Dynamic networking method and device based on edge sensor network

Abstract

The invention provides a dynamic networking method and device based on an edge sensor network, which are applied to the technical field of computer networks, and the method comprises the steps: carrying out the sequential sorting based on the normalized residual energy of each data collection node, and obtaining a node energy sequence; determining an intermediate node path set based on the Euclidean distance from each intermediate node in the intermediate routing node set to other nodes in the intermediate routing node set; determining a first Euclidean distance between a head position node in the intermediate node path set and a sink node, and a second Euclidean distance between a tail position node in the intermediate node path set and the sink node; determining a backbone route of the intermediate node path set based on the first Euclidean distance and the second Euclidean distance; accessing each peripheral node into a backbone route to obtain a dynamic topology network of the edge sensor network; according to the invention, the dynamic networking of the edge sensor network with balanced energy consumption can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of computer networks, and particularly to a dynamic networking method and device based on an edge sensor network. Background Art

[0002] An edge sensor network (Wireless Sensor Network, WSN) is an edge network composed of distributed sensor nodes, and these nodes can sense and collect various information in the environment, such as temperature, humidity, pressure, light, etc. These data are collected and transmitted to a central processing unit or a cloud server for processing and analysis, so as to realize real-time monitoring and intelligent control of the environment.

[0003] In an edge sensor network, the data transmission method is generally that data acquisition nodes transmit data to a sink node through the shortest path or the minimum number of hops. However, when the nodes of the edge sensor network are densely deployed, it will cause a large number of data acquisition nodes to act as intermediate routing nodes, resulting in excessive energy consumption of some nodes and affecting the overall network life.

[0004] It can be seen that the networking method of the edge sensor network in the related technology has the technical problem of uneven node energy consumption. Summary of the Invention

[0005] The present invention provides a dynamic networking method and device based on an edge sensor network, which is used to solve the defect of uneven node energy consumption in the networking method of the edge sensor network in the prior art, and realize dynamic networking of an edge sensor network with balanced energy consumption.

[0006] The present invention provides a dynamic networking method based on an edge sensor network, including the following steps.

[0007] Sequentially sort based on the normalized remaining energy of each data acquisition node in the edge sensor network to obtain a node energy sequence; determine an intermediate routing node set and a peripheral routing node set based on the node energy sequence according to a target sorting threshold; determine an intermediate node path set based on the Euclidean distance from each intermediate node in the intermediate routing node set to other nodes in the intermediate routing node set; determine the first Euclidean distance between the head position node in the intermediate node path set and the sink node, and the second Euclidean distance between the tail position node in the intermediate node path set and the sink node, where the sink node is used to aggregate the node energy of each data acquisition node in the edge sensor network; determine the backbone routing of the intermediate node path set based on the magnitudes of the first Euclidean distance and the second Euclidean distance; connect each peripheral node in the peripheral routing node set to the backbone routing in the connection manner with the minimum transmission energy consumption to obtain the dynamic topology networking of the edge sensor network.

[0008] According to a dynamic networking method based on an edge sensor network provided by the present invention, the determining an intermediate node path set based on the Euclidean distance from each intermediate node in the intermediate routing node set to other nodes in the intermediate routing node set includes: taking the node with the largest normalized remaining energy in the intermediate routing node set as the current intermediate node; repeatedly executing the following process until each intermediate node in the intermediate routing node set is traversed to form an intermediate node path set: determining the Euclidean distance from the current intermediate node to other nodes in the intermediate routing node set; taking the node with the minimum Euclidean distance among the other nodes that have not been assigned as the next hop of any node as the next hop node of the current intermediate node; taking the next hop node as the new current intermediate node.

[0009] According to a dynamic networking method based on an edge sensor network provided by the present invention, each intermediate node in the intermediate node path set is sorted in descending order of node energy. The determining the backbone routing of the intermediate node path set based on the magnitudes of the first Euclidean distance and the second Euclidean distance includes: when the first Euclidean distance is less than the second Euclidean distance, connecting each intermediate node between the tail position node and the head position node in ascending order of energy, and connecting the sink node after the head position node to obtain the backbone routing of the intermediate node path set; when the first Euclidean distance is greater than the second Euclidean distance, connecting each intermediate node between the head position node and the tail position node in descending order of energy, and connecting the sink node after the tail position node to obtain the backbone routing of the intermediate node path set.

[0010] A dynamic networking method based on an edge sensor network provided by the present invention. Before connecting each peripheral node in the peripheral routing node set to the backbone routing according to the connection method with the minimum transmission energy consumption to obtain the dynamic topology networking of the edge sensor network, the method further includes: taking each peripheral node in the peripheral routing node set as the current peripheral node respectively, and determining the transmission energy consumption between the current peripheral node and any node in the backbone routing based on the node distance between the current peripheral node and any node in the backbone routing, where, when the node distance is less than or equal to a preset distance threshold, determining the transmission energy consumption of the current peripheral node based on the free space model; when the node distance is greater than the preset distance threshold, determining the transmission energy consumption of the current peripheral node based on the multipath fading model.

[0011] A dynamic networking method based on an edge sensor network provided by the present invention. The determining the transmission energy consumption of the current peripheral node based on the free space model includes: The determining the transmission energy consumption of the current peripheral node based on the multipath fading model includes: Wherein, represents the transmission energy consumption of the current peripheral node, represents the amount of data transmitted by the current peripheral node, represents the transmission circuit loss, represents the energy required for power amplification in the free space model, represents the energy required for power amplification in the multipath fading model, represents the node distance between the current peripheral node and any node in the backbone routing, represents the preset distance threshold.

[0012] A dynamic networking method based on an edge sensor network provided by the present invention. The method further includes: abstracting each data acquisition node and the connection relationship between each data acquisition node in the edge sensor network to obtain an abstract relationship table; when detecting a change in the topological relationship of the dynamic topology networking of the edge sensor network, updating the abstract relationship table of the changed node and the abstract relationship table of the adjacent nodes of the changed node.

