing the power consumption of wireless sensor networks while ensuring data transmission quality and network coverage has become an important research direction.

[0005] There is currently no effective solution to the technical problem that traditional wireless sensor networks cannot reduce energy consumption while ensuring data transmission quality and network coverage. Summary of the Invention

[0006] This invention provides a data transmission method, a data transmission device, an electronic device, and a computer program product to at least solve the technical problem that traditional wireless sensor networks cannot reduce energy consumption while ensuring data transmission quality and network coverage.

[0007] According to one aspect of the present invention, a data transmission method is provided, comprising: acquiring a set of sensor nodes of a wireless sensor network, wherein each sensor node in the set corresponds to a sensor in the wireless sensor network; performing multiple iterative clustering on a plurality of sensor nodes in the set to obtain a plurality of clusters, wherein, in each iteration of clustering, a clustering evaluation index for each sensor node is determined based on an energy balance factor and a distance balance factor for each sensor node in a candidate node set, and the sensor node with the smallest clustering evaluation index is selected as the cluster head node for this clustering, wherein the candidate node set includes: sensor nodes in the sensor set that were not clustered before this clustering, the energy balance factor is determined based on the remaining energy of the sensor node, the distance balance factor is determined based at least on the distance between the sensor nodes, the clustering evaluation index is positively correlated with the remaining energy, and the clustering evaluation index is positively correlated with the distance balance factor; and transmitting the collected data of each sensor node to the cluster head node in a multi-hop transmission manner from a plurality of sensor nodes in the same cluster.

[0008] Optionally, each iteration of the clustering process includes: determining a distance equalization factor for each sensor node based on the sensor location of each sensor node in the candidate node set, wherein the sensor location is used at least to determine a first distance between each sensor node and the center location of the wireless sensor network, and a second distance between each sensor node and other sensors in the candidate node set, the distance equalization factor being positively correlated with the first distance and negatively correlated with the second distance; determining an energy equalization factor and a weighting factor for each sensor node based on the remaining energy of each sensor node in the candidate node set, wherein the energy equalization factor is positively correlated with the remaining energy, and the weighting factor is based on the remaining energy and the pre-defined distance. The difference in energy thresholds is determined to represent the degree of influence of the distance balancing factor and the remaining energy on the clustering evaluation index. Based on the energy balancing factor and the distance balancing factor, and the weighting factors corresponding to the energy balancing factor and the distance balancing factor, the clustering evaluation index for each sensor node is determined. The sensor node with the smallest clustering evaluation index in the candidate node set is selected as the cluster head node for this clustering. Sensor nodes whose node relationships with the cluster head node meet the clustering conditions are selected from the candidate node set to obtain the clusters obtained in this clustering. The clustering conditions at least indicate that a preset number of sensor nodes whose Euclidean distance from the cluster head node meets the preset distance requirement should be selected.

[0009] Optionally, determining the distance equalization factor corresponding to each sensor node based on the sensor position of each sensor node in the candidate node set includes: traversing multiple sensor nodes in the candidate node set to obtain candidate sensor nodes traversed each time, wherein the sensor position of the candidate sensor node is a candidate position; determining the first distance of the candidate sensor node based on the candidate node position and the network center position of the wireless sensor network center; determining the node distance between the candidate sensor node and each sensor node in the candidate node set based on the candidate node position and the sensor positions of other sensor nodes in the candidate node set, and determining the second distance based on the node distance, wherein the second distance is the average value of the node distances; and determining the distance equalization factor of the candidate sensor node based on the ratio of the first distance to the second distance.

[0010] Optionally, determining the energy balance factor and weight factor corresponding to each sensor node based on the remaining energy of each sensor node in the candidate node set includes: traversing multiple sensor nodes in the candidate node set to obtain candidate sensor nodes traversed each time, and determining the remaining energy of the candidate sensor node; determining the energy balance factor of the candidate sensor node based on the ratio of the remaining energy of the candidate sensor node to the initial energy; and assigning corresponding weight factors to the energy balance factor and the distance balance factor based on the difference between the remaining energy of the candidate sensor node and the preset energy threshold, wherein the weight factor includes: the energy weight corresponding to the energy balance factor and the distance weight corresponding to the distance balance factor, the energy weight being negatively correlated with the remaining energy, and the sum of the energy weight and the distance weight being 1.

[0011] Optionally, assigning corresponding weight factors to the energy balancing factor and the distance balancing factor based on the difference between the remaining energy of the candidate sensor node and the preset energy threshold includes: determining whether the remaining energy is greater than the preset energy threshold based on the difference between the remaining energy of the candidate sensor node and the preset energy threshold; if the remaining energy is greater than the preset energy threshold, assigning a first weight to the energy balancing factor and a second weight to the distance balancing factor, wherein the sum of the first weight and the second weight is 1; if the remaining energy is not greater than the preset energy threshold, assigning a third weight to the energy balancing factor and a fourth weight to the distance balancing factor, wherein the sum of the third weight and the fourth weight is 1, and the third weight is greater than the first weight.

[0012] Optionally, determining the sensor nodes in the candidate node set whose node relationships with the cluster head node meet the clustering conditions, and obtaining the clustered clusters obtained in this clustering, includes: counting the number of sensor nodes in the candidate node set that respond to the broadcast content sent by the cluster head node, wherein the sensor corresponding to the cluster head node sends the broadcast content according to a preset distance radius, and the sensors within the preset distance radius respond to the broadcast content by returning an acknowledgment packet to the sensor acting as the cluster head node, and the number of nodes is determined based on the number of acknowledgment packets; and then, based on the number of nodes and the broadcast content sent by the cluster head node... The coverage area of ​​the content is used to determine the node density within the cluster, wherein the coverage area is determined based on the preset distance radius; based on the node density within the cluster, a preset number is determined, wherein the preset number represents the number of sensor nodes in the same cluster as the cluster head node, and the preset number is positively correlated with the node density within the cluster; the sensor nodes in the candidate node set are arranged in ascending order of their Euclidean distance from the cluster head node to obtain a candidate node queue, and the preset number of sensor nodes are selected from the candidate node queue in order from the head to the tail to obtain the cluster corresponding to the cluster head node.

[0013] Optionally, transmitting the collected data of each sensor node to the cluster head node in a multi-hop transmission manner from multiple sensor nodes in the same cluster includes: in each hop transmission, traversing multiple sensor nodes in the cluster to obtain a data sending node; detecting the remaining energy of each sensor node in the cluster and the link quality between each sensor node and the data sending node; selecting a data receiving node from the multiple sensor nodes in the cluster based on the remaining energy and the link quality, wherein the remaining energy and the link quality of the data receiving node meet preset transmission conditions; in each hop transmission, the data sending node sends a data collection packet to the data receiving node, and determines the data sending node and the data receiving node for the next hop transmission, until the data receiving node is the cluster head node, wherein the data sending node for the next hop transmission is the data receiving node for the current hop transmission, and the data collection packet includes at least: the collected data of the sensor that is the data sending node, and the data collection packet received by the data sending node.

[0014] Optionally, selecting a data receiving node from multiple sensor nodes in the cluster based on the remaining energy and the link quality includes: determining the importance of each correlation factor influencing the selection of the data receiving node, wherein the correlation factors include at least the remaining energy and the link quality, and the importance is a numerical representation of the correlation factor's influence on the selection of the data receiving node; determining the factor weight of each correlation factor based on the ratio of the importance of each correlation factor to the cumulative importance of the multiple correlation factors, wherein the cumulative importance is the sum of the importance of the multiple correlation factors; performing a weighted summation on the multiple correlation factors based on the factor weight of each correlation factor to obtain a candidate score for each sensor node; and selecting the sensor node with the highest candidate score as the data receiving node.

[0015] Optionally, in each hop of transmission, sending the acquisition data packet from the data sending node to the data receiving node includes: in each hop of transmission, detecting the signal strength between the data sending node and the data receiving node; determining the transmission power of the data sending node based on the signal strength, wherein the transmission power is negatively correlated with the signal strength; and sending the acquisition data packet from the data sending node to the data receiving node according to the transmission power.

[0016] Optionally, determining the transmit power of the data transmitting node based on the signal strength includes: obtaining signal correlation parameters that affect the signal strength, wherein the signal correlation parameters include at least: the signal transmission distance between the data transmitting nodes; analyzing the signal transmission distance using the Friesian transmission equation to obtain the transmit power, wherein the transmit power is negatively correlated with the signal transmission distance.

[0017] Optionally, after transmitting the collected data of each sensor node to the cluster head node in a multi-hop transmission manner from multiple sensor nodes in the same cluster, the method further includes: detecting the data value of the collected data transmitted by each sensor node through the cluster head node, wherein the data value is determined at least based on data timeliness, data relevance, and data centralization, the data timeliness is an evaluation value determined based on the time difference between the time the cluster head node receives the collected data and the time the collected data is collected, the data relevance is an evaluation value determined based on the similarity between the collected data and pre-set demand data, the data centralization is an evaluation value determined at least based on the amount of data in the collected data, the data timeliness is negatively correlated with the time difference, the data relevance is positively correlated with the similarity, the data centralization is positively correlated with the amount of data, and the data value is positively correlated with the data timeliness, the data relevance, and the data centralization; and transmitting each piece of collected data to the data center in descending order of data value.

[0018] According to another aspect of the present invention, a data transmission apparatus is also provided, comprising: an acquisition module, configured to acquire a set of sensor nodes of a wireless sensor network, wherein each sensor node in the set corresponds to a sensor in the wireless sensor network; a clustering module, configured to perform multiple iterative clustering on multiple sensor nodes in the set to obtain multiple clusters, wherein, in each iterative clustering process, a clustering evaluation index for each sensor node is determined based on an energy balance factor and a distance balance factor for each sensor node in a candidate node set, and the sensor node with the smallest clustering evaluation index is selected as the cluster head node for this clustering, wherein the candidate node set includes: sensor nodes that were not clustered in the sensor set before this clustering, the energy balance factor is determined based on the remaining energy of the sensor node, the distance balance factor is determined based at least on the distance between the sensor nodes, the clustering evaluation index is positively correlated with the remaining energy, and the clustering evaluation index is positively correlated with the distance balance factor; and a transmission module, configured to transmit the collected data of each sensor node to the cluster head node in a multi-hop transmission manner from multiple sensor nodes in the same cluster.

[0019] According to another aspect of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to execute the above-described data transmission device through the computer program.

[0020] According to another aspect of the present invention, a computer program product is also provided, including computer instructions that, when executed by a processor, implement the steps of the above-described data transmission apparatus.

[0021] In this embodiment of the invention, during the clustering process, by comprehensively considering the remaining energy of sensor nodes and the distance between sensor nodes, the clustering evaluation index is dynamically adjusted to ensure that sensor nodes with sufficient energy are preferentially selected as cluster head nodes, thereby reducing energy consumption and extending network lifetime. The multi-hop transmission mode further optimizes the data transmission path, reduces the energy requirement of a single transmission, and enhances the robustness and coverage of the network. Thus, it achieves the technical effect of reducing sensor energy consumption while ensuring data transmission quality and network coverage, thereby solving the technical problem that traditional wireless sensor networks cannot reduce energy consumption while ensuring data transmission quality and network coverage. Attached Figure Description

[0022] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0023] Figure 1 This is a flowchart of a data transmission method according to an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of a network clustering data transmission method based on adaptive weights and data value according to an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of a cellular wireless sensor network architecture according to an embodiment of the present invention;

[0026] Figure 4 This is a schematic diagram comparing simulation results of how the number of remaining surviving nodes in a sensor network changes with the number of rounds of network operation, according to an embodiment of the present invention.

