WSN node optimization deployment method based on greedy DBSCAN algorithm

Abstract

The invention provides a WSN node optimization deployment method based on a greedy DBSCAN algorithm, which mainly reduces the number of minimized deployment nodes and improves the coverage precision in an electromagnetic sensing scene. According to the method, a greedy algorithm and a DBSCAN algorithm are combined, and an electromagnetic coverage model suitable for the field three-dimensional terrain is introduced according to the propagation characteristics of electromagnetic waves, so that the coverage range is evaluated. According to the invention, on the premise of ensuring the target coverage requirement, the optimal node deployment number is realized. Compared with a traditional heuristic algorithm, under the same topographic condition, the number of needed nodes can be reduced by more than 80%.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless sensor networks, and mainly relates to a method for deploying nodes in an electromagnetic perception wireless sensor network. Background Art

[0002] A wireless sensor network (WSN) is a distributed sensor network, often called the "nerve endings" of the Internet of Things (IoT). It is a crucial component of the IoT and primarily consists of wireless sensor nodes capable of sensing various types of information. Each sensor node possesses sensing, communication, and computing capabilities. Numerous nodes form a self-organizing network system through single or multi-hop connections. This network system offers real-time sensing, information collection, fusion, and transmission capabilities, and is widely used in military exploration, environmental monitoring, emergency communications, and other fields.

[0003] For WSNs, the primary issue to address is node deployment, which directly affects the accuracy and comprehensiveness of monitoring results. During deployment, the deployment method affects coverage. Furthermore, coverage is often used to evaluate deployment results, and the calculation method can also significantly impact coverage results.

[0004] Currently, wireless sensor network deployment methods fall into two main categories: maximizing coverage and minimizing the number of nodes. Maximizing coverage is typically solved using heuristic algorithms, which continuously adjust the locations of deployed nodes and other parameters to optimize coverage. As the number of iterations increases, coverage gradually approaches the optimal value. Deployment methods designed to minimize the number of nodes, on the other hand, reduce deployment costs by reducing the number of nodes, while ensuring that the deployment results meet the coverage target. Existing research primarily uses heuristic algorithms to solve deployment methods that minimize the number of nodes. However, these methods suffer from excessive iterations and low optimization efficiency in 3D terrain, and the resulting solutions are not optimal for minimizing nodes. Furthermore, existing research uses coverage calculation methods that are determined solely by the distance between nodes and targets. When applied to electromagnetic sensing scenarios, these methods fail to consider the impact of terrain shielding, radio frequency, and power on coverage, resulting in inaccurate coverage. Summary of the Invention

[0005] In order to overcome the shortcomings of the existing technology, reduce and minimize the number of deployed nodes, and improve the coverage accuracy in electromagnetic sensing scenarios, the present invention provides a WSN node optimization deployment method based on the greedy DBSCAN algorithm.

[0006] A WSN node optimization deployment method based on the greedy DBSCAN algorithm includes the following steps: Step 1: Build a simulation environment and obtain a grid terrain model of the target deployment area; Establish a grid terrain model based on the digital elevation model (DEM) of the target deployment area; Step 2: Determine the candidate set based on the grid terrain model; the candidate set is a collection of wireless sensor network node deployment locations; all elements in the candidate set satisfy: Multi-transmitter and multi-receiver coverage in a scenario where all elements in the candidate set are simultaneously transmitted by multiple transmitting sites and received by multiple receiving sites Meet the target coverage requirements; the multi-transmit multi-receive coverage is calculated based on the multi-transmit receive power of all receiving sites; the multi-transmit receive power of all receiving sites is calculated based on the Longley-Rice model; Step 3: Based on the candidate set, use the DBSCAN algorithm to cluster the candidate set and obtain the classification center; each classification center is the deployment location of the wireless sensor network node.

[0007] Furthermore, methods for determining the candidate set include greedy algorithms, particle swarm optimization algorithms, and genetic algorithms.