[0013] The present invention also provides a dynamic networking device based on an edge sensor network, including the following modules: a sorting module, configured to perform sequential sorting based on the normalized remaining energy of each data acquisition node in the edge sensor network to obtain a node energy sequence; a set partitioning module, configured to determine an intermediate routing node set and a peripheral routing node set based on the node energy sequence according to a target sorting threshold; an intermediate node module, configured to determine an intermediate node path set based on the Euclidean distance between each intermediate node in the intermediate routing node set and other nodes in the intermediate routing node set; a distance determination module, configured to determine a first Euclidean distance between a head position node in the intermediate node path set and a convergence node, and a second Euclidean distance between a tail position node in the intermediate node path set and the convergence node, where the convergence node is configured to converge the node energy of each data acquisition node in the edge sensor network; a backbone routing module, configured to determine a backbone routing of the intermediate node path set based on the magnitudes of the first Euclidean distance and the second Euclidean distance; and a dynamic topology networking module, configured to connect each peripheral node in the peripheral routing node set to the backbone routing in a connection manner with the minimum transmission energy consumption to obtain the dynamic topology networking of the edge sensor network.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the program, it implements the dynamic networking method based on an edge sensor network as described in any one of the above.

[0015] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the dynamic networking method based on an edge sensor network as described in any one of the above.

[0016] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the dynamic networking method based on an edge sensor network as described in any one of the above.

[0017] The dynamic networking method and device based on an edge sensor network provided by the present invention divide the intermediate routing node set and the peripheral routing node set by sorting the normalized remaining energy of data acquisition nodes, which can ensure that nodes with higher energy undertake more routing tasks, thereby extending the lifespan of the entire network; by calculating the Euclidean distance between nodes in the intermediate routing node set to determine the path set, it can intuitively reflect the physical position relationship between nodes, which helps to construct a more compact and efficient routing path; selecting the backbone route based on the Euclidean distance between the head position node and the tail position node from the sink node can ensure that the data transmission path is as short as possible, reducing data transmission delay and energy consumption; connecting each peripheral node in the peripheral routing node set to any node in the backbone route in the connection manner with the minimum transmission energy consumption, and then obtaining a dynamic topology network, which can adapt to edge sensor networks of different scales and layouts, enhancing the scalability and adaptability of the network; thus, the edge sensor network realizes the balanced utilization of energy, avoiding the situation where some nodes fail prematurely due to overuse, thereby improving the stability and reliability of the entire network. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the accompanying drawings required for use in the description of the embodiments or the prior art will be briefly introduced one by one below. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a schematic flowchart of the dynamic networking method based on an edge sensor network provided by the present invention.

[0020] Figure 2 It is a schematic diagram of the topology of the edge sensor network provided by the present invention.

[0021] Figure 3 It is a schematic diagram of the data storage sub-strategy of the edge sensor network provided by the present invention.

[0022] Figure 4 It is a schematic diagram of the security processing sub-strategy of the edge sensor network provided by the present invention.

[0023] Figure 5 It is a schematic flowchart of the implementation process of the dynamic networking based on an edge sensor network provided by the present invention.

[0024] Figure 6 It is a schematic structural diagram of the dynamic networking device based on an edge sensor network provided by the present invention.

[0025] Figure 7It is a schematic diagram of the physical structure of the electronic device provided by the present invention. Detailed implementation manners

[0026] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] An edge network is a distributed computing and communication architecture that moves the focus of computing and data processing from traditional central servers to network edge nodes. This network architecture allows data to be processed and analyzed closer to its source of generation, enabling faster, low-latency responses and more efficient data processing. The edge network pushes computing and data storage closer to the edge, near the user or device, reducing the distance of data transmission, thereby reducing network congestion and transmission latency.

[0028] An edge sensor network (Wireless Sensor Network, WSN) is an edge network composed of distributed sensor nodes that can sense and collect various information in the environment, such as temperature, humidity, pressure, light, etc. This data is collected and transmitted to a central processing unit or a cloud server for processing and analysis, thereby achieving real-time monitoring and intelligent control of the environment. Applying WSN to an edge environment information collection system that requires high-throughput data transmission can solve problems such as high labor costs, long monitoring cycles, large amounts of data, and relatively low remote monitoring accuracy. With the rapid development of microprocessor technology, the volume of sensor nodes in edge sensor networks is getting smaller and the number of devices is getting larger. Their computing power, storage capacity, and communication capabilities have all been greatly improved.

[0029] There is a special node connected to the Internet in the WSN, called the sink node. The collected data of the data collection nodes in the network is sent to the sink node through single-hop or multi-hop routing, and then the sink node transmits it to the Internet, thus realizing the collection of network-collected data. This method can achieve efficient data collection of environmental data. However, the edge sensor nodes are generally powered by batteries, so their lifespan depends on the speed of energy consumption. The edge sensing nodes consume energy mainly in two ways, namely data transmission and data collection. Data transmission usually sends data to the sink node through the shortest path or the minimum number of hops. This method generally needs to pass through intermediate routing nodes. When the WSN nodes are densely deployed, some data collection nodes may frequently act as intermediate routing nodes to transmit data, resulting in excessive energy consumption, which in turn affects the overall lifespan of the network. In addition, the WSN topology generally adopts a centralized deployment method, and the data collected by the nodes is easily obtained maliciously, which is likely to cause security problems such as DOS attacks, replay attacks, and IP address attacks by attackers. This will cause network paralysis and large deviations in subsequent data analysis, affecting the accuracy of the network-collected data and reducing the network lifespan.

[0030] As can be seen from the above, the edge sensor network in the related technology has the following disadvantages: WSN usually uses the battery power supply method to provide energy for the nodes. When the battery runs out, the nodes fail. The edge sensing nodes consume energy during data transmission. The traditional data transmission method in WSN generally sends data from the data collection nodes to the sink node through the shortest path or the minimum number of hops. When the WSN nodes are densely deployed, it will cause a large number of data collection nodes to act as intermediate routing nodes, resulting in excessive node energy consumption and affecting the overall lifespan of the network.

[0031] To solve the above problems, the present invention provides a dynamic networking method based on an edge sensor network. By designing a dynamic networking strategy for the sensor network with balanced energy consumption and designing a dynamic routing topology between the data collection nodes and the sink node, it is possible to prevent the routing paralysis of the entire network caused by the energy exhaustion of individual intermediate nodes, achieve energy balance of the entire network, and improve the overall collection lifespan of the network.

[0032] Optionally, the dynamic networking method based on the edge sensor network in the embodiments of the present application can be executed by a server, or can be executed by a terminal device, or can also be jointly executed by a server and a terminal device. Taking the server as an example to execute the dynamic networking method based on the edge sensor network in this embodiment.

[0033] Data acquisition nodes in edge sensor networks are usually located in remote areas and cannot be continuously powered. Generally, battery power supply is used to provide energy for them. When the battery energy is exhausted, the nodes will no longer be able to collect data. If the edge sensor network adopts a fixed networking strategy and routing strategy, sensor nodes that frequently act as intermediate routing nodes will consume a large amount of energy, resulting in the inability to collect environmental data due to node energy exhaustion.