[0027] Figure 5 This is a schematic diagram comparing simulation results of changes in the total VOI of a sensor network according to an embodiment of the present invention;

[0028] Figure 6 This is a schematic diagram of a data transmission device according to an embodiment of the present invention;

[0029] Figure 7 This is a structural block diagram of a computer terminal according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] First, some nouns or terms that appear in the description of the embodiments of this application shall be interpreted as follows:

[0033] WSN (Wireless Sensor Network) is a network composed of multiple wireless sensor nodes that can autonomously collect environmental data and transmit and share it wirelessly. WSNs are widely used in environmental monitoring, smart agriculture, industrial monitoring, and other fields, and are characterized by flexible deployment and easy maintenance.

[0034] AWDV-NC (Adaptive Weight and Data Value-Based Network Clustering Data Transmission Method): This is a wireless sensor network data transmission method proposed in this invention.

[0035] LEACH (Low-Energy Adaptive Clustering Hierarchy): is an energy-efficient routing protocol designed specifically for Wireless Sensor Networks (WSNs). LEACH significantly extends the network lifetime by introducing a hierarchical architecture and a self-organizing cluster structure.

[0036] ELA-LEACH (Energy and Location-based Adaptive Cluster Head Election Algorithm): This is a cluster head election algorithm that comprehensively considers the remaining energy and location information of nodes.

[0037] KNN (K-Nearest Neighbors) is a simple and intuitive supervised learning algorithm primarily used for classification and regression tasks. Its core idea is based on the principle of "like attracts like": given a test sample, by calculating its distance to all samples in the training set, the K nearest neighbors are selected, and the class or value of the test sample is predicted based on the majority class or average of these K neighbors.

[0038] DA-KNN (Density-Aware-KNN, a density-aware KNN clustering algorithm): is an improved KNN algorithm, specifically improved by adaptively adjusting the k value according to the node density to adapt to different network topologies.

[0039] Friis (Friis Transmission Equation): A fundamental theoretical model in the field of wireless communication, used to describe the propagation loss of signals between two antennas in free space. It is applicable to far-field propagation under ideal conditions, i.e., assuming that the propagation path is a straight line and there are no obstacles or other interferences.

[0040] VOI (Value of Information): refers to the actual benefits and importance that data collected by sensor nodes can bring in a specific application scenario.

[0041] RTC (Data Relevance and Timeliness): This is one of the metrics used to measure the relevance and timeliness between the data transmitted by a node and the required data.

[0042] CLD (Centralization of Data Packets): This is one of the metrics used to measure the degree of centralization of data packets in a cluster. In other words, if the data generated by a node is more concentrated and contains more information, it is considered to have a higher degree of centralization.

[0043] According to an embodiment of the present invention, a data transmission method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0044] Figure 1 This is a flowchart of a data transmission method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0045] Step S102: Obtain the set of sensor nodes of the wireless sensor network, wherein each sensor node in the set of sensor nodes corresponds to a sensor in the wireless sensor network.

[0046] Step S104: Perform multiple iterative clustering on multiple sensor nodes in the sensor node set to obtain multiple clusters. In each iteration of clustering, determine the clustering evaluation index of each sensor node based on the energy balance factor and distance balance factor of each sensor node in the candidate node set, and select the sensor node with the smallest clustering evaluation index as the cluster head node of this clustering. The candidate node set includes: sensor nodes that were not clustered in the sensor set before this clustering. The energy balance factor is determined based on the remaining energy of the sensor node, and the distance balance factor is determined based on at least the distance between the sensor nodes. The clustering evaluation index is positively correlated with the remaining energy and positively correlated with the distance balance factor.

[0047] Step S106: The data collected by each sensor node is transmitted to the cluster head node in a multi-hop transmission manner from multiple sensor nodes in the same cluster.

[0048] In this embodiment of the invention, during the clustering process, by comprehensively considering the remaining energy of sensor nodes and the distance between sensor nodes, the clustering evaluation index is dynamically adjusted to ensure that sensor nodes with sufficient energy are preferentially selected as cluster head nodes, thereby reducing energy consumption and extending network lifetime. The multi-hop transmission mode further optimizes the data transmission path, reduces the energy requirement of a single transmission, and enhances the robustness and coverage of the network. Thus, it achieves the technical effect of reducing sensor energy consumption while ensuring data transmission quality and network coverage, thereby solving the technical problem that traditional wireless sensor networks cannot reduce energy consumption while ensuring data transmission quality and network coverage.

[0049] In step S102 above, the wireless sensor network includes multiple sensors. The multiple sensors in the wireless sensor network can be represented by a set of sensor nodes, and the sensor nodes in the set of sensor nodes correspond one-to-one with the sensors in the wireless sensor network.

[0050] In step S104 above, each iteration of clustering can select a cluster head node from the set of sensor nodes, and then determine the sensor nodes in the same cluster as the cluster head node. Thus, each iteration of clustering can generate a cluster, and multiple iterations of clustering can generate multiple clusters.

[0051] In step S104 above, the candidate node set is determined based on the sensor node set. In the case of the first clustering, the candidate node set is the sensor node set. After the first clustering is completed, the sensor nodes that have been assigned to the clusters will be removed from the sensor node set or the candidate node set. The removed sensor node set or candidate node set will then become the candidate node set for the next clustering.

[0052] Optionally, the area covered by each cluster can be a regular hexagon.

[0053] In step S106 above, multi-hop transmission is an information transmission method that forwards data through multiple intermediate nodes. It is widely used in wireless communication, sensor networks and other fields. Its working principle is that data needs to be relayed through multiple intermediate nodes from the source node to the target node, rather than being transmitted directly. Each node has both data transmission and reception and routing functions, forming a self-organizing network.

[0054] The embodiments described above are applicable to large-scale wireless sensor networks, such as urban infrastructure monitoring and precision irrigation systems in agriculture, and can achieve a dual improvement in network performance and energy efficiency through refined management.

[0055] Optionally, the specific formula for calculating the clustering evaluation index is as follows:

[0056]

[0057] Where n is the total number of nodes in the cellular wireless sensor network, v is the ratio of the number of nodes that have acted as cluster heads to the total number of nodes, and q represents the number of rounds already performed. G represents the number of cluster head nodes that have been selected in the current round; G is the set of cluster head nodes that were not selected in this round (i.e., the set of candidate nodes); E current E represents the current remaining energy of a node. total This represents the initial total energy of the node (i.e., the initial energy); F represents the distance balance factor. and The weights represent the node's remaining energy and distance balance factor. Higher weights have a greater impact on the distance balance factor. (Setting...) and

[0058] Alternatively, the distance balance factor F can be calculated as follows:

[0059]

[0060] Among them, setting (X) r ,Y r(X) represents the center coordinates of the wireless sensor network's center location. i ,Y i ) represents the position coordinates of the current node i in the wireless sensor network (i.e., the sensor node coordinates), (X) j ,Y j ) represents the coordinates of all candidate nodes in the wireless sensor network; the closer the cluster head node is to the central node, the more balanced the energy consumption of the entire network and the longer the network's lifespan; r represents the round of iterative clustering.

[0061] As an optional embodiment, each iteration of the clustering process includes: determining a distance equalization factor for each sensor node based on the sensor location of each sensor node in the candidate node set, wherein the sensor location is used at least to determine a first distance between each sensor node and the center location of the wireless sensor network, and a second distance between each sensor node and other sensors in the candidate node set, the distance equalization factor being positively correlated with the first distance and negatively correlated with the second distance; and determining an energy equalization factor and a weighting factor for each sensor node based on the remaining energy of each sensor node in the candidate node set, wherein the energy equalization factor is positively correlated with the remaining energy, and the weighting factor is based on the remaining energy and... The difference between preset energy thresholds is determined to represent the degree of influence of distance balance factor and remaining energy on cluster evaluation index. Based on the energy balance factor and distance balance factor, and their respective weighting factors, a cluster evaluation index for each sensor node is determined. In the candidate node set, the sensor node with the smallest cluster evaluation index is selected as the cluster head node for this clustering. In the candidate node set, sensor nodes whose node relationships with the cluster head node meet the clustering conditions are selected, resulting in the clusters obtained from this clustering. The clustering conditions are used to indicate at least a preset number of sensor nodes whose Euclidean distance from the cluster head node meets the preset distance requirement.

[0062] The embodiments described above in this application assign a comprehensive evaluation index to each sensor node by quantifying its remaining energy and sensor location. This allows for greater consideration of energy balance factors when remaining energy is high, and greater focus on distance balance factors when remaining energy is low. This approach ensures data transmission efficiency while also fully considering the energy status of nodes, avoiding network fragmentation caused by energy depletion. In this way, wireless sensor networks can allocate resources more intelligently, improving not only the continuity and reliability of data transmission but also extending the overall network lifespan.

[0063] Optionally, the energy balance factor A is calculated as follows:

[0064] As an optional embodiment, determining the distance equalization factor corresponding to each sensor node based on the sensor position of each sensor node in the candidate node set includes: traversing multiple sensor nodes in the candidate node set to obtain candidate sensor nodes traversed each time, wherein the sensor position of the candidate sensor node is a candidate position; determining a first distance of the candidate sensor node based on the candidate node position and the network center position of the wireless sensor network center; determining the node distance between the candidate sensor node and each sensor node in the candidate node set based on the candidate node position and the sensor positions of other sensor nodes in the candidate node set, and determining a second distance based on the node distance, wherein the second distance is the average value of the node distances; and determining the distance equalization factor of the candidate sensor node based on the ratio of the first distance and the second distance of the candidate sensor node.

[0065] The above embodiments of this application, which describe the process of determining the distance equalization factor, demonstrate the impact of spatial distribution on the performance of wireless sensor networks. By calculating the positional differences of sensor nodes relative to the network center and other sensor nodes, the relative importance and transmission efficiency of sensor nodes within the network can be effectively assessed. This location-based evaluation mechanism helps to construct a more rational clustering structure, reduce unnecessary long-distance transmissions, thereby reducing energy consumption and improving the timeliness and accuracy of data transmission.

[0066] Optionally, the first distance L1 is calculated as follows:

[0067] L1=(X r -X i ) 2 +(Y r -Y i ) 2 .

[0068] Alternatively, the second distance L2 can be calculated as follows:

[0069]

[0070] As an optional embodiment, determining the energy balance factor and weight factor corresponding to each sensor node based on the remaining energy of each sensor node in the candidate node set includes: traversing multiple sensor nodes in the candidate node set to obtain the candidate sensor nodes traversed each time, and determining the remaining energy of the candidate sensor nodes; determining the energy balance factor of the candidate sensor nodes based on the ratio of the remaining energy of the candidate sensor nodes to the initial energy; and assigning corresponding weight factors to the energy balance factor and distance balance factor based on the difference between the remaining energy of the candidate sensor nodes and a preset energy threshold. The weight factors include: the energy weight corresponding to the energy balance factor and the distance weight corresponding to the distance balance factor. The energy weight is negatively correlated with the remaining energy, and the sum of the energy weight and the distance weight is 1.