[0008] Furthermore, the steps to determine the candidate set using the greedy algorithm are: Step 2-1: Set the initial candidate set and survey collection ,in , , that is, the initial candidate set and the inspection set are initialized to empty sets; Step 2-2: Take the starting grid point of the grid terrain model as the inspection point and add the starting grid point to the inspection set middle; Step 2-3: Deploy a transmitting station at the survey point and set the transmitting frequency and transmitting power of the transmitting station; Step 2-4: Set the parameters of the electromagnetic propagation model according to the environmental conditions of the deployment area and evenly distribute the deployment area. The receiving sensitivity of each receiving station is set, and the Longley-Rice model is used to calculate the receiving sensitivity of the first receiving station. Received power at each receiving station The parameters of the electromagnetic propagation model include the receiving sensitivity of the receiving station, the transmitting power of the transmitting station, and the transmitting frequency of the transmitting station. Step 2-5: Calculate the Coverage of receiving sites : (1) in, It is The coverage of the receiving sites, It is The receiving power of each receiving station, It is The receiving sensitivity of each receiving site. If the receiving power at the receiving site is greater than or equal to the receiving site sensitivity, it means that the site can be covered by the transmitting site, and its coverage is marked as "1". Otherwise, the site cannot be covered, and its coverage is marked as "0". Step 2-6: Calculate the single-transmitter-multiple-receiver coverage rate of the survey point to the target deployment area; Traversal receiving sites, obtain the coverage of all receiving sites, and calculate the Single-transmitter-multiple-receiver coverage rate of each inspection point in the target deployment area : (2) In the formula For the The single-transmitter-multiple-receiver coverage rate of each inspection point in the target deployment area is For the launch site Coverage conditions at each receiving site; Step 2-7: Take the next grid point of the current grid terrain model as the inspection point and add the inspection point to the inspection set Repeat steps 2-3 to 2-6 until all grid points of the grid terrain model are used as inspection points; Step 2-8: Test set The elements in the array are sorted in descending order according to the size of the single-transmitter-multiple-receiver coverage obtained by the element as the inspection point, and the first Elements are included in the initial candidate set in, and from Eliminate this elements; Generate thresholds for candidate sets; Step 2-9: Initial candidate set All elements in are considered as inspection points, and the initial candidate set All elements in the image are used as inspection points at the same time, and the Longley-Rice model is used to obtain the multi-transmission receiving power of all receiving sites. The multi-transmission and multi-receiving coverage of all elements as inspection points is calculated based on the multi-transmission receiving power of all receiving sites. That is, the multi-transmission and multi-receiving coverage is obtained when multiple transmitting sites transmit at the same time and multiple receiving sites receive at the same time. ; if , then jump to step 2-8; if , then the initial candidate set All elements in the set are candidate sets , go to step 3; is the target coverage, is the coverage margin.

[0009] Furthermore, based on the candidate set, the DBSCAN algorithm is used to cluster the candidate set. The steps to obtain the classification center are as follows: Step 3-1: Based on the results of step 2, apply the DBSCAN algorithm to perform clustering and weight reduction; First, set the cluster radius to 1, and then calculate the candidate set The multiple transmission and multiple reception coverage of all elements in ,like , then randomly increase the cluster radius; calculate the multi-transmit and multi-receive coverage rate , until , the most recent satisfaction The result of classification is the result of the candidate set The elements in are divided into several categories and noise; Step 3-2: Calculate cluster centers; Record candidate set The multiple-transmission receiving power of the receiving site in the classification is calculated, and according to the classification results, the element with the highest single-transmission multiple-receiver coverage in each class is taken as the center of the class; for each noise point, each noise point is regarded as a separate class, and the point is taken as the center of the class.

[0010] Furthermore, a receiving sensitivity range is set, and corresponding colors are assigned according to the range of the multi-transmitted receiving power obtained in step 3-2 to obtain a coverage range visualization map; the base map of the coverage range visualization map is a satellite image of the deployment area.