[0034] Therefore, the present invention designs a dynamic networking method based on edge sensor networks, which aims to select intermediate routing nodes that balance network energy consumption through dynamic networking during the data transmission process to form a data transmission path, thereby overall improving the acquisition lifespan of the sensor network.

[0035] Figure 1 is a schematic flowchart of the dynamic networking method based on edge sensor networks provided by the present invention. As Figure 1 shown, the method includes the following steps: Step 101, perform sequential sorting based on the normalized remaining energy of each data acquisition node to obtain a node energy sequence.

[0036] After calculating the normalized remaining energy of each data acquisition node in the edge sensor network, sort the data acquisition nodes according to the value of the normalized remaining energy. The sorting can be from high to low (indicating from the most energy - sufficient to the lowest energy), or from low to high (indicating from the lowest energy to the most energy - sufficient), depending on the application requirements.

[0037] In some embodiments, sort the data acquisition nodes in the edge sensor network in descending order of the normalized remaining energy of each data acquisition node to obtain a node energy sequence. For example, , where represents the node energy sequence, represents that the sorting of the normalized remaining energy is of the data acquisition node.

[0038] Through the embodiments of the present invention, the normalized remaining energy is used to evaluate the energy state of each node in the network; by comparing the normalized energy values, data acquisition nodes with sufficient energy and data acquisition nodes with insufficient energy can be identified (for example, setting a sufficient energy threshold); so as to facilitate subsequent implementation of load balancing in the network.

[0039] Step 102, determine an intermediate routing node set and a peripheral routing node set based on the node energy sequence according to a target sorting threshold.

[0040] In an embodiment of the present invention, a target sorting threshold is preset, which will be used to divide the node energy sequence into different intervals, so as to obtain an intermediate routing node set and a peripheral routing node set.

[0041] For example, the average value or median of the node energy sequence can be used as the threshold for intermediate routing nodes. Nodes above this threshold are considered to have sufficient energy and are suitable as intermediate routing nodes; nodes below this threshold are considered to have lower energy and are more suitable as peripheral routing nodes.

[0042] In some embodiments, the nodes in the intermediate routing node set are usually located at the center or critical path of the network, and are used to transmit data packets from the source node to the target node, while ensuring the integrity and reliability of the data.

[0043] The nodes in the peripheral routing node set are usually located at the edge or farther areas of the network, and participate less in data transmission tasks, but can play a key role in network expansion or fault recovery.

[0044] In some embodiments, select the nodes in the node energy sequence whose rankings are in the top 1 / 3 as intermediate routing nodes. If the number of nodes in the top 1 / 3 is not an integer, round up; use the remaining nodes in the node energy sequence as peripheral routing nodes.

[0045] By dividing the intermediate routing nodes and peripheral routing nodes, it can be ensured that nodes with sufficient energy undertake more data transmission tasks, while nodes with lower energy are used as backups or perform less transmission work. This helps to balance the energy consumption in the network, avoid some nodes from being prematurely exhausted due to overuse, and thus improve the energy efficiency of the entire network.

[0046] Step 103: Determine an intermediate node path set based on the Euclidean distances from each intermediate node in the intermediate routing node set to other nodes in the intermediate routing node set.

[0047] For each node in the intermediate routing node set, calculate its Euclidean distance to all other nodes.

[0048] The calculation formula for the Euclidean distance is: , where is the Euclidean distance between two nodes, and are the coordinates of the two nodes respectively.

[0049] Organize all the calculated Euclidean distances into a distance matrix, where the rows and columns of the matrix represent the nodes in the intermediate routing node set respectively.

[0050] According to the distance matrix, the path with the shortest Euclidean distance can be selected as the transmission path between intermediate nodes. For example, path planning algorithms (such as Dijkstra's algorithm, Floyd-Warshall algorithm, etc.) can be used to determine the shortest path.

[0051] Step 104, determine the first Euclidean distance between the head position node in the intermediate node path set and the aggregation node, and the second Euclidean distance between the tail position node in the intermediate node path set and the aggregation node.

[0052] Among them, the aggregation node is used to aggregate the node energy of each data collection node in the edge sensor network.

[0053] In some embodiments, the position of the head position node is , and the position of the tail position node is , and the position of the aggregation node is known, denoted as .

[0054] Calculate the Euclidean distance between the aggregation node and the head position node in the intermediate node path set. The formula is: .

[0055] Calculate the Euclidean distance between the aggregation node and the tail position node in the intermediate node path set. The formula is: .

[0056] Step 105, based on the magnitudes of the first Euclidean distance and the second Euclidean distance, determine the backbone route of the intermediate node path set.

[0057] In the embodiments of the present invention, compare the magnitudes of the first Euclidean distance and the second Euclidean distance to determine the direction of the backbone route.

[0058] For example, when the first Euclidean distance is less than the second Euclidean distance, connect each intermediate node between the tail position node and the head position node in ascending order of energy, and connect the aggregation node after the head position node to obtain the backbone route of the intermediate node path set; When the first Euclidean distance is greater than the second Euclidean distance, connect each intermediate node between the head position node and the tail position node in descending order of energy, and connect the aggregation node after the tail position node to obtain the backbone route of the intermediate node path set.

[0059] Step 106, connect each peripheral node in the peripheral route node set to the backbone route in the connection manner with the minimum transmission energy consumption to obtain the dynamic topology networking of the edge sensor network.

[0060] In the embodiment of the present invention, the remaining number of data collection nodes except the intermediate routing nodes (i.e., the node data of the peripheral routing node set) is M, and the peripheral routing node set is represented as ={ , }. Calculate the energy consumption when each node in the peripheral routing node set is directly connected to each node in the backbone routing, and select the connection method with the minimum energy consumption to connect each peripheral node in the peripheral routing node set to the backbone routing.

[0061] For example, connected to makes , compared with connected to other nodes in the backbone routing, the energy consumption is smaller. Then is connected to . Finally, when all the nodes in the peripheral routing node set are connected to the backbone routing, the low-energy consumption energy-balanced networking is completed, and the data collection nodes transmit data to the aggregation node through the backbone routing.