[0071] The embodiments described above in this application achieve a refined evaluation of sensor nodes through energy balancing factors and weighting factors. This ensures that the selection of cluster head nodes considers both the remaining energy of the nodes to avoid premature depletion and the geographical distribution among nodes to optimize data transmission paths. This adaptive weighting mechanism, especially when the remaining energy is below a preset energy threshold, places greater emphasis on the energy balancing factor, which helps to build more balanced clusters, reduces the additional energy consumption caused by long-distance transmission, and thus improves the overall energy utilization efficiency and data transmission stability of the wireless sensor network.

[0072] As an optional embodiment, assigning corresponding weight factors to the energy equalization factor and the distance equalization factor based on the difference between the remaining energy of the candidate sensor node and the preset energy threshold includes: determining whether the remaining energy is greater than the preset energy threshold based on the difference between the remaining energy of the candidate sensor node and the preset energy threshold; if the remaining energy is greater than the preset energy threshold, assigning a first weight to the energy equalization factor and a second weight to the distance equalization factor, wherein the sum of the first weight and the second weight is 1; if the remaining energy is not greater than the preset energy threshold, assigning a third weight to the energy equalization factor and a fourth weight to the distance equalization factor, wherein the sum of the third weight and the fourth weight is 1, and the third weight is greater than the first weight.

[0073] The dynamic allocation strategy of weighting factors in the above embodiments of this application can flexibly adjust the importance of energy balancing factors and distance balancing factors according to the remaining energy status of sensor nodes, ensuring that the most suitable cluster head node can be selected in different network operation stages. This mechanism tends to select nodes closer to the center when sensor nodes have sufficient remaining energy to optimize data transmission paths; while when sensor nodes are nearing depletion of remaining energy, it places greater emphasis on remaining energy to prevent premature failure of critical nodes and impact on overall network connectivity. This method not only improves the energy utilization efficiency of wireless sensor networks but also enhances their adaptability and survivability, making it suitable for various wireless sensor network applications requiring long-term stable operation, such as marine environmental monitoring and wildlife tracking research. Through intelligent management, it achieves continuous optimization of network performance.

[0074] Optionally, the preset energy threshold is 50%, when E current When the remaining energy is greater than 50%, its impact on the overall network performance is small, and an energy weight is set for the remaining energy. The smallest value is 0.4, which is the distance weight from the equilibrium factor. The largest value is 0.6; when E current When the energy utilization rate is less than 50%, the sensor network has insufficient energy, so the remaining energy is weighted accordingly. Increased to 0.6, distance weight of the equilibrium factor It was reduced to 0.4.

[0075] As an optional embodiment, determining sensor nodes in the candidate node set whose node relationships with the cluster head node meet the clustering conditions, and obtaining the clusters obtained in this clustering includes: counting the number of sensor nodes in the candidate node set that respond to the broadcast content sent by the cluster head node, wherein the sensor corresponding to the cluster head node sends the broadcast content according to a preset distance radius, and the sensors within the preset distance radius respond to the broadcast content and return an acknowledgment packet to the sensor acting as the cluster head node, and the number of nodes is determined according to the number of acknowledgment packets; determining the node density within the cluster based on the number of nodes and the coverage area of ​​the broadcast content sent by the cluster head node, wherein the coverage area is determined according to the preset distance radius; determining a preset number based on the node density within the cluster, wherein the preset number represents the number of sensor nodes in the same cluster as the cluster head node, and the preset number is positively correlated with the node density within the cluster; arranging the sensor nodes in the candidate node set in ascending order of their Euclidean distance from the cluster head node to obtain a candidate node queue, and selecting a preset number of sensor nodes from the candidate node queue in order from the head to the tail of the queue to obtain the cluster corresponding to the cluster head node.

[0076] In the embodiments described above, the cluster generation strategy ensures that each cluster head node is surrounded by a group of well-distributed sensor nodes with sufficient energy. This not only optimizes the local data transmission path but also promotes the balanced utilization of network resources. By calculating the node density within the cluster and adjusting the preset number accordingly, data transmission efficiency can be maintained while avoiding communication bottlenecks or coverage blind spots caused by excessively dense or sparse sensor nodes. By rationally dividing the clusters, rapid data collection and processing can be achieved, while reducing energy waste and improving the overall performance and reliability of the wireless sensor network.

[0077] Optionally, the cluster head node broadcasts its unique identifier with a broadcast radius of R; sensor nodes within the broadcast range, upon receiving the identifier, return an acknowledgment packet (ACK) to complete the interaction; the formula for calculating the node density within the cluster is as follows:

[0078]

[0079] Where D is the node density within the cluster, and B is the number of nodes that returned ACK confirmation packets.

[0080] Optionally, the dynamic adjustment formula for the k value (i.e., the preset quantity) is:

[0081]

[0082] Among them, D min The lowest density threshold, ranging from 0.005 to 0.05 nodes per square meter, represents a low-density area; the highest density threshold D... max The value ranges from 0.05 to 3 nodes per square meter, representing a high-density area; k min The maximum value of k; k min It is the minimum value of k.

[0083] Optionally, by calculating the Euclidean distance between the cluster head node and the surrounding sensor nodes, the k nodes with the smallest distance to the cluster head node are selected as neighbor nodes, thereby establishing the cluster.

[0084] Alternatively, using the density-aware KNN clustering algorithm (i.e., the DA-KNN algorithm), the cluster K-dis(i) representation is obtained:

[0085]

[0086] Among them, C i For a cluster with sensor node i as the cluster head node, the set of indices of the k neighbor nodes with the smallest distance to the cluster head node (i.e., the sensor nodes within the cluster) is defined as:

[0087]

[0088] Optionally, the above cluster generation strategy based on intra-cluster node density is particularly suitable for scenarios requiring high-density monitoring, such as factory production line monitoring and underground pipeline inspection. It is also applicable to other application scenarios that require efficient data transmission via wireless sensor networks. By adaptively adjusting the strategy, it can achieve a dual improvement in network performance and energy efficiency.

[0089] As an optional embodiment, transmitting the collected data of each sensor node to the cluster head node in a multi-hop transmission manner from multiple sensor nodes in the same cluster includes: in each hop transmission, traversing multiple sensor nodes in the cluster to obtain a data sending node; detecting the remaining energy of each sensor node in the cluster and the link quality between each sensor node and the data sending node; selecting a data receiving node from among the multiple sensor nodes in the cluster based on the remaining energy and link quality, wherein the remaining energy and link quality of the data receiving node meet preset transmission conditions; in each hop transmission, the data sending node sends a data acquisition packet to the data receiving node, and determines the data sending node and data receiving node for the next hop transmission, until the data receiving node is the cluster head node, wherein the data sending node for the next hop transmission is the data receiving node for the current hop transmission, and the data acquisition packet includes at least: the collected data of the sensor that is the data sending node, and the data acquisition packet received by the data sending node.

[0090] In the embodiments described above, within the same cluster, the multi-hop transmission mechanism can select the optimal data receiving node in each hop based on remaining energy and link quality. This effectively prevents low-energy nodes from becoming transmission bottlenecks and reduces data retransmissions caused by poor link quality, thereby improving the overall stability and speed of data transmission.

[0091] Optionally, the aforementioned multi-hop transmission mechanism is particularly suitable for environments with severe signal interference and uneven node distribution in wireless sensor networks, such as urban traffic monitoring and on-site monitoring of large-scale sports events. By optimizing the transmission path, it can achieve efficient and reliable data transmission. Furthermore, for any application that requires wireless sensor networks to transmit data, the multi-hop transmission mechanism can significantly improve network performance.

[0092] As an optional embodiment, selecting a data receiving node from multiple sensor nodes in a cluster based on remaining energy and link quality includes: determining the importance of each related factor influencing the selection of the data receiving node, wherein the related factors include at least remaining energy and link quality, and the importance is a numerical representation of the influence of the related factors on the selection of the data receiving node; determining the factor weight of each related factor based on the ratio of the importance of each related factor to the cumulative importance of multiple related factors, wherein the cumulative importance is the sum of the importance of multiple related factors; performing a weighted summation of multiple related factors based on the factor weight of each related factor to obtain a candidate score for each sensor node; and selecting the sensor node with the highest candidate score as the data receiving node.

[0093] The embodiments described above in this application, by assigning different weights to remaining energy and link quality, can dynamically adjust the selection criteria for data receiving nodes according to the actual network conditions. This method tends to select nodes with higher energy levels when link quality is generally good, in order to extend network lifetime; while in environments with poor link quality, it places greater emphasis on link quality to ensure the continuity and integrity of data transmission.

[0094] Optionally, the factor weight of each related factor is calculated as follows:

[0095]

[0096] Among them, f i It represents the importance of the i-th related factor, based on the real-time changes in the network's state. It is the cumulative result of the importance of multiple related factors, where i represents the related factor of remaining energy or link quality, and j represents the related factor of remaining energy and link quality.

[0097] Optionally, the above mechanism for selecting data receiving nodes in clusters is particularly suitable for wireless sensor networks operating in complex and ever-changing environments, such as field monitoring under extreme weather conditions and equipment monitoring in industrial production. Through intelligent node selection, efficient and stable data transmission can be achieved.

[0098] The embodiments described above are not limited to wireless sensor networks. For all systems that rely on inter-node communication, such as Internet of Things (IoT) device networks and UAV swarm communication, the overall performance and reliability of the system can be improved by optimizing the selection of data receiving nodes.

[0099] As an optional embodiment, in each hop of transmission, sending the acquisition data packet from the data sending node to the data receiving node includes: in each hop of transmission, detecting the signal strength between the data sending node and the data receiving node; determining the transmission power of the data sending node based on the signal strength, wherein the transmission power is negatively correlated with the signal strength; and sending the acquisition data packet from the data sending node to the data receiving node according to the transmission power.

[0100] The adaptive transmit power control mechanism described in the embodiments of this application is a powerful tool for optimizing energy management in wireless sensor networks. When the signal strength is strong, the transmit power is appropriately reduced to decrease energy consumption; conversely, when the signal strength is weak, the transmit power is increased to ensure successful data transmission. By dynamically adjusting the transmit power, energy can be saved to the maximum extent while ensuring data transmission quality.

[0101] Optionally, the aforementioned adaptive transmit power control mechanism is particularly suitable for deploying wireless sensor networks in environments with complex terrain and numerous obstacles, such as mountainous environmental monitoring and smart grid management in urban building complexes. By intelligently controlling the transmit power, it achieves efficient and energy-saving data transmission.

[0102] The embodiments described above are not limited to wireless sensor networks. For all systems that need to communicate efficiently under limited energy conditions, such as satellite communication networks and remote medical monitoring systems, adaptive transmit power control can improve the communication quality and energy utilization efficiency of the system.