[0011] The present invention combines greedy thinking with the DBSCAN algorithm to obtain a candidate set through greedy traversal, then reduces the size of the candidate set through clustering, thereby reducing the number of deployed nodes. Furthermore, an electromagnetic coverage model is introduced to improve the accuracy of the electromagnetic sensing wireless sensor network's coverage and enable visualization of the coverage area. This method optimizes the number of node deployments in complex three-dimensional terrain environments while ensuring target coverage. Compared to heuristic algorithms, under the same terrain conditions, this method can reduce the number of required nodes by over 80%. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a step diagram of the wireless sensor node optimization deployment method based on the greedy DBSCAN clustering algorithm of the present invention; Figure 2 It is the restored topographic map of the deployment area; Figure 3 It is aimed at Figure 2A visualization of the deployment results for the deployment area shown. DETAILED DESCRIPTION

[0013] The technical solution adopted by the present invention to solve the technical problem is a WSN node optimization deployment method based on the greedy DBSCAN algorithm, which includes the following steps: Step 1: Build a simulation environment and obtain a grid terrain model of the target deployment area; Establish a grid terrain model based on the digital elevation model (DEM) of the target deployment area; Step 2: Greedy selection of candidate sets ; Step 2-1: Set the initial candidate set and survey collection ,in , , that is, the initial candidate set and the inspection set are initialized to empty sets; Step 2-2: Take the starting grid point of the grid terrain model as the inspection point and add the starting grid point to the inspection set middle; Step 2-3: Deploy a transmitting station at the survey site and set the transmitting frequency and power of the transmitting station; Step 2-4: Set the parameters of the electromagnetic propagation model according to the environmental conditions of the deployment area and evenly distribute the deployment area. The receiving sensitivity of each receiving station is set, and the Longley-Rice model is used to calculate the receiving sensitivity of the first receiving station. The received power at each receiving station The parameters of the electromagnetic propagation model include the receiving sensitivity of the receiving station, the transmitting power of the transmitting station, and the transmitting frequency of the transmitting station. Step 2-5: Calculate the Coverage of receiving sites : (1) in, It is The coverage of the receiving sites, It is The receiving power of each receiving station, It is The receiving sensitivity of each receiving site. If the receiving power at the receiving site is greater than or equal to the receiving site sensitivity, it means that the site can be covered by the transmitting site, and its coverage is marked as "1". Otherwise, the site cannot be covered, and its coverage is marked as "0". Step 2-6: Calculate the single-transmitter-multiple-receiver coverage rate of the survey point to the target deployment area; Traversal receiving sites, obtain the coverage of all receiving sites, and calculate the Single-transmitter-multiple-receiver coverage rate of each inspection point in the target deployment area : (2) In the formula For the The single-transmitter-multiple-receiver coverage rate of each inspection point in the target deployment area is For the launch site The coverage of each receiving site; the essence of formula (2) is the proportion of covered receiving sites in all receiving sites; Step 2-7: Take the next grid point of the current grid terrain model as the inspection point and add the inspection point to the inspection set Repeat steps 2-3 to 2-6 until all grid points of the grid terrain model are used as inspection points; Step 2-8: Test set The elements in the array are sorted in descending order according to the size of the single-transmitter-multiple-receiver coverage obtained by the element as the inspection point, and the first Elements are included in the initial candidate set in, and from Eliminate this elements; Generate a threshold for candidate sets. This threshold determines the speed of candidate set generation. A higher threshold speed increases candidate set generation speed, but also slows down the calculation of multi-transmitter and multi-receiver coverage. Step 2-9: All elements in are considered as inspection points, and the initial candidate set All elements in the image are used as inspection points at the same time, and the Longley-Rice model is used to obtain the multi-transmission receiving power of all receiving sites. The multi-transmission and multi-receiving coverage of all elements as inspection points is calculated based on the multi-transmission receiving power of all receiving sites. That is, the multi-transmission and multi-receiving coverage is obtained when multiple transmitting sites transmit at the same time and multiple receiving sites receive at the same time. The Longley-Rice model is used to predict the path loss of electromagnetic waves over irregular terrain. When calculating the received power at a receiving station, applying the Longley-Rice model allows the calculation results to more accurately represent the electromagnetic propagation characteristics in actual scenarios, resulting in more accurate calculation results. if , then jump to step 2-8; if , then the initial candidate set All elements in the set are candidate sets , go to step 3; is the target coverage, is the coverage margin; Step 3: Clustering and weight reduction based on DBSCAN algorithm; Step 3-1: Based on the results of step 2, apply the DBSCAN algorithm to perform clustering and weight reduction; First, set the cluster radius to 1, and then calculate the candidate set The multiple transmission and multiple reception coverage of all elements in ,like , then randomly increase the cluster radius; calculate the multi-transmit and multi-receive coverage rate , until , the most recent satisfaction The result of classification is the result of the candidate set The elements in are divided into several categories and noise; Step 3-2: Calculate cluster centers; Record candidate set The multi-transmission receiving power of the receiving station in the classification is calculated, and according to the classification results, the element with the highest single-transmission multi-receiver coverage in each class is regarded as the center of the class; for each noise point, each noise point is regarded as a separate class, and the point is regarded as the center of the class; Step 4: Use each cluster center as the deployment location of the wireless sensor network node; Step 5: Coverage visualization; Set appropriate receiving sensitivity intervals to assign different colors. According to the intervals of the multi-transmitter receiving power recorded in step 3-2, corresponding colors are assigned to form an electromagnetic coverage layer, while the base map is a satellite image of the deployment area. The essence of this method is to draw contour maps of the multi-transmitter receiving power at the receiving site and fill the areas between adjacent layers with colors.