[0062] Through the above steps of the embodiment of the present invention, by dividing the intermediate routing node set and the peripheral routing node set based on the sorting of the normalized remaining energy of the data collection nodes, it can ensure that the nodes with higher energy undertake more routing tasks, thereby extending the lifespan of the entire network; by calculating the Euclidean distance between the nodes in the intermediate routing node set to determine the path set, it can intuitively reflect the physical position relationship between the nodes, which is helpful for constructing a more compact and efficient routing path; by selecting the backbone routing based on the Euclidean distance between the head position node and the tail position node from the aggregation node, it can ensure that the data transmission path is as short as possible, reducing data transmission delay and energy consumption; according to the connection method with the minimum transmission energy consumption, directly connect each peripheral node in the peripheral routing node set to any node in the backbone routing, and then obtain a dynamic topology networking, which can adapt to edge sensor networks of different scales and layouts, enhancing the scalability and adaptability of the network; thus, enabling the edge sensor network to achieve balanced energy utilization, avoiding the situation that some nodes fail prematurely due to overuse, thereby improving the stability and reliability of the entire network.

[0063] Before sorting the normalized remaining energy of each data collection node in sequence to obtain the node energy sequence, the above method further includes: Normalize the node energy of each collection node in the edge sensor network to obtain the normalized remaining energy of each collection node.

[0064] An edge sensor network is a network composed of a large number of sensor nodes deployed in an edge computing environment. These sensor nodes have wireless communication and computing capabilities, and sense, collect, and process physical or environmental information within the network coverage area through cooperation.

[0065] A collection node is a basic component unit in an edge sensor network, responsible for sensing and collecting physical or environmental information in the target area. These nodes are usually equipped with various types of sensors, such as temperature sensors, humidity sensors, light sensors, etc.

[0066] In some embodiments, record the remaining energy of each collection node, calculate the total remaining energy of all collection nodes, and calculate the normalized remaining energy of each collection node, that is, the normalized remaining energy of each collection node is the ratio of the remaining energy of each collection node to the total remaining energy of all collection nodes.

[0067] In an embodiment of the present invention, when at time t, the data collection nodes in the edge sensor network start to pack data and prepare to transmit it to the aggregation node, obtain the remaining energy and historical data collection volume information of the data collection nodes, and assume that the node The remaining energy is and the historical data collection volume is The normalized remaining energy of the node can be expressed by the following formula: where represents the normalized remaining energy of the data collection node , represents the remaining energy of the data collection node , represents the historical data collection volume of the data collection node .

[0068] According to a dynamic networking method based on an edge sensor network provided by the present invention, based on the Euclidean distance from each intermediate node in the intermediate routing node set to other nodes in the intermediate routing node set, determine the intermediate node path set, including: Take the node with the largest normalized remaining energy in the intermediate routing node set as the current intermediate node; Repeat the following process until each intermediate node in the intermediate routing node set is traversed to form an intermediate node path set: Determine the Euclidean distance from the current intermediate node to other nodes in the intermediate routing node set; Take the node with the smallest Euclidean distance among the other nodes that have not been assigned as the next hop of any node as the next hop node of the current intermediate node; Use the next-hop node as the new current intermediate node. In some embodiments, assume that K intermediate routing nodes are obtained, and the intermediate routing nodes are sorted in descending order of normalized remaining energy. The set of intermediate routing nodes is ={ , }.

[0069] Starting from , calculate the Euclidean distance between each node in and other nodes in . Select the node with the shortest Euclidean distance that has not been selected as the next hop of the current node. When all nodes have been calculated, the calculation of the intermediate node path is completed, and the intermediate node path set is formed as { , }.

[0070] Through the embodiments of the present invention, by calculating the Euclidean distance from the current intermediate node to other nodes and selecting the unselected node with the smallest distance as the next hop, it is possible to ensure the selection of the optimal transmission path within a local range. This helps to reduce the latency and energy consumption of data transmission. Each intermediate node only needs to consider its distance to adjacent nodes without having to master the global information of the entire network. This simplifies the routing decision-making process and reduces the computational complexity.

[0071] According to a dynamic networking method based on an edge sensor network provided by the present invention, each intermediate node in the intermediate node path set is sorted in descending order of node energy; Based on the magnitude of the first Euclidean distance and the second Euclidean distance, determine the backbone route of the intermediate node path set, including: When the first Euclidean distance is less than the second Euclidean distance, connect each intermediate node between the node at the tail position to the node at the head position in ascending order of energy, and connect the aggregation node after the node at the head position to obtain the backbone route of the intermediate node path set; When the first Euclidean distance is greater than the second Euclidean distance, connect each intermediate node between the node at the head position to the node at the tail position in descending order of energy, and connect the aggregation node after the node at the tail position to obtain the backbone route of the intermediate node path set.

[0072] When the first Euclidean distance is less than the second Euclidean distance, it means that the distance between the aggregation node and the node at the head position is closer. Therefore, when data is transmitted from the node at the tail position to the node at the head position, the energy of the nodes can be more effectively utilized, and the latency of data transmission can be reduced. At this time, the construction of the backbone route should follow the increasing order of energy from the tail to the head.

[0073] When the first Euclidean distance is greater than the second Euclidean distance, the distance between the sink node and the tail position node is closer, and it is more reasonable for data to be transmitted from the head position node to the tail position node. At this time, the construction of the backbone route should follow the order of decreasing energy from the head to the tail.

[0074] In some embodiments, each intermediate node in the set of intermediate routing nodes ={ , } is sorted in descending order of node energy, the head position node is the head position node in the set of intermediate node paths , and the tail position node is the tail position node in the set of intermediate node paths . The distances between the head position node , the tail position node and the sink node are calculated respectively.

[0075] If is closer to , then each intermediate node between the tail position node and the head position node is connected in ascending order of energy, and the sink node is connected after the head position node . The backbone route of the set of intermediate node paths obtained is . .

[0076] If is closer to , then each intermediate node between the head position node and the tail position node is connected in descending order of energy, and the sink node is connected after the tail position node . The backbone route of the set of intermediate node paths obtained is .

[0077] Through the embodiments of the present invention, according to the comparison result of the first Euclidean distance (the distance between the head position node and the sink node) and the second Euclidean distance (the distance between the tail position node and the sink node), the routing direction is dynamically selected; when the head position is closer to the sink node, data is selected to be transmitted from the tail to the head, and vice versa. This selection helps to reduce the total energy consumption of data transmission because data can be transmitted along a path with relatively low energy consumption.

[0078] A dynamic networking method based on an edge sensor network provided by the present invention further includes, before each peripheral node in the peripheral routing node set is connected to the backbone routing according to the connection method with the minimum transmission energy consumption to obtain the dynamic topology networking of the edge sensor network: Each peripheral node in the peripheral routing node set is respectively used as the current peripheral node, and the transmission energy consumption between the current peripheral node and any node in the backbone routing is determined based on the node distance between the current peripheral node and any node in the backbone routing, where it includes: When the node distance is less than or equal to the preset distance threshold, the transmission energy consumption of the current peripheral node is determined based on the free space model; When the node distance is greater than the preset distance threshold, the transmission energy consumption of the current peripheral node is determined based on the multipath fading model.