[0103] As an optional embodiment, determining the transmit power of a data transmitting node based on signal strength includes: obtaining signal correlation parameters that affect signal strength, wherein the signal correlation parameters include at least the signal transmission distance between data transmitting nodes; and analyzing the signal transmission distance using the Friesian transmission equation to obtain the transmit power, wherein the transmit power is negatively correlated with the signal transmission distance.

[0104] The embodiments described above in this application, which determine the transmission power based on signal strength and transmission distance, represent an innovation in energy management for wireless sensor networks. By accurately calculating the transmission power, it is possible to meet data transmission requirements while avoiding excessive energy consumption.

[0105] Optionally, the Friesian transmission equation is used to calculate the transmit power, and the specific calculation formula is as follows:

[0106]

[0107] Among them, P Rx For received power, P TxG is the transmission power, λ is the signal wavelength, d is the distance between the transmitting end (i.e., the data transmitting node) and the receiving end (i.e., the data receiving node), and G is the signal wavelength. t , G r This represents the antenna gain at the transmitting and receiving ends.

[0108] Optionally, the above-described method for accurately calculating the sensor's transmission power is particularly suitable for wireless sensor networks operating in environments with severe signal attenuation, such as underwater communication and deep space exploration. By reasonably adjusting the transmission power, the adverse effects of environmental factors on signal transmission can be overcome, thereby achieving stable data transmission.

[0109] The embodiments described above are not limited to wireless sensor networks. For all systems that need to perform efficient and low-power communication in complex environments, such as secure communication and scientific research exploration, the communication capabilities and energy utilization efficiency of the system can be improved through intelligent adjustment of the transmission power.

[0110] As an optional embodiment, after transmitting the collected data of each sensor node to the cluster head node in a multi-hop transmission manner from multiple sensor nodes in the same cluster, the method further includes: detecting the data value of the collected data transmitted by each sensor node through the cluster head node, wherein the data value is determined at least based on data timeliness, data relevance, and data centralization. Data timeliness is an evaluation value determined based on the time difference between the time the cluster head node receives the collected data and the time the collected data is collected. Data relevance is an evaluation value determined based on the similarity between the collected data and pre-set demand data. Data centralization is an evaluation value determined at least based on the amount of data in the collected data. Data timeliness is negatively correlated with time difference, data relevance is positively correlated with similarity, data centralization is positively correlated with data amount, and data value is positively correlated with data timeliness, data relevance, and data centralization. Each piece of collected data is transmitted to the data center in descending order of data value.

[0111] The data value prioritization transmission strategy described in the embodiments of this application is an important means to improve the data processing efficiency and decision support capabilities of wireless sensor networks. By comprehensively evaluating the timeliness, relevance, and centralization of data, high-value data can be prioritized for transmission, thereby accelerating the processing speed of critical information and improving the network's responsiveness and decision-making efficiency. In this way, wireless sensor networks can allocate resources more intelligently, not only improving the continuity and reliability of data transmission but also extending the overall network operating cycle, providing strong support for real-time data analysis and decision-making.

[0112] Optionally, the formulas for calculating data timeliness and data relevance (RTC) are as follows:

[0113] RTCk,i (t)=μ k TL(tt k,i )(1-μ k C k ;

[0114]

[0115] Where, μ k It is a constant between 0 and 1, where t represents the current time; C k X represents the correlation between the data packet k perceived by node i and the required data; X represents the data information of the required data; and S represents the data information perceived by the node.

[0116] Optionally, the formula for calculating the degree of data centralization (CLD) is as follows:

[0117]

[0118] Among them, TI ic The time required for data packet P to travel from sensor node i to cluster head node is represented by Num; Num represents the number of data packets P that are sensed; t initial It is the time when the data packet is first transmitted to the cluster head node; t now It is the time point of the latest data packet transmission by the cluster head node.

[0119] Optionally, the value of data is determined at least based on its timeliness, relevance, and degree of centralization, as shown in the following formula:

[0120] VoI = α1·RTC + α2·CLD;

[0121] Where α1 and α2 are weighting factors, 0≤α1≤1, 0≤α2≤1, and α1+α2=1, RTC represents data relevance and timeliness, and CLD represents the degree of data packet centralization.

[0122] The above embodiments of this application employ a transmission strategy based on the order of data value, which is particularly suitable for scenarios requiring real-time data analysis and decision support, such as disaster early warning and intelligent traffic management. By prioritizing the transmission of highly timely and relevant data, it is possible to respond quickly to emergencies and make timely and effective decisions.

[0123] The embodiments described above are not limited to wireless sensor networks. They can be applied to all systems involving big data processing and real-time decision-making, such as financial transactions and medical diagnosis, to achieve efficient information utilization and rapid decision-making response through data value ranking and transmission.

[0124] This invention also provides a preferred embodiment, which offers a network clustering data transmission method based on adaptive weights and data value. This method constructs a cellular wireless sensor network architecture, uses the ELA-LEACH algorithm for cluster head node election, and employs a density-aware KNN algorithm for network clustering, thereby improving network coverage. Within each cluster, factors such as distance, remaining energy, and inter-node link quality are comprehensively considered. Based on a dynamic weight allocation mechanism, the importance of each factor is dynamically adjusted according to the real-time network status to select the optimal next-hop node and adaptively select appropriate power for transmission, reducing the energy consumption of the wireless sensor network. Furthermore, based on cluster head transmission scheduling using data value assessment, a data value is defined, consisting of data relevance and timeliness (RTC) and data packet centralization (CLD). Cluster head nodes determine the transmission order to the data center according to the data value of the cluster, improving the VOI of data transmission. This achieves the technical effects of improving data transmission VOI, reducing the energy consumption of the wireless sensor network, and increasing network coverage.

[0125] Figure 2 This is a schematic diagram of a network clustering data transmission method based on adaptive weights and data value according to an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0126] Step S201: Receive signals, construct a cellular wireless sensor network architecture, use the ELA-LEACH algorithm to elect cluster head nodes, and use the density-aware KNN clustering algorithm (i.e., the DA-KNN algorithm) to cluster the network.

[0127] Step S202: Within the cluster, considering factors such as distance, remaining energy, and link quality between nodes, and based on a dynamic weight allocation mechanism, dynamically adjusts the importance of each factor according to the real-time network status, selects the optimal next-hop node, and adaptively selects the transmit power.

[0128] Step S203: Cluster head transmission scheduling based on data value assessment defines data value as composed of data relevance and timeliness (RTC) and data packet centralization degree (CLD). Cluster head nodes determine the transmission order to the data center according to the data value of the cluster.

[0129] Specifically, step S201 above includes the following steps:

[0130] Step S2011: Construct a cellular wireless sensor network architecture.

[0131] Figure 3 This is a schematic diagram of a cellular wireless sensor network architecture according to an embodiment of the present invention, such as... Figure 3 As shown, it contains M sensor nodes M = {m1, ..., m}m} and N cluster head nodes N = {N1, ..., N n The location of each node is known; a cellular topology based on region segmentation is used to achieve efficient management of full coverage by dividing the monitoring area into multiple hexagonal units; environmental data is collected by other nodes in each unit and aggregated to the cluster head node, which then transmits the integrated information to the base station.

[0132] Step S2012: Use the ELA-LEACH algorithm to elect cluster head nodes.

[0133] Optionally, the ELA-LEACH algorithm comprehensively considers the remaining energy and distance of nodes to elect cluster head nodes. This ELA-LEACH algorithm can calculate the clustering evaluation index T(n) for each sensor node based on the remaining energy and distance balance factor, and select the sensor node with the smallest clustering evaluation index as the cluster head node. The specific calculation formula is as follows:

[0134]

[0135] Where n is the total number of nodes in the cellular wireless sensor network, v is the ratio of the number of nodes that have acted as cluster heads to the total number of nodes, and q represents the number of rounds already performed. G represents the number of cluster head nodes that have been selected in the current round; G is the set of cluster head nodes that were not selected in this round (i.e., the set of candidate nodes); E current E represents the current remaining energy of a node. total This represents the initial total energy of the node (i.e., the initial energy); F represents the distance balance factor. and The weights represent the node's remaining energy and distance balance factor. Higher weights have a greater impact on the distance balance factor. (Setting...) and

[0136] Alternatively, the distance balance factor F can be calculated as follows:

[0137]

[0138] Among them, setting (X) r ,Y r (X) represents the center coordinates of the wireless sensor network's center location. i ,Y i ) represents the position coordinates of the current node i in the wireless sensor network (i.e., the sensor node coordinates), (X) j ,Y j) represents the coordinates of all candidate nodes in the wireless sensor network; the closer the cluster head node is to the central node, the more balanced the energy consumption of the entire network and the longer the network's lifespan; r represents the round of iterative clustering.

[0139] Step S2013: Use the density-aware KNN clustering algorithm to perform network clustering.

[0140] Optionally, after selecting the cluster head node, the density-aware KNN clustering algorithm is used to select the remaining k neighbor nodes to complete the cluster establishment.

[0141] Optionally, the density-aware KNN clustering algorithm dynamically adjusts the k value based on the calculated node density within the cluster. In regions with high node density, a larger k value is selected to enhance the correlation between neighboring nodes; in regions with low node density, a smaller k value is used to ensure the accuracy and effectiveness of neighbor selection.

[0142] Optionally, the node density within a cluster is calculated based on the cluster head node. During this process, the cluster head node broadcasts its unique identifier with a broadcast radius of R. Nodes within the broadcast radius, upon receiving the identifier, return an acknowledgment packet (ACK) to complete the interaction. The formula for calculating the node density within a cluster is as follows:

[0143]

[0144] Where D is the node density within the cluster, and B is the number of nodes that returned ACK confirmation packets.

[0145] Optionally, the dynamic adjustment formula for the value of k is:

[0146]

[0147] Among them, D min The lowest density threshold, ranging from 0.005 to 0.05 nodes per square meter, represents a low-density area; the highest density threshold D... max The value ranges from 0.05 to 3 nodes per square meter, representing a high-density area; k min The maximum value of k; k min It is the minimum value of k.

[0148] Optionally, by calculating the Euclidean distance between the cluster head node and the surrounding sensor nodes, the k nodes with the smallest distance to the cluster head node are selected as neighbor nodes, thereby establishing the cluster.

[0149] Alternatively, using the density-aware KNN clustering algorithm (i.e., the DA-KNN algorithm), the cluster K-dis(i) representation is obtained:

[0150]

[0151] Among them, C i For a cluster with sensor node i as the cluster head node, the set of indices of the k neighbor nodes with the smallest distance to the cluster head node (i.e., the sensor nodes within the cluster) is defined as:

[0152]

[0153] Specifically, step S202 above includes the following steps:

[0154] Step S2011, next-hop selection mechanism based on dynamic weight allocation.

[0155] Optionally, within a cluster, other nodes transmit the collected data to the cluster head node via a multi-hop method. When selecting the next hop node, the remaining energy of the node and the link quality are considered comprehensively, and the weights of each factor are dynamically adjusted according to the real-time status of the network to cope with different network environments.