[0014] like Figure 2 Taking the deployment environment shown in FIG. 1 as an example, the technical solution in the embodiment of the present invention is described. Figure 1 The flowchart of the present invention is shown.

[0015] A WSN node optimization deployment method based on the greedy DBSCAN algorithm includes the following steps: Step 1: Build a simulation environment and obtain a grid terrain model of the target deployment area; Establish a grid terrain model based on the digital elevation model (DEM) of the target deployment area; Step 2: Greedy selection of candidate sets; Step 2-1: Set the initial candidate set and survey collection ,in , , that is, the initial candidate set and survey collection All are initialized to empty sets; Step 2-2: Take the starting grid point of the grid terrain model as the inspection point and add the starting grid point to the inspection set middle; Step 2-3: Deploy a transmitter site at the survey point and set the transmission frequency of the transmitter site and power ; Step 2-4: Evenly distribute the deployment area receiving stations and use the Longley-Rice model to calculate the The received power at each receiving station ; The Longley-Rice model, also known as the Irregular Terrain Model (ITM), is a semi-empirical model that comprehensively considers direct, reflected, and diffraction propagation mechanisms. It is used to predict the path loss of electromagnetic waves on irregular terrain. The current mature Longley-Rice model calculation program already supports multi-antenna transmission and reception scenarios, and can simulate the complex situation of multiple transmitting sites working simultaneously and multiple receiving sites receiving in parallel. In the single-transmitting site scenario, the program can calculate the single-transmitting received power of each receiving site; in the multi-transmitting site scenario, it can calculate the multi-transmitting integrated received power of each receiving site. Step 2-5: Calculate the Coverage of receiving sites : (1) in, It is The coverage of the receiving sites, It is The receiving power of each receiving station, It is The receiving sensitivity of each receiving site is set If the received power at the receiving site is greater than or equal to the receiving site sensitivity, it means that the site can be covered by the transmitting site, and its coverage status is marked as "1". Otherwise, the site cannot be covered, and its coverage status is marked as "0". Step 2-6: Calculate the single-transmitter-multiple-receiver coverage rate of the survey point to the target deployment area; Traversal receiving sites, obtain the coverage of all receiving sites, and calculate the Single-transmitter-multiple-receiver coverage rate of each inspection point in the target deployment area : (2) In the formula For the The single-transmitter-multiple-receiver coverage rate of each inspection point in the target deployment area is For the launch site Coverage conditions at each receiving site; Step 2-7: Take the next grid point of the current grid terrain model as the inspection point and add the inspection point to the inspection set Repeat steps 2-3 to 2-6 until all grid points of the grid terrain model are used as inspection points; Step 2-8: Test set The elements in are sorted in descending order according to the single-transmitter-multiple-receiver coverage obtained by the element as the observation point, and the first 10 elements are included in the initial candidate set in, and from Remove these 10 elements; Step 2-9: Initial candidate set All elements in are considered as inspection points, and the initial candidate set All elements in the image are used as inspection points at the same time, and the Longley-Rice model is used to obtain the multi-transmission receiving power of all receiving sites. The multi-transmission and multi-receiving coverage of all elements as inspection points is calculated based on the multi-transmission receiving power of all receiving sites. That is, the multi-transmission and multi-receiving coverage is obtained when multiple transmitting sites transmit at the same time and multiple receiving sites receive at the same time. ; if , then jump to step 2-8; if , then the initial candidate set All elements in the set are candidate sets , go to step 3; set the target coverage , coverage margin ; Step 3: Clustering and weight reduction based on DBSCAN algorithm; Step 3-1: Based on the results of step 2, apply the DBSCAN algorithm to perform clustering and weight reduction; First, set the cluster radius to 1, and then calculate the candidate set The multiple transmission and multiple reception coverage of all elements in ,like , then randomly increase the cluster radius; calculate the multi-transmit and multi-receive coverage rate , until , the most recent satisfaction The result of classification is the result of the candidate set The elements in are divided into several categories and noise; Step 3-2: Calculate cluster centers; Record candidate set The multi-transmission receiving power of the receiving station in the classification is calculated, and according to the classification results, the element with the highest single-transmission multi-receiver coverage in each class is regarded as the center of the class; for each noise point, each noise point is regarded as a separate class, and the point is regarded as the center of the class; Step 4: Use each cluster center as the deployment location of the wireless sensor network node; Step 5: Coverage visualization; Set the receiving sensitivity range to [-58, -73, -79, -84, -100] dBm, and assign different colors accordingly. According to the range of the multi-transmitter receiving power recorded in step 3-2, the corresponding colors are assigned to form an electromagnetic coverage layer, and the base map is the satellite map of the deployment area. The essence of this method is to draw a contour map of the multi-transmitter receiving power at the receiving site and fill the colors between adjacent layers, such as Figure 3 shown.