[0079] In some embodiments, assume the topology graph is an edge sensor network, where the set of nodes corresponds to all sensor nodes in the edge sensor network, where is a data acquisition node, is an aggregation node. Since the data acquisition nodes in the edge sensor network may be far from the aggregation node, data usually needs to be transmitted to the aggregation node through multiple intermediate node routes. The energy consumption of a wireless sensor network mainly consists of two parts, namely data transmission energy consumption and data reception energy consumption.

[0080] The data acquisition node can be used as a data sending node, and the intermediate routing nodes and the aggregation node through which the data acquisition node transmits data to the aggregation node are data receiving nodes. When the data sending node sends data, when the distance between two sensor nodes is less than the threshold, the power amplification loss adopts the free space model, otherwise the multipath fading model is adopted.

[0081] When the node distance is less than or equal to the preset distance threshold, the free space model is used to calculate the transmission energy consumption. The free space model is a radio wave propagation model under ideal propagation conditions and is suitable for short-distance communication. Within this distance range, the radio wave energy is neither absorbed by obstacles nor reflected or scattered, so the transmission energy consumption can be calculated more accurately.

[0082] When the node distance is greater than the preset distance threshold, the multipath fading model is used to calculate the transmission energy consumption. The multipath fading model takes into account the multiple-path propagation of the signal due to reflection, scattering, etc. during the propagation process, and the fading phenomenon caused by the interference of the signals on these paths. Therefore, when communicating over a long distance, the multipath fading model can calculate the transmission energy consumption more accurately.

[0083] When the node distance is greater than the preset distance threshold, the multipath fading model is used to calculate the transmission energy consumption. The multipath fading model takes into account the multiple-path propagation of the signal due to reflection, scattering, etc. during the propagation process, and the fading phenomenon caused by the interference of the signals on these paths. Therefore, when communicating over a long distance, the multipath fading model can calculate the transmission energy consumption more accurately.

[0084] A dynamic networking method based on an edge sensor network provided by the present invention determines the transmission energy consumption of current surrounding nodes based on a free space model, including: Determine the transmission energy consumption of current surrounding nodes based on a multipath fading model, including: Among them, represents the transmission energy consumption of the current surrounding node, represents the amount of data transmitted by the current surrounding node, represents the transmission circuit loss, represents the energy required for power amplification in the free space model, represents the energy required for power amplification in the multipath fading model, represents the node distance between the current surrounding node and any node in the backbone route, represents a preset distance threshold.

[0085] In some embodiments, when receiving data, the calculation model of the energy consumption of the wireless sensor network (edge sensor network) is shown in the following formula: Among them, represents the receiving energy consumption, represents the amount of data transmitted by the current surrounding node, represents the transmission circuit loss.

[0086] When a node only serves as a data sender in the edge sensor network, it only generates data transmission energy consumption; when a node only serves as a data receiver, it only generates data receiving energy consumption; when a node serves as an intermediate routing node to route data sent by other nodes, it generates both data transmission energy consumption and data receiving energy consumption.

[0087] For example, assume that the data acquisition node transmits data to the aggregation node through the intermediate routing node set , generates data transmission energy consumption, generates data receiving energy consumption, the nodes within generate both data transmission and data receiving energy consumption.

[0088] Through the embodiments of the present invention, it is possible to calculate the energy consumption of different types of nodes. After understanding the node energy consumption characteristics of different types of nodes, a more reasonable network topology structure and routing protocol can be designed to improve the fault tolerance and robustness of the network. For example, when selecting a route, the remaining energy and energy consumption characteristics of the node can be considered to ensure the reliable transmission of data.

[0089] In the related art, the WSN topology generally adopts a centralized deployment method. It is easy for malicious parties to obtain the data collected by nodes, which may cause security problems such as DOS attacks, replay attacks, and IP address attacks by attackers. This will cause network paralysis and large deviations in subsequent data analysis, affecting the accuracy of the data collected by the network and reducing the network lifespan.

[0090] To address the above problems, the present invention provides a dynamic networking method based on an edge sensor network. The WSN network topology is managed by a blockchain to prevent attacks on network nodes; the data collected by nodes and topology data are stored in a distributed ledger; data collection encryption and timestamps are used to ensure the synchronization of blockchain data and the data collected by the aggregation node, preventing data tampering; and security problems such as DOS attacks, replay attacks, and IP address attacks are handled through a blacklist of malicious node IPs and node reputation scoring.

[0091] The present invention provides a dynamic networking method based on an edge sensor network, including: a dynamic networking strategy for an energy-balanced sensor network, an edge sensor network management strategy based on a blockchain, and an implementation plan. Among them, the dynamic networking strategy for an energy-balanced sensor network realizes the energy balance of the entire network and improves the overall data collection lifespan of the network by designing a dynamic routing topology between data collection nodes and aggregation nodes (specifically, reference can be made to the above embodiments). The edge sensor network management strategy based on a blockchain provides secure and trustworthy topology management and data synchronization for the dynamic networking strategy of the energy-balanced sensor network, preventing security risks such as malicious attacks.

[0092] According to a dynamic networking method based on an edge sensor network provided by the present invention, the above method further includes: abstracting each data collection node in the edge sensor network and the connection relationships between the data collection nodes to obtain an abstract relationship table; when detecting a change in the topology relationship of the dynamic topology networking of the edge sensor network, updating the abstract relationship table of the changed node and the abstract relationship table of the adjacent nodes of the changed node.

[0093] Among them, the abstract relationship table is stored in the distributed ledger. The abstract relationship table includes: keys and values; the keys include: node names, and the values include at least one of the following: node battery capacity, adjacent node names, link lengths between adjacent nodes, topology generation time, node public keys, and node reputation scores; Among them, the topological relationship changes include at least one of the following: re-networking, node offline, link failure, and the node reputation score drops below the threshold.

[0094] The present invention proposes a dynamic networking method based on an edge sensor network, which also includes an edge sensor network topology management sub-strategy, an edge sensor network data storage sub-strategy, an edge sensor network data synchronization sub-strategy, and an edge sensor network security processing sub-strategy.