[0156] Specifically, when there are significant differences in the energy distribution among nodes within a cluster, the weight of remaining energy will be increased, and nodes with sufficient remaining energy will be prioritized for data transmission to extend the network lifetime and avoid communication interruptions caused by premature energy depletion; or when network congestion occurs within a cluster, the weight of link quality will be increased, and the network load will be reduced by selecting paths with better signal quality and stable transmission, thereby improving the reliability and efficiency of data transmission.

[0157] The embodiments described above in this application, through a dynamic weight allocation mechanism, can optimize transmission performance under different network conditions and ensure efficient network operation in various scenarios.

[0158] Optionally, the specific implementation formula for the dynamic weight allocation mechanism is as follows:

[0159]

[0160] Among them, f i It represents the importance of the i-th related factor, based on the real-time changes in the network's state. It is the cumulative result of the importance of multiple related factors, where i represents the related factor of remaining energy or link quality, and j represents the related factor of remaining energy and link quality.

[0161] Step S2012, Adaptive transmit power control.

[0162] Optionally, during data transmission, different transmission powers can be used to transmit signals to the next hop node based on the different signal strengths between nodes in the cluster, in order to save network energy and extend network lifespan.

[0163] For example, if a high signal strength is detected between the current node and the next-hop node transmitting the signal, it indicates that the communication link between the nodes is of good quality. The current node can complete data transmission with lower transmission power to reduce energy consumption and improve transmission efficiency. Conversely, if a weak signal strength is detected between the current node and the next-hop node transmitting the signal, the current node will increase its transmission power to ensure data integrity and accuracy.

[0164] Optionally, the theoretical model of the Friis transmission equation is used to calculate the transmit power, and the specific calculation formula is as follows:

[0165]

[0166] Among them, P Rx For received power, P Tx Let G be the transmit power, λ be the signal wavelength, d be the distance between the transmitter and receiver, and G be the signal strength. t G r This represents the antenna gain at the transmitting and receiving ends.

[0167] Table 1 is a schematic table of power consumption corresponding to a transmission level according to an embodiment of the present invention. As shown in Table 1, five different levels of transmission power are set, and the corresponding current consumption, power, and transmission distance information are given; the minimum transmission distance is set to 10m, and the maximum transmission power is set to 165mW.

[0168] Table 1

[0169]

[0170] Optionally, in step S203 above, the formula for calculating data value is determined based on data relevance and timeliness (RTC) and data packet centralization (CLD), as shown in the following formula:

[0171] VoI = α1·RTC + α2·CLD;

[0172] Where α1 and α2 are weighting factors, 0≤α1≤1, 0≤α2≤1, and α1+α2=1, RTC represents data relevance and timeliness, and CLD represents the degree of data packet centralization.

[0173] Optionally, in this application, it is believed that data relevance and timeliness have a greater impact on data collection, so α1 is set to 0.6 and α2 to 0.4.

[0174] Optionally, RTC represents the relevance and timeliness between the data transmitted by the node and the required data, as shown in the following formula:

[0175] RTC k,i(t)=μ k TL(tt k,i )(1-μ k C k ;

[0176]

[0177] Where, μ k It is a constant between 0 and 1, where t represents the current time; C k X represents the correlation between the data packet k perceived by node i and the required data; X represents the data information of the required data; and S represents the data information perceived by the node.

[0178] Optionally, CLD represents the degree of centralization of data packets in the cluster. That is, if the data generated by a node is more concentrated and has a larger amount of information, it is considered to have a higher degree of centralization. The specific formula is as follows:

[0179]

[0180] Among them, TI ic The time required for data packet P to travel from sensor node i to cluster head node is represented by Num; Num represents the number of data packets P that are sensed; t initial It is the time when the data packet is first transmitted to the cluster head node; t now It is the time point of the latest data packet transmission by the cluster head node.

[0181] Table 2 is a schematic table of simulation results under different network scenarios according to an embodiment of the present invention. As shown in Table 2, simulations were performed under different network scenarios, and the values ​​corresponding to each simulation parameter were obtained by averaging the results of 100 experiments.

[0182] Table 2

[0183]

[0184] Figure 4 This is a schematic diagram comparing simulation results of how the number of remaining surviving nodes in a sensor network changes with the number of rounds of network operation, according to an embodiment of the present invention. Figure 4As shown, the simulation results of the network clustering and data transmission method based on adaptive weights and data value, and the traditional LEACH algorithm, are compared to those of the traditional algorithm, showing the change in the number of surviving nodes in a sensor network as the number of rounds increases. The simulation results show that the network using the LEACH algorithm experiences complete node death after approximately 480 rounds, while the proposed ELA-LEACH algorithm still has some nodes surviving after 600 rounds. This demonstrates that the ELA-LEACH algorithm effectively reduces the energy consumption of wireless sensor networks and extends the network's lifetime.

[0185] Figure 5 This is a schematic diagram illustrating the comparison of simulation results regarding the change in total VOI of a sensor network according to an embodiment of the present invention, as shown below. Figure 5 As shown, simulation results comparing the network clustering data transmission method based on adaptive weights and data value with the traditional LEACH algorithm in terms of the change in total VOI in a sensor network are presented. The simulation results show that the VOI generated by data collection gradually increases over time. Because this application emphasizes timeliness and relevance in the data collection process, the VOI generated by the ELA-LEACH algorithm is significantly higher than that of the traditional LEACH algorithm. After 480 rounds, the data transmission method based on the LEACH algorithm leads to the complete death of network nodes, and the VOI no longer changes. Therefore, the technical solution provided in this application can effectively improve the VOI of data in the network, providing more efficient support for real-time decision-making and task response.

[0186] According to an embodiment of the present invention, a data transmission device embodiment is also provided. It should be noted that the data transmission device can be used to execute the data transmission method in the embodiments of the present invention, and the data transmission method in the embodiments of the present invention can be executed in the data transmission device.

[0187] Figure 6 This is a schematic diagram of a data transmission device according to an embodiment of the present invention, such as... Figure 6As shown, the device may include: an acquisition module 62, used to acquire a set of sensor nodes in a wireless sensor network, wherein each sensor node in the sensor node set corresponds to a sensor in the wireless sensor network; a clustering module 64, used to perform multiple iterative clustering on multiple sensor nodes in the sensor node set to obtain multiple clusters, wherein, in each iteration of clustering, based on the energy balance factor and distance balance factor of each sensor node in the candidate node set, a clustering evaluation index for each sensor node is determined, and the sensor node with the smallest clustering evaluation index is selected as the cluster head node for this clustering, the candidate node set includes: sensor nodes that were not clustered in the sensor set before this clustering, the energy balance factor is determined based on the remaining energy of the sensor node, the distance balance factor is determined based at least on the distance between sensor nodes, the clustering evaluation index is positively correlated with the remaining energy, and the clustering evaluation index is positively correlated with the distance balance factor; and a transmission module 66, used to transmit the collected data of each sensor node to the cluster head node in a multi-hop transmission manner from multiple sensor nodes in the same cluster.

[0188] It should be noted that the acquisition module 62 in this embodiment can be used to execute step S102 in this application embodiment, the clustering module 64 in this embodiment can be used to execute step S104 in this application embodiment, and the transmission module 66 in this embodiment can be used to execute step S106 in this application embodiment. The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments.

[0189] In this embodiment of the invention, during the clustering process, by comprehensively considering the remaining energy of sensor nodes and the distance between sensor nodes, the clustering evaluation index is dynamically adjusted to ensure that sensor nodes with sufficient energy are preferentially selected as cluster head nodes, thereby reducing energy consumption and extending network lifetime. The multi-hop transmission mode further optimizes the data transmission path, reduces the energy requirement of a single transmission, and enhances the robustness and coverage of the network. Thus, it achieves the technical effect of reducing sensor energy consumption while ensuring data transmission quality and network coverage, thereby solving the technical problem that traditional wireless sensor networks cannot reduce energy consumption while ensuring data transmission quality and network coverage.

[0190] As an optional embodiment, the apparatus for executing each iteration of the clustering process in the clustering module includes at least: a first determining unit, configured to determine a distance equalization factor corresponding to each sensor node based on the sensor position of each sensor node in the candidate node set, wherein the sensor position is used at least to determine a first distance between each sensor node and the center position of the wireless sensor network, and a second distance between each sensor node and other sensors in the candidate node set, the distance equalization factor being positively correlated with the first distance and negatively correlated with the second distance; and a second determining unit, configured to determine an energy equalization factor and a weighting factor corresponding to each sensor node based on the remaining energy of each sensor node in the candidate node set, wherein the energy... The equilibrium factor is positively correlated with the remaining energy, and the weight factor is determined based on the difference between the remaining energy and the preset energy threshold. It is used to represent the degree of influence of the distance equilibrium factor and the remaining energy on the cluster evaluation index, respectively. The third determining unit is used to determine the cluster evaluation index of each sensor node based on the energy equilibrium factor and the distance equilibrium factor, as well as the weight factors corresponding to the energy equilibrium factor and the distance equilibrium factor, respectively. The fourth determining unit is used to determine the sensor nodes whose node relationship with the cluster head node meets the clustering conditions in the candidate node set, and obtain the clusters obtained by this clustering. The clustering conditions are used to indicate at least a preset number of sensor nodes whose Euclidean distance with the cluster head node meets the preset distance requirements.

[0191] As an optional embodiment, the first determining unit includes: a first traversal subunit, used to traverse multiple sensor nodes in the candidate node set to obtain candidate sensor nodes traversed each time, wherein the sensor position of the candidate sensor node is a candidate position; a first determining subunit, used to determine a first distance of the candidate sensor node based on the candidate node position and the network center position of the wireless sensor network center; a second determining subunit, used to determine the node distance between the candidate sensor node and each sensor node in the candidate node set based on the candidate node position and the sensor positions of other sensor nodes in the candidate node set, and to determine a second distance based on the node distance, wherein the second distance is the average value of the node distances; and a third determining unit, used to determine a distance equalization factor of the candidate sensor node based on the ratio of the first distance and the second distance of the candidate sensor node.

[0192] As an optional embodiment, the second determining unit includes: a second traversal subunit, used to traverse multiple sensor nodes in the candidate node set, obtain the candidate sensor nodes traversed each time, and determine the remaining energy of the candidate sensor nodes; a fourth determining subunit, used to determine the energy balance factor of the candidate sensor nodes based on the ratio of the remaining energy of the candidate sensor nodes to the initial energy; and a first allocation subunit, used to allocate corresponding weight factors to the energy balance factor and the distance balance factor based on the difference between the remaining energy of the candidate sensor nodes and a preset energy threshold, wherein the weight factors include: the energy weight corresponding to the energy balance factor and the distance weight corresponding to the distance balance factor, the energy weight is negatively correlated with the remaining energy, and the sum of the energy weight and the distance weight is 1.

[0193] As an optional embodiment, the first allocation subunit includes: a fifth determining subunit, configured to determine whether the remaining energy is greater than the preset energy threshold based on the difference between the remaining energy of the candidate sensor node and the preset energy threshold; a second allocation subunit, configured to allocate a first weight to the energy balance factor and a second weight to the distance balance factor when the remaining energy is greater than the preset energy threshold, wherein the sum of the first weight and the second weight is 1; and a third allocation subunit, configured to allocate a third weight to the energy balance factor and a fourth weight to the distance balance factor when the remaining energy is not greater than the preset energy threshold, wherein the sum of the third weight and the fourth weight is 1, and the third weight is greater than the first weight.