[0095] In the edge sensor network topology management sub-strategy, the edge sensor network nodes and the connection relationships between nodes are abstracted and stored in a distributed ledger through an abstract relationship table in the form of <key, value>, where key is the node name and value includes the node battery capacity, adjacent node names, link lengths between adjacent nodes, topology generation time, node public key, and node reputation score.

[0096] Reference Figure 2 , Figure 2 is a schematic diagram of the edge sensor network topology provided by the present invention.

[0097] When the sensor network re-networks, a node goes offline, a link fails, or the node reputation score drops below the threshold through an energy consumption balanced sensor network dynamic networking strategy, the topological relationship changes. At this time, the abstract relationship tables of the changed nodes and their adjacent nodes are updated to achieve topological dynamic management. Assume that the edge sensor network topology diagram is as Figure 2 shown, and the abstract relationships of nodes and are shown in Table 1 and Table 2 respectively.

[0098] Table 1 Abstract relationship table of node

[0099] Table 2 Abstract relationship table of node

[0100] The business logic of the specific management process is implemented through the network topology management smart contract, which realizes the operations of adding, deleting, modifying, and querying the topological abstract relationships. For example, when a new node is added to the network, an abstract relationship table is created for this node, and the adjacent nodes are searched. Entries of the node name and the link length between this node and the adjacent node are added to the abstract relationship tables of the adjacent nodes. When the connection relationship between nodes changes, such as the change of the distance between nodes or node disconnection, the corresponding entries in the abstract relationship tables of the two nodes are updated. When a node crashes due to reasons such as energy exhaustion, failure, or the reputation score dropping below the threshold, the abstract relationship table of this node is deleted, and the entries related to this node in the abstract relationship tables of all its adjacent nodes are deleted. When requests such as data routing and node status viewing are generated, the node abstract relationship table is queried to obtain node information.

[0101] Reference Figure 3 , Figure 3 is a schematic diagram of the edge sensor network data storage sub-strategy provided by the present invention.

[0102] In the edge sensor network data storage sub-strategy, the edge sensor network data is stored in the distributed ledger. These data are divided into two categories, namely the network topology information of the edge sensor network and the acquisition data and timestamps of the edge sensors. The schematic diagram is as Figure 3 shown (where the network topology information is as Figure 2 shown).

[0103] The data processed by the edge sensor network topology management are all stored through the edge sensor network topology data storage smart contract. Since the amount of this type of data is small and will not cause a large storage burden on the blockchain, the storage method on the blockchain is adopted. For the edge sensor acquisition data, when the data acquisition node transmits data to the aggregation node, all nodes package the data to be transmitted and attach a timestamp label, sign it with the node private key, and store it through the edge sensor acquisition data storage smart contract. This type of data is first stored on the blockchain. When the edge sensor network data synchronization sub-strategy function execution ends, the edge sensor acquisition data storage smart contract stores this part of the data under the blockchain (off-chain storage system), and the corresponding data hash is permanently stored on the blockchain to ensure the traceability of the acquisition data.

[0104] In the edge sensor network data synchronization sub-strategy, after the data collected by the data acquisition nodes is transmitted to the aggregation node, a function for synchronizing and comparing the data collected by the aggregation node with the data stored on the blockchain is provided. First, the smart contract on the blockchain verifies to obtain the public keys of each node on the blockchain, and uses these public keys to decrypt the data transmitted by each node. After successful decryption, the timestamp is verified. If the timestamp of the data collected by the aggregation node is the same as the timestamp of the data on the blockchain, the verification passes, indicating that the data has not been tampered with during the transmission. If the smart contract on the blockchain collects data packets from non-network nodes, packets impersonating network nodes, or high-concurrency network node packets, these packets are transmitted to the edge sensor network security processing sub-strategy for processing.

[0105] Reference Figure 4 , Figure 4 is a schematic diagram of the edge sensor network security processing sub-strategy provided by the present invention.

[0106] In the edge sensor network security processing sub-strategy, the smart contract for node security analysis on the blockchain obtains the data packets from non-network nodes, packets impersonating network nodes, or high-concurrency network node packets collected by the smart contract for verification on the blockchain, and processes and analyzes these packets. The schematic diagram is as Figure 4 shown.

[0107] The smart contract for node security analysis on the blockchain differentiates the three types of data packets through the node IP and node key carried by the data packets. For data packets from non-network nodes, their IPs are different from the IPs of any node in the network. The node IP is obtained and added to the blacklist, which is stored on the blockchain. For packets impersonating network nodes, since the impersonating node cannot obtain the key information of the real node, data synchronization cannot be completed at the aggregation node. The smart contract for node security analysis on the blockchain obtains the IP of the impersonating node and reduces the reputation score of the real node corresponding to the IP. For high-concurrency network node packets, the node IP and concurrency are recorded, and the reputation score of the node is reduced according to the concurrency. When the reputation score of the node drops to a certain value, the node is locked as a malicious node.

[0108] The following describes an example of the dynamic networking method based on the edge sensor network provided by the present invention in actual applications.

[0109] Based on the above-mentioned edge sensor network management strategy based on blockchain and the dynamic networking strategy of the energy consumption balanced sensor network, the schematic diagram of the implementation process of the dynamic networking based on the edge sensor network provided by the present invention is as Figure 5 shown. This solution assumes that the underlying infrastructure of the blockchain has been built, the business logic of the smart contract has been implemented, and the initial sensor network nodes have been deployed. The specific process is as follows: Step 1: At time t, all data collection nodes in the sensor network pack the collected data, encrypt it using their respective private keys, and attach timestamps.

[0110] Step 2: Calculate the normalized remaining energy of each data collection node.

[0111] Among them, the node The normalized remaining energy can be expressed by the following formula: Among them, represents the normalized remaining energy of the data collection node , represents the remaining energy of the data collection node , represents the historical data collection volume of the data collection node .

[0112] Step 3: Sort the normalized energies of each data collection node calculated in Step 2 from high to low to obtain the node energy sequence as .

[0113] Step 4: Select the top one-third of the nodes in the node energy sequence as intermediate routing nodes to form an intermediate routing node set ={ , }.

[0114] Step 5: Starting from , calculate the Euclidean distance between each node in and other nodes in , and select the node with the shortest Euclidean distance that has not been selected as the next hop of the current node. Finally, form an intermediate node path set as ={ , }.

[0115] Step 6: Calculate the distances between , and the sink node respectively. If is closer to , then form the backbone route as , otherwise, the backbone route is .

[0116] Step 7: Assume that the remaining number of data collection nodes except the intermediate routing nodes is M, and the formed node set is represented as ={ , }.

[0117] Step 8, calculate the energy consumption when each node in

[0118] is directly connected to each node in the backbone route, and select the connection method with the minimum energy consumption.

[0119] The specific calculation method can refer to the above embodiments, and the present invention will not elaborate herein. Step 9, when the connection of all

[0120] nodes to the backbone route is completed, the low-energy consumption energy-balanced networking is completed. The edge sensor network topology management sub-strategy manages the current topology through the network topology management smart contract. The edge sensor network data storage strategy stores the current topology data through the network topology data storage smart contract.

[0121] Step 10, the acquisition node transmits the data packed in Step 1 to the aggregation node through the backbone route.

[0122] Step 11, when the data is transmitted to the aggregation node, verify the accuracy of each data packet through the on-chain verification smart contract in the edge sensor network data synchronization strategy. If the verification is successful, the strategy ends; otherwise, go to Step 12.

[0123] The present invention proposes a dynamic networking strategy for an energy-balanced sensor network. This strategy calculates the normalized remaining energy of data acquisition nodes, and selects the nodes with the top-ranked normalized remaining energy to jointly form a backbone route with the aggregation node. Other acquisition nodes calculate the minimum energy consumption through the sensor network energy consumption formula and connect to the intermediate routing nodes on the corresponding backbone route. When all nodes are interconnected, the dynamic networking of the energy-balanced sensor network is completed.

[0124] The present invention proposes a dynamic networking strategy for an edge sensor network based on blockchain. This strategy manages the WSN network topology through blockchain to prevent network node attacks; stores node acquisition data and topology data through a distributed ledger; ensures the synchronization of blockchain data and the data collected by the aggregation node through data encryption and time stamps to prevent data tampering; handles security issues such as DOS attacks, replay attacks, and IP address attacks through the malicious node IP blacklist and node reputation scoring; and finally designs a dynamic networking strategy for an energy-balanced sensor network. By designing a dynamic routing topology between data acquisition nodes and the aggregation node, the energy balance of the entire network is achieved, and the overall acquisition life of the network is improved.

[0125] The present invention proposes a management strategy for edge sensor network nodes based on blockchain, which consists of four sub-strategies: edge sensor network topology management, edge sensor network data storage, edge sensor network data synchronization, and edge sensor network security processing. The edge sensor network topology management sub-strategy realizes dynamic topology management through smart contracts; the edge sensor network data storage sub-strategy realizes trusted storage of topology and collected data through smart contracts; the edge sensor network data synchronization sub-strategy verifies the authenticity of data transmitted from collection nodes to aggregation nodes and the credibility of nodes through smart contracts; the edge sensor network security processing sub-strategy obtains malicious data packets and analyzes them through smart contracts, records the IPs of network malicious nodes or updates reputation scores.

[0126] The dynamic networking device based on the edge sensor network provided by the present invention will be described below. The dynamic networking device based on the edge sensor network described below can be correspondingly referred to the dynamic networking method based on the edge sensor network described above.

[0127] Reference Figure 6 , Figure 6 is a schematic structural diagram of the dynamic networking device based on the edge sensor network provided by the present invention.

[0128] The sorting module 601 is used to perform sequential sorting based on the normalized remaining energy of each data collection node in the edge sensor network to obtain a node energy sequence; The set partitioning module 602 is used to determine an intermediate routing node set and a peripheral routing node set based on the node energy sequence according to a target sorting threshold; The intermediate node module 603 is used to determine an intermediate node path set based on the Euclidean distance from each intermediate node in the intermediate routing node set to other nodes in the intermediate routing node set; The distance determination module 604 is used to determine a first Euclidean distance between the head position node in the intermediate node path set and the aggregation node, and a second Euclidean distance between the tail position node in the intermediate node path set and the aggregation node, where the aggregation node is used to aggregate the node energy of each data collection node in the edge sensor network; The backbone routing module 605 is used to determine the backbone routing of the intermediate node path set based on the magnitudes of the first Euclidean distance and the second Euclidean distance; The dynamic topology networking module 606 is used to connect each peripheral node in the peripheral routing node set to the backbone routing in the connection manner with the minimum transmission energy consumption to obtain the dynamic topology networking of the edge sensor network.

[0129] Specifically, the above-mentioned dynamic networking device based on an edge sensor network provided by the present invention can implement all the method steps implemented by the above-mentioned dynamic networking method embodiment based on an edge sensor network and can achieve the same technical effects. Therefore, the parts and beneficial effects that are the same as those in the method embodiment will not be specifically described herein again.

[0130] Figure 7 is a schematic physical structure diagram of an electronic device provided by the present invention. As Figure 7 shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730, and a communication bus 740. Among them, the processor 710, the communication interface 720, and the memory 730 complete communication with each other through the communication bus 740. The processor 710 can call the logical instructions in the memory 730 to execute the dynamic networking method based on an edge sensor network. The method includes: sequentially sorting based on the normalized remaining energy of each data acquisition node in the edge sensor network to obtain a node energy sequence; determining an intermediate routing node set and a peripheral routing node set based on the node energy sequence according to a target sorting threshold; determining an intermediate node path set based on the Euclidean distance from each intermediate node in the intermediate routing node set to other nodes in the intermediate routing node set; determining a first Euclidean distance between the head position node in the intermediate node path set and the aggregation node, and a second Euclidean distance between the tail position node in the intermediate node path set and the aggregation node, where the aggregation node is used to aggregate the node energy of each data acquisition node in the edge sensor network; determining the backbone routing of the intermediate node path set based on the magnitudes of the first Euclidean distance and the second Euclidean distance; and connecting each peripheral node in the peripheral routing node set to the backbone routing in the connection manner with the minimum transmission energy consumption to obtain the dynamic topology networking of the edge sensor network.

[0131] In addition, when the logical instructions in the above-mentioned memory 730 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0132] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the dynamic networking method based on an edge sensor network provided by the above-mentioned various methods. The method includes: sequentially sorting based on the normalized remaining energy of each data acquisition node in the edge sensor network to obtain a node energy sequence; determining an intermediate routing node set and a peripheral routing node set based on the node energy sequence according to a target sorting threshold; determining an intermediate node path set based on the Euclidean distance from each intermediate node in the intermediate routing node set to other nodes in the intermediate routing node set; determining a first Euclidean distance between the head position node in the intermediate node path set and the aggregation node, and a second Euclidean distance between the tail position node in the intermediate node path set and the aggregation node, where the aggregation node is used to aggregate the node energy of each data acquisition node in the edge sensor network; determining the backbone route of the intermediate node path set based on the magnitudes of the first Euclidean distance and the second Euclidean distance; and connecting each peripheral node in the peripheral routing node set to the backbone route in the connection manner with the minimum transmission energy consumption to obtain the dynamic topology networking of the edge sensor network.