[0194] As an optional embodiment, the fourth determining unit includes: a statistics subunit, used to count the number of sensor nodes in the candidate node set that respond to the broadcast content sent by the cluster head node, wherein the sensor corresponding to the cluster head node sends the broadcast content according to a preset distance radius, and the sensors within the preset distance radius respond to the broadcast content and return an acknowledgment packet to the sensor acting as the cluster head node, and the number of nodes is determined according to the number of acknowledgment packets; a sixth determining subunit, used to determine the node density within the cluster based on the number of nodes and the coverage area of ​​the broadcast content sent by the cluster head node, wherein the coverage area is determined according to the preset distance radius; a seventh determining subunit, used to determine a preset number based on the node density within the cluster, wherein the preset number represents the number of sensor nodes in the same cluster as the cluster head node, and the preset number is positively correlated with the node density within the cluster; and a selection subunit, used to arrange the sensor nodes in the candidate node set in ascending order of their Euclidean distance from the cluster head node to obtain a candidate node queue, and select a preset number of sensor nodes from the candidate node queue in order from the head to the tail of the queue to obtain the cluster corresponding to the cluster head node.

[0195] As an optional embodiment, the transmission module includes: a traversal unit, used to traverse multiple sensor nodes in the cluster to obtain a data sending node in each hop transmission; a detection unit, used to detect the remaining energy of each sensor node in the cluster and the link quality between each sensor node and the data sending node; a selection unit, used to select a data receiving node from the multiple sensor nodes in the cluster based on the remaining energy and link quality, wherein the remaining energy and link quality of the data receiving node meet preset transmission conditions; and a sending unit, used to send a data acquisition packet from the data sending node to the data receiving node in each hop transmission, and determine the data sending node and data receiving node for the next hop transmission, until the data receiving node is the cluster head node, wherein the data sending node for the next hop transmission is the data receiving node for the current hop transmission, and the data acquisition packet includes at least: the acquisition data of the sensor that is the data sending node, and the data acquisition packet received by the data sending node.

[0196] As an optional embodiment, the selection unit includes: an eighth determining subunit, used to determine the importance of each associated factor affecting the selection of the data receiving node, wherein the associated factors include at least: remaining energy and link quality, and the importance is a numerical representation of the influence of the associated factors on the selection of the data receiving node; a ninth determining subunit, used to determine the factor weight of each associated factor based on the ratio of the importance of each associated factor to the cumulative importance of multiple associated factors, wherein the cumulative importance is the sum of the importance of multiple associated factors; a calculation subunit, used to perform a weighted summation of multiple associated factors based on the factor weight of each associated factor to obtain a candidate score for each sensor node; and a selection subunit, used to select the sensor node with the highest candidate score as the data receiving node.

[0197] As an optional embodiment, the transmitting unit includes: a detection subunit, used to detect the signal strength between the data transmitting node and the data receiving node in each hop of transmission; a tenth determination subunit, used to determine the transmission power of the data transmitting node based on the signal strength, wherein the transmission power is negatively correlated with the signal strength; and a transmitting subunit, used to send the acquisition data packet from the data transmitting node to the data receiving node according to the transmission power.

[0198] As an optional embodiment, the tenth determining subunit includes: an acquisition subunit, used to acquire signal correlation parameters that affect signal strength, wherein the signal correlation parameters include at least: the signal transmission distance between data transmitting nodes; and an analysis subunit, used to analyze the signal transmission distance using the Friesian transmission equation to obtain the transmission power, wherein the transmission power is negatively correlated with the signal transmission distance.

[0199] As an optional embodiment, the device further includes: a detection submodule, used to detect the data value of the collected data transmitted by each sensor node through the cluster head node after multiple sensor nodes in the same cluster transmit the collected data of each sensor node to the cluster head node in a multi-hop transmission manner. The data value is determined at least based on data timeliness, data relevance, and data centralization. Data timeliness is an evaluation value determined based on the time difference between the time the cluster head node receives the collected data and the time the collected data is collected. Data relevance is an evaluation value determined based on the similarity between the collected data and pre-set demand data. Data centralization is an evaluation value determined at least based on the amount of data in the collected data. Data timeliness is negatively correlated with the time difference, data relevance is positively correlated with similarity, data centralization is positively correlated with the amount of data, and data value is positively correlated with data timeliness, data relevance, and data centralization. A transmission submodule is used to transmit each collected data point sequentially to the data center in descending order of data value.

[0200] Embodiments of the present invention can provide an electronic device, which can be a computer terminal, and the computer terminal can be any one of a group of computer terminal devices. Optionally, in this embodiment, the computer terminal can also be replaced by a mobile terminal or other terminal device.

[0201] Optionally, in this embodiment, the computer terminal may be located in at least one of a plurality of network devices in a computer network.

[0202] In this embodiment, the computer terminal can execute the program code for the following steps in the data transmission method: obtaining a set of sensor nodes in a wireless sensor network, wherein each sensor node in the set corresponds to a sensor in the wireless sensor network; performing multiple iterative clustering on multiple sensor nodes in the set to obtain multiple clusters, wherein, in each iteration of clustering, based on the energy balance factor and distance balance factor of each sensor node in the candidate node set, the clustering evaluation index of each sensor node is determined, and the sensor node with the smallest clustering evaluation index is selected as the cluster head node for this clustering, wherein the candidate node set includes: sensor nodes that were not clustered in the sensor set before this clustering, the energy balance factor is determined based on the remaining energy of the sensor node, the distance balance factor is determined based at least on the distance between sensor nodes, the clustering evaluation index is positively correlated with the remaining energy, and the clustering evaluation index is positively correlated with the distance balance factor; transmitting the collected data of each sensor node to the cluster head node in a multi-hop transmission manner from multiple sensor nodes in the same cluster.

[0203] Figure 7 This is a structural block diagram of a computer terminal according to an embodiment of the present invention, such as... Figure 7 As shown, the computer terminal 70 may include one or more (only one is shown in the figure) processors 72 and memory 74.

[0204] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the data transmission method and apparatus in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned data transmission method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal 70 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0205] The processor can access information and applications stored in memory via a transmission device to perform the following steps: acquiring a set of sensor nodes in a wireless sensor network, where each sensor node in the set corresponds to a sensor in the wireless sensor network; performing multiple iterative clustering operations on the multiple sensor nodes in the set to obtain multiple clusters, wherein in each iteration of clustering, a clustering evaluation index for each sensor node is determined based on the energy balance factor and distance balance factor of each sensor node in the candidate node set, and the sensor node with the smallest clustering evaluation index is selected as the cluster head node for this clustering; the candidate node set includes: sensor nodes that were not clustered in the sensor set before this clustering, the energy balance factor is determined based on the remaining energy of the sensor nodes, the distance balance factor is determined based at least on the distance between sensor nodes, the clustering evaluation index is positively correlated with the remaining energy, and the clustering evaluation index is positively correlated with the distance balance factor; transmitting the collected data of each sensor node to the cluster head node using a multi-hop transmission method from multiple sensor nodes in the same cluster.

[0206] Optionally, the processor may also execute program code for the following steps: determining a distance equalization factor for each sensor node based on the sensor location of each sensor node in the candidate node set, wherein the sensor location is used at least to determine a first distance between each sensor node and the center location of the wireless sensor network, and a second distance between each sensor node and other sensors in the candidate node set, the distance equalization factor being positively correlated with the first distance and negatively correlated with the second distance; determining an energy equalization factor and a weighting factor for each sensor node based on the remaining energy of each sensor node in the candidate node set, wherein the energy equalization factor is positively correlated with the remaining energy, and the weighting factor is based on the remaining energy... The difference between the energy level and the preset energy threshold is determined to represent the degree of influence of the distance balance factor and the remaining energy on the cluster evaluation index. Based on the energy balance factor and the distance balance factor, as well as the weight factors corresponding to the energy balance factor and the distance balance factor, the cluster evaluation index of each sensor node is determined. The sensor node with the smallest cluster evaluation index in the candidate node set is determined as the cluster head node of this clustering. The sensor nodes whose node relationship with the cluster head node meets the clustering conditions are determined in the candidate node set to obtain the clusters obtained in this clustering. The clustering conditions are used to indicate at least a preset number of sensor nodes whose Euclidean distance with the cluster head node meets the preset distance requirements.

[0207] Optionally, the processor may also execute program code with the following steps: traversing multiple sensor nodes in the candidate node set to obtain candidate sensor nodes traversed each time, wherein the sensor position of the candidate sensor node is a candidate position; determining a first distance between the candidate sensor node and the network center position of the wireless sensor network based on the candidate node position; determining the node distance between the candidate sensor node and each sensor node in the candidate node set based on the candidate node position and the sensor positions of other sensor nodes in the candidate node set, and determining a second distance based on the node distance, wherein the second distance is the average value of the node distances; and determining a distance equalization factor for the candidate sensor node based on the ratio of the first distance and the second distance.

[0208] Optionally, the processor may also execute program code with the following steps: traversing multiple sensor nodes in the candidate node set, obtaining the candidate sensor nodes traversed each time, and determining the remaining energy of the candidate sensor nodes; determining the energy balance factor of the candidate sensor nodes based on the ratio of the remaining energy of the candidate sensor nodes to the initial energy; assigning corresponding weight factors to the energy balance factor and the distance balance factor based on the difference between the remaining energy of the candidate sensor nodes and a preset energy threshold, wherein the weight factors include: the energy weight corresponding to the energy balance factor and the distance weight corresponding to the distance balance factor, the energy weight being negatively correlated with the remaining energy, and the sum of the energy weight and the distance weight being 1.

[0209] Optionally, the processor may also execute program code for the following steps: determining whether the remaining energy is greater than the preset energy threshold based on the difference between the remaining energy of the candidate sensor node and the preset energy threshold; if the remaining energy is greater than the preset energy threshold, assigning a first weight to the energy balancing factor and a second weight to the distance balancing factor, wherein the sum of the first weight and the second weight is 1; if the remaining energy is not greater than the preset energy threshold, assigning a third weight to the energy balancing factor and a fourth weight to the distance balancing factor, wherein the sum of the third weight and the fourth weight is 1, and the third weight is greater than the first weight.

[0210] Optionally, the processor may also execute program code for the following steps: counting the number of sensor nodes in the candidate node set that respond to the broadcast content sent by the cluster head node, wherein the sensor corresponding to the cluster head node sends the broadcast content according to a preset distance radius, and the sensors within the preset distance radius respond to the broadcast content and return an acknowledgment packet to the sensor acting as the cluster head node, and the number of nodes is determined based on the number of acknowledgment packets; determining the node density within the cluster based on the number of nodes and the coverage area of ​​the broadcast content sent by the cluster head node, wherein the coverage area is determined based on the preset distance radius; determining a preset number based on the node density within the cluster, wherein the preset number represents the number of sensor nodes in the same cluster as the cluster head node, and the preset number is positively correlated with the node density within the cluster; arranging the sensor nodes in the candidate node set in ascending order of their Euclidean distance from the cluster head node to obtain a candidate node queue, and selecting a preset number of sensor nodes from the candidate node queue in order from the head to the tail of the queue to obtain the cluster corresponding to the cluster head node.