[0133] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a dynamic networking method based on an edge sensor network provided by the above-mentioned various methods. The method includes: a dynamic networking method based on an edge sensor network, the method includes: sequentially sorting based on the normalized remaining energy of each data acquisition node in the edge sensor network to obtain a node energy sequence; determining an intermediate routing node set and a peripheral routing node set based on the node energy sequence according to a target sorting threshold; determining an intermediate node path set based on the Euclidean distance from each intermediate node in the intermediate routing node set to other nodes in the intermediate routing node set; determining a first Euclidean distance between the head position node in the intermediate node path set and the aggregation node, and a second Euclidean distance between the tail position node in the intermediate node path set and the aggregation node, where the aggregation node is used to aggregate the node energy of each data acquisition node in the edge sensor network; determining a backbone route of the intermediate node path set based on the magnitudes of the first Euclidean distance and the second Euclidean distance; connecting each peripheral node in the peripheral routing node set to the backbone route in the connection manner with the minimum transmission energy consumption to obtain a dynamic topology networking of the edge sensor network.

[0134] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative efforts.

[0135] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic networking method based on edge sensor network, characterized in that: include: Based on the normalized residual energy of each data collection node in the edge sensor network, the node energy sequence is obtained; Determine a set of intermediate routing nodes and a set of peripheral routing nodes based on the node energy sequence according to a target sorting threshold; Determining an intermediate node path set based on the Euclidean distance from each intermediate node in the intermediate routing node set to other nodes in the intermediate routing node set; Determine a first Euclidean distance between a head position node in the intermediate node path set and a sink node, and a second Euclidean distance between a tail position node in the intermediate node path set and the sink node, wherein the sink node is used to aggregate node energy of each data acquisition node in the edge sensor network; Determining a backbone route of the intermediate node path set based on the first Euclidean distance and the second Euclidean distance; According to the connection mode with the minimum transmission energy consumption, each peripheral node in the peripheral routing node set is connected to the backbone routing to obtain the dynamic topology networking of the edge sensor network.

2. The dynamic networking method based on edge sensor network according to claim 1, characterized in that: The determining of the intermediate node path set based on the Euclidean distance from each intermediate node in the intermediate routing node set to other nodes in the intermediate routing node set includes: Taking the node with the largest normalized residual energy in the intermediate routing node set as the current intermediate node; Repeat the following process until each intermediate node in the intermediate routing node set is traversed to form an intermediate node path set: Determine the Euclidean distance from the current intermediate node to other nodes in the set of intermediate routing nodes; Using the node with the smallest Euclidean distance among other nodes that are not assigned as the next hop of any node as the next hop node of the current intermediate node; The next hop node is used as a new current intermediate node.

3. The dynamic networking method based on edge sensor network according to claim 1, characterized in that: Each intermediate node in the intermediate node path set is sorted from large to small according to node energy; The determining the backbone route of the intermediate node path set based on the first Euclidean distance and the second Euclidean distance includes: When the first Euclidean distance is less than the second Euclidean distance, each intermediate node between the tail position node and the head position node is connected in order of energy from small to large, and the convergence node is connected after the head position node to obtain the backbone route of the intermediate node path set; When the first Euclidean distance is greater than the second Euclidean distance, each intermediate node between the head position node and the tail position node is connected in descending order of energy, and the convergence node is connected to the tail position node to obtain the backbone route of the intermediate node path set.

4. The dynamic networking method based on edge sensor network according to claim 1, characterized in that: Before connecting each peripheral node in the peripheral routing node set to the backbone routing in accordance with the connection mode with the minimum transmission energy consumption to obtain the dynamic topology networking of the edge sensor network, the method further includes: Each peripheral node in the peripheral routing node set is used as a current peripheral node, and based on the node distance between the current peripheral node and any node in the backbone routing, the transmission energy consumption between the current peripheral node and any node in the backbone routing is determined, wherein the method includes: When the node distance is less than or equal to a preset distance threshold, determining the transmission energy consumption of the current surrounding nodes based on a free space model; When the node distance is greater than the preset distance threshold, the transmission energy consumption of the current peripheral nodes is determined based on a multipath fading model.

5. The dynamic networking method based on edge sensor network according to claim 4, characterized in that: The determining the transmission energy consumption of the current surrounding nodes based on the free space model includes: The determining the transmission energy consumption of the current peripheral nodes based on the multipath fading model includes: in, represents the sending energy consumption of the current surrounding nodes, Indicates the amount of data sent by the current surrounding node, represents the transmission circuit loss, represents the energy required for power amplification in the free space model, represents the energy required for power amplification in the multipath fading model, represents the node distance between the current peripheral node and any node in the backbone routing, Indicates the preset distance threshold.

6. The dynamic networking method based on edge sensor network according to claim 1, characterized in that: The method further comprises: Abstracting each data acquisition node in the edge sensor network and the connection relationship between each data acquisition node to obtain an abstract relationship table; When a change in the topological relationship of the dynamic topological networking of the edge sensor network is detected, the abstract relationship table of the changed node and the abstract relationship table of the neighboring nodes of the changed node are updated.

7. A dynamic networking device based on an edge sensor network, characterized in that: include: A sorting module is used to sort the normalized residual energy of each data collection node in the edge sensor network to obtain a node energy sequence; A set partitioning module, used to determine the intermediate routing node set and the peripheral routing node set based on the node energy sequence according to the target sorting threshold; An intermediate node module, configured to determine an intermediate node path set based on the Euclidean distance from each intermediate node in the intermediate routing node set to other nodes in the intermediate routing node set; a distance determination module, used to determine a first Euclidean distance between a head position node in the intermediate node path set and a sink node, and a second Euclidean distance between a tail position node in the intermediate node path set and the sink node, wherein the sink node is used to aggregate node energy of each data acquisition node in the edge sensor network; A backbone routing module, configured to determine a backbone routing of the intermediate node path set based on the first Euclidean distance and the second Euclidean distance; The dynamic topology networking module is used to connect each peripheral node in the peripheral routing node set to the backbone routing in a connection mode with minimum transmission energy consumption to obtain the dynamic topology networking of the edge sensor network.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the dynamic networking method based on the edge sensor network as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the dynamic networking method based on an edge sensor network as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the dynamic networking method based on an edge sensor network as claimed in any one of claims 1 to 6 is implemented.

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