[0211] Optionally, the processor may also execute program code for the following steps: in each hop transmission case, traverse multiple sensor nodes in the cluster to obtain the data sending node; detect the remaining energy of each sensor node in the cluster and the link quality between each sensor node and the data sending node; select a data receiving node from the multiple sensor nodes in the cluster based on the remaining energy and link quality, wherein the remaining energy and link quality of the data receiving node meet preset transmission conditions; in each hop transmission case, the data sending node sends a data acquisition packet to the data receiving node, and determines the data sending node and data receiving node for the next hop transmission, until the data receiving node is the cluster head node, wherein the data sending node for the next hop transmission is the data receiving node for this hop transmission, and the data acquisition packet includes at least: the acquisition data of the sensor that is the data sending node, and the data acquisition packet received by the data sending node.

[0212] Optionally, the processor may also execute program code for the following steps: determining the importance of each related factor affecting the selection of a data receiving node, wherein the related factors include at least: remaining energy and link quality, and the importance is a numerical representation of the influence of the related factors on the selection of the data receiving node; determining the factor weight of each related factor based on the ratio of the importance of each related factor to the cumulative importance of multiple related factors, wherein the cumulative importance is the sum of the importance of multiple related factors; performing a weighted summation of multiple related factors based on the factor weight of each related factor to obtain a candidate score for each sensor node; and selecting the sensor node with the highest candidate score as the data receiving node.

[0213] Optionally, the processor may also execute program code that performs the following steps: in each hop of transmission, detect the signal strength between the data sending node and the data receiving node; determine the transmission power of the data sending node based on the signal strength, wherein the transmission power is negatively correlated with the signal strength; and have the data sending node send the acquisition data packet to the data receiving node according to the transmission power.

[0214] Optionally, the processor may also execute program code for the following steps: obtaining signal correlation parameters that affect signal strength, wherein the signal correlation parameters include at least: the signal transmission distance between data transmitting nodes; analyzing the signal transmission distance using the Friesian transmission equation to obtain the transmission power, wherein the transmission power is negatively correlated with the signal transmission distance.

[0215] Optionally, the processor may also execute program code for the following steps: detecting the data value of the collected data transmitted by each sensor node through the cluster head node, wherein the data value is determined at least based on data timeliness, data relevance, and data centralization; data timeliness is an evaluation value determined based on the time difference between the time the cluster head node receives the collected data and the time the collected data is collected; data relevance is an evaluation value determined based on the similarity between the collected data and pre-set demand data; and data centralization is an evaluation value determined at least based on the amount of data in the collected data. Data timeliness is negatively correlated with time difference, data relevance is positively correlated with similarity, data centralization is positively correlated with data amount, and data value is positively correlated with data timeliness, data relevance, and data centralization; and transmitting each piece of collected data to the data center in descending order of data value.

[0216] Those skilled in the art will understand that Figure 7 The structure shown is for illustrative purposes only. The computer terminal can also be a smartphone (such as an Android phone, an iOS phone, etc.), a tablet computer, a mobile internet device (MID), a PAD, and other terminal devices. Figure 7 This does not limit the structure of the aforementioned electronic device. For example, the computer terminal 70 may also include components that are more advanced than those described above. Figure 7 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 7 The different configurations shown.

[0217] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a computer program instructing the hardware related to the terminal device. The computer program can be stored in a non-volatile medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0218] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the data transmission method provided in the above embodiments.

[0219] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0220] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining a set of sensor nodes in a wireless sensor network, wherein each sensor node in the sensor node set corresponds to a sensor in the wireless sensor network; performing multiple iterative clustering on multiple sensor nodes in the sensor node set to obtain multiple clusters, wherein, in each iteration of clustering, based on the energy balance factor and distance balance factor of each sensor node in the candidate node set, the clustering evaluation index of each sensor node is determined, and the sensor node with the smallest clustering evaluation index is selected as the cluster head node for this clustering, the candidate node set includes: sensor nodes that were not clustered in the sensor set before this clustering, the energy balance factor is determined based on the remaining energy of the sensor node, the distance balance factor is determined based at least on the distance between sensor nodes, the clustering evaluation index is positively correlated with the remaining energy, and the clustering evaluation index is positively correlated with the distance balance factor; transmitting the collected data of each sensor node to the cluster head node in a multi-hop transmission manner from multiple sensor nodes in the same cluster.

[0221] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining a distance equalization factor for each sensor node based on the sensor location of each sensor node in the candidate node set, wherein the sensor location is used at least to determine a first distance between each sensor node and the center location of the wireless sensor network, and a second distance between each sensor node and other sensors in the candidate node set, the distance equalization factor being positively correlated with the first distance and negatively correlated with the second distance; determining an energy equalization factor and a weighting factor for each sensor node based on the remaining energy of each sensor node in the candidate node set, wherein the energy equalization factor is positively correlated with the remaining energy, and the weighting factor is positively correlated with the remaining energy. The weight factor is determined based on the difference between the remaining energy and the preset energy threshold, and is used to represent the degree of influence of the distance balance factor and the remaining energy on the cluster evaluation index, respectively. The cluster evaluation index of each sensor node is determined according to the energy balance factor and the distance balance factor, as well as the weight factors corresponding to the energy balance factor and the distance balance factor, respectively. The sensor node with the smallest cluster evaluation index in the candidate node set is determined as the cluster head node of this clustering. The sensor nodes whose node relationship with the cluster head node meets the clustering conditions are determined in the candidate node set, and the clusters obtained by this clustering are obtained. The clustering conditions are used to indicate at least a preset number of sensor nodes whose Euclidean distance with the cluster head node meets the preset distance requirements.

[0222] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: traversing multiple sensor nodes in the candidate node set to obtain candidate sensor nodes traversed each time, wherein the sensor position of the candidate sensor node is a candidate position; determining a first distance of the candidate sensor node based on the candidate node position and the network center position of the wireless sensor network center; determining the node distance between the candidate sensor node and each sensor node in the candidate node set based on the candidate node position and the sensor positions of other sensor nodes in the candidate node set, and determining a second distance based on the node distance, wherein the second distance is the average value of the node distances; and determining a distance equalization factor of the candidate sensor node based on the ratio of the first distance and the second distance of the candidate sensor node.

[0223] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: traversing multiple sensor nodes in the candidate node set, obtaining the candidate sensor nodes traversed each time, and determining the remaining energy of the candidate sensor nodes; determining the energy balance factor of the candidate sensor nodes based on the ratio of the remaining energy of the candidate sensor nodes to the initial energy; assigning corresponding weight factors to the energy balance factor and the distance balance factor based on the difference between the remaining energy of the candidate sensor nodes and a preset energy threshold, wherein the weight factors include: the energy weight corresponding to the energy balance factor and the distance weight corresponding to the distance balance factor, the energy weight being negatively correlated with the remaining energy, and the sum of the energy weight and the distance weight being 1.

[0224] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining whether the remaining energy is greater than the preset energy threshold based on the difference between the remaining energy of the candidate sensor node and the preset energy threshold; if the remaining energy is greater than the preset energy threshold, assigning a first weight to the energy balancing factor and a second weight to the distance balancing factor, wherein the sum of the first weight and the second weight is 1; if the remaining energy is not greater than the preset energy threshold, assigning a third weight to the energy balancing factor and a fourth weight to the distance balancing factor, wherein the sum of the third weight and the fourth weight is 1, and the third weight is greater than the first weight.

[0225] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: counting the number of sensor nodes in the candidate node set that respond to the broadcast content sent by the cluster head node, wherein the sensor corresponding to the cluster head node sends the broadcast content according to a preset distance radius, and the sensors within the preset distance radius respond to the broadcast content and return an acknowledgment packet to the sensor acting as the cluster head node, and the number of nodes is determined according to the number of acknowledgment packets; determining the cluster node density based on the number of nodes and the coverage area of ​​the broadcast content sent by the cluster head node, wherein the coverage area is determined according to the preset distance radius; determining a preset number based on the cluster node density, wherein the preset number represents the number of sensor nodes in the same cluster as the cluster head node, and the preset number is positively correlated with the cluster node density; arranging the sensor nodes in the candidate node set in ascending order of their Euclidean distance from the cluster head node to obtain a candidate node queue, and selecting a preset number of sensor nodes from the candidate node queue in order from the head to the tail of the queue to obtain the cluster corresponding to the cluster head node.

[0226] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: counting the number of sensor nodes in the candidate node set that respond to the broadcast content sent by the cluster head node, wherein the sensor corresponding to the cluster head node sends the broadcast content according to a preset distance radius, and the sensors within the preset distance radius respond to the broadcast content and return an acknowledgment packet to the sensor acting as the cluster head node, and the number of nodes is determined according to the number of acknowledgment packets; determining the cluster node density based on the number of nodes and the coverage area of ​​the broadcast content sent by the cluster head node, wherein the coverage area is determined according to the preset distance radius; determining a preset number based on the cluster node density, wherein the preset number represents the number of sensor nodes in the same cluster as the cluster head node, and the preset number is positively correlated with the cluster node density; arranging the sensor nodes in the candidate node set in ascending order of their Euclidean distance from the cluster head node to obtain a candidate node queue, and selecting a preset number of sensor nodes from the candidate node queue in order from the head to the tail of the queue to generate the cluster corresponding to the cluster head node.

[0227] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: in the case of each hop transmission, traversing multiple sensor nodes in the cluster to obtain a data sending node; detecting the remaining energy of each sensor node in the cluster and the link quality between each sensor node and the data sending node; selecting a data receiving node from the multiple sensor nodes in the cluster based on the remaining energy and link quality, wherein the remaining energy and link quality of the data receiving node meet preset transmission conditions; in the case of each hop transmission, the data sending node sends a data acquisition packet to the data receiving node, and determines the data sending node and data receiving node for the next hop transmission, until the data receiving node is the cluster head node, wherein the data sending node for the next hop transmission is the data receiving node for this hop transmission, and the data acquisition packet includes at least: the acquisition data of the sensor that is the data sending node, and the data acquisition packet received by the data sending node.

[0228] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the importance of each associated factor affecting the selection of a data receiving node, wherein the associated factors include at least: remaining energy and link quality, and the importance is a numerical representation of the influence of the associated factors on the selection of the data receiving node; determining the factor weight of each associated factor based on the ratio of the importance of each associated factor to the cumulative importance of multiple associated factors, wherein the cumulative importance is the sum of the importance of multiple associated factors; performing a weighted summation of multiple associated factors based on the factor weight of each associated factor to obtain a candidate score for each sensor node; and selecting the sensor node with the highest candidate score as the data receiving node.

[0229] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: in the case of each hop transmission, detecting the signal strength between the data sending node and the data receiving node; determining the transmit power of the data sending node based on the signal strength, wherein the transmit power is negatively correlated with the signal strength; and having the data sending node send a data acquisition packet to the data receiving node according to the transmit power.

[0230] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining signal correlation parameters that affect signal strength, wherein the signal correlation parameters include at least: the signal transmission distance between data transmitting nodes; analyzing the signal transmission distance using the Friesian transmission equation to obtain the transmission power, wherein the transmission power is negatively correlated with the signal transmission distance.

[0231] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: detecting the data value of the collected data transmitted by each sensor node through the cluster head node, wherein the data value is determined at least based on data timeliness, data relevance, and data centralization. Data timeliness is an evaluation value determined based on the time difference between the time the cluster head node receives the collected data and the time the collected data is collected. Data relevance is an evaluation value determined based on the similarity between the collected data and pre-set demand data. Data centralization is an evaluation value determined at least based on the amount of data in the collected data. Data timeliness is negatively correlated with the time difference, data relevance is positively correlated with similarity, data centralization is positively correlated with the amount of data, and data value is positively correlated with data timeliness, data relevance, and data centralization. Each piece of collected data is transmitted to the data center in descending order of data value.

[0232] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it implements the steps of the data transmission method provided in the above embodiments.

[0233] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0234] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0235] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0236] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0237] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0238] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0239] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A data transmission method, characterized in that, include: Obtain a set of sensor nodes in a wireless sensor network, wherein each sensor node in the set corresponds to a sensor in the wireless sensor network; Multiple sensor nodes in the sensor node set are iteratively clustered multiple times to obtain multiple clusters. In each iteration of clustering, a clustering evaluation index for each sensor node is determined based on the energy balance factor and distance balance factor of each sensor node in the candidate node set. The sensor node with the smallest clustering evaluation index is selected as the cluster head node for this clustering. The candidate node set includes sensor nodes that were not clustered in the sensor set before this clustering. The energy balance factor is determined based on the remaining energy of the sensor node, and the distance balance factor is determined based on at least the distance between the sensor nodes. The clustering evaluation index is positively correlated with the remaining energy and positively correlated with the distance balance factor. The sensor nodes in the same cluster transmit the collected data of each sensor node to the cluster head node in a multi-hop transmission manner.

2. The method according to claim 1, characterized in that, Each iteration of the clustering process includes: Based on the sensor location of each sensor node in the candidate node set, a distance equalization factor is determined for each sensor node. The sensor location is used to determine at least a first distance between each sensor node and the center location of the wireless sensor network, and a second distance between each sensor node and other sensors in the candidate node set. The distance equalization factor is positively correlated with the first distance and negatively correlated with the second distance. Based on the remaining energy of each sensor node in the candidate node set, the energy balance factor and weight factor corresponding to each sensor node are determined, wherein the energy balance factor is positively correlated with the remaining energy, and the weight factor is determined based on the difference between the remaining energy and a preset energy threshold, and is used to represent the degree of influence of the distance balance factor and the remaining energy on the clustering evaluation index, respectively. Based on the energy balance factor and the distance balance factor, and the weight factors corresponding to the energy balance factor and the distance balance factor, the clustering evaluation index of each sensor node is determined. In the candidate node set, the sensor node with the smallest clustering evaluation index is determined as the cluster head node for this clustering. In the candidate node set, sensor nodes whose node relationships with the cluster head node meet the clustering conditions are determined to obtain the cluster cluster obtained in this clustering. The clustering conditions are at least used to indicate that a preset number of sensor nodes whose Euclidean distance with the cluster head node meets the preset distance requirement are selected.

3. The method according to claim 2, characterized in that, Based on the sensor location of each sensor node in the candidate node set, the distance equalization factor corresponding to each sensor node is determined as follows: The multiple sensor nodes in the candidate node set are traversed to obtain the candidate sensor nodes traversed each time, wherein the sensor position of the candidate sensor node is the candidate position; The first distance between the candidate sensor nodes is determined based on the location of the candidate node and the location of the network center of the wireless sensor network. Based on the candidate node position and the sensor positions of other sensor nodes in the candidate node set, the node distance between the candidate sensor node and each sensor node in the candidate node set is determined, and based on the node distance, a second distance is determined, wherein the second distance is the average value of the node distances; The distance equalization factor of the candidate sensor node is determined based on the ratio of the first distance to the second distance of the candidate sensor node.

4. The method according to claim 2, characterized in that, Based on the remaining energy of each sensor node in the candidate node set, the energy balance factor and weighting factor corresponding to each sensor node are determined as follows: The process involves traversing multiple sensor nodes in the candidate node set to obtain the candidate sensor nodes traversed each time, and determining the remaining energy of the candidate sensor nodes. The energy balance factor of the candidate sensor node is determined based on the ratio of the remaining energy to the initial energy of the candidate sensor node. Based on the difference between the remaining energy of the candidate sensor node and the preset energy threshold, corresponding weight factors are assigned to the energy balancing factor and the distance balancing factor. The weight factors include: the energy weight corresponding to the energy balancing factor and the distance weight corresponding to the distance balancing factor. The energy weight is negatively correlated with the remaining energy, and the sum of the energy weight and the distance weight is 1.

5. The method according to claim 4, characterized in that, Based on the difference between the remaining energy of the candidate sensor node and the preset energy threshold, corresponding weighting factors are assigned to the energy equalization factor and the distance equalization factor, including: Based on the difference between the remaining energy of the candidate sensor node and the preset energy threshold, it is determined whether the remaining energy is greater than the preset energy threshold; When the remaining energy is greater than the preset energy threshold, a first weight is assigned to the number of energy balance factors, and a second weight is assigned to the distance balance factor, wherein the sum of the first weight and the second weight is 1; If the remaining energy is not greater than the preset energy threshold, a third weight is assigned to the energy balance factor and a fourth weight is assigned to the distance balance factor, wherein the sum of the third weight and the fourth weight is 1, and the third weight is greater than the first weight.

6. The method according to claim 2, characterized in that, From the candidate node set, determine the sensor nodes whose node relationships with the cluster head node meet the clustering conditions. The resulting clusters are: The number of sensor nodes in the candidate node set that respond to the broadcast content sent by the cluster head node is counted. The sensor corresponding to the cluster head node sends the broadcast content according to a preset distance radius. The sensors within the preset distance radius respond to the broadcast content and return an acknowledgment packet to the sensor that is the cluster head node. The number of nodes is determined according to the number of acknowledgment packets. The cluster node density is determined based on the number of nodes and the coverage area of ​​the broadcast content emitted by the cluster head node, wherein the coverage area is determined according to the preset distance radius; Based on the node density within the cluster, the preset number is determined, wherein the preset number represents the number of sensor nodes in the same cluster as the cluster head node, and the preset number is positively correlated with the node density within the cluster; The sensor nodes in the candidate node set are arranged in ascending order of their Euclidean distance from the cluster head node to obtain a candidate node queue. Then, a preset number of sensor nodes are selected from the candidate node queue in order from the head to the tail to obtain the cluster corresponding to the cluster head node.

7. The method according to claim 1, characterized in that, Transmitting the collected data of each sensor node to the cluster head node in a multi-hop transmission manner from multiple sensor nodes in the same cluster includes: In each hop of transmission, the multiple sensor nodes in the cluster are traversed to obtain the data sending node; The remaining energy of each sensor node in the cluster and the link quality between each sensor node and the data sending node are detected. Based on the remaining energy and the link quality, a data receiving node is selected from among the multiple sensor nodes in the cluster, wherein the remaining energy and the link quality of the data receiving node meet preset transmission conditions; In each hop of transmission, the data sending node sends a data acquisition packet to the data receiving node, and determines the data sending node and the data receiving node for the next hop of transmission, until the data receiving node is the cluster head node, wherein the data sending node for the next hop of transmission is the data receiving node for the current hop of transmission, and the data acquisition packet includes at least: the data acquisition data of the sensor that is the data sending node, and the data acquisition packet received by the data sending node.

8. The method according to claim 7, characterized in that, Based on the remaining energy and the link quality, the selection of a data receiving node from among the multiple sensor nodes in the cluster includes: Determine the importance of each associated factor affecting the selection of the data receiving node, wherein the associated factors include at least: the remaining energy and the link quality, and the importance is a numerical representation of the associated factor's influence on the selection of the data receiving node; The factor weight of each of the related factors is determined based on the ratio of the importance of each of the related factors to the cumulative importance of the multiple related factors, wherein the cumulative importance is the sum of the importance of the multiple related factors; Based on the factor weight of each of the associated factors, a weighted sum is performed on multiple associated factors to obtain a candidate score for each sensor node; The sensor node with the highest candidate score is selected as the data receiving node.

9. The method according to claim 7, characterized in that, In each hop of transmission, the data sending node sends the acquisition data packet to the data receiving node, including: In each hop of transmission, the signal strength between the data sending node and the data receiving node is detected; Based on the signal strength, the transmit power of the data transmitting node is determined, wherein the transmit power is negatively correlated with the signal strength; The data transmitting node sends the acquisition data packet to the data receiving node according to the transmission power.

10. The method according to claim 9, characterized in that, Determining the transmit power of the data transmitting node based on the signal strength includes: Obtain signal correlation parameters that affect the signal strength, wherein the signal correlation parameters include at least: the signal transmission distance between the data transmitting nodes; The transmission distance of the signal is analyzed using the Friesian transmission equation to obtain the transmission power, wherein the transmission power is negatively correlated with the transmission distance of the signal.

11. The method according to claim 1, characterized in that, After transmitting the collected data of each sensor node to the cluster head node in a multi-hop transmission manner from multiple sensor nodes in the same cluster, the method further includes: The cluster head node detects the data value of the collected data transmitted by each sensor node. The data value is determined at least based on data timeliness, data relevance, and data centralization. Data timeliness is an evaluation value determined by the time difference between the time the cluster head node receives the collected data and the time the data is collected. Data relevance is an evaluation value determined by the similarity between the collected data and pre-set demand data. Data centralization is an evaluation value determined at least by the amount of data in the collected data. Data timeliness is negatively correlated with the time difference, data relevance is positively correlated with the similarity, data centralization is positively correlated with the amount of data, and data value is positively correlated with data timeliness, data relevance, and data centralization. Each piece of collected data is transmitted to the data center in descending order of its value.

12. A data transmission device, characterized in that, include: An acquisition module is used to acquire a set of sensor nodes in a wireless sensor network, wherein each sensor node in the set corresponds to a sensor in the wireless sensor network. A clustering module is used to perform multiple iterative clustering on multiple sensor nodes in the sensor node set to obtain multiple clusters. In each iteration of clustering, based on the energy balance factor and distance balance factor of each sensor node in the candidate node set, a clustering evaluation index for each sensor node is determined, and the sensor node with the smallest clustering evaluation index is selected as the cluster head node for this clustering. The candidate node set includes: sensor nodes in the sensor set that were not clustered before this clustering. The energy balance factor is determined based on the remaining energy of the sensor node, and the distance balance factor is determined based at least on the distance between the sensor nodes. The clustering evaluation index is positively correlated with the remaining energy and positively correlated with the distance balance factor. The transmission module is used to transmit the collected data of each sensor node to the cluster head node in a multi-hop transmission manner from multiple sensor nodes in the same cluster.

13. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the data transmission method of any one of claims 1 to 11 through the computer program.

14. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the data transmission method according to any one of claims 1 to 11.

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