[0016] As shown in Table 1, deployment area 1 is a flat area; deployment area 2 is a mountainous area; after deployment, the coverage of the present invention is comparable to that of the traditional heuristic algorithm; but in terms of the number of node deployments, it has a clear advantage. In deployment area 1, the number of nodes deployed by the present invention is 80% lower than that of the traditional heuristic algorithm; in deployment area 2, the number of nodes deployed by the present invention is 95% lower than that of the traditional heuristic algorithm.

[0017] Table 1

Claims

1. A WSN node optimization deployment method based on the greedy DBSCAN algorithm, characterized by: The steps include: Step 1: Build a simulation environment and obtain a grid terrain model of the target deployment area; Establish a grid terrain model based on the digital elevation model (DEM) of the target deployment area; Step 2: Determine a candidate set based on the grid terrain model; the candidate set is a collection of wireless sensor network node deployment locations; All elements in the candidate set meet the following requirements: The multi-transmit multi-receive coverage ratio in the scenario where all elements in the candidate set act as multiple transmitting sites transmitting simultaneously and multiple receiving sites receiving simultaneously meets the target coverage ratio requirement; the multi-transmit multi-receive coverage ratio is calculated based on the multi-transmit receive power of all receiving sites; and the multi-transmit receive power of all receiving sites is calculated based on the Longley-Rice model. Step 3: Based on the candidate set, use the DBSCAN algorithm to cluster the candidate set and obtain the classification center; each classification center is the deployment location of the wireless sensor network node.

2. The WSN node optimization deployment method based on the greedy DBSCAN algorithm according to claim 1, characterized in that: Methods for determining candidate sets include greedy algorithms, particle swarm optimization algorithms, and genetic algorithms.

3. A WSN node optimization deployment method based on the greedy DBSCAN algorithm according to claim 2, characterized in that: The steps to determine the candidate set using the greedy algorithm are: Step 2-1: Set the initial candidate set and survey collection ,in , , that is, the initial candidate set and the inspection set are initialized to empty sets; Step 2-2: Take the starting grid point of the grid terrain model as the inspection point and add the starting grid point to the inspection set middle; Step 2-3: Deploy a transmitting station at the survey point and set the transmitting frequency and transmitting power of the transmitting station; Step 2-4: Set the parameters of the electromagnetic propagation model according to the environmental conditions of the deployment area and evenly distribute the deployment area. The receiving sensitivity of each receiving station is set, and the Longley-Rice model is used to calculate the receiving sensitivity of the first receiving station. Received power at each receiving station The parameters of the electromagnetic propagation model include the receiving sensitivity of the receiving station, the transmitting power of the transmitting station, and the transmitting frequency of the transmitting station. Step 2-5: Calculate the Coverage of receiving sites : (1) in, It is The coverage of the receiving sites, It is The receiving power of each receiving station, It is The receiving sensitivity of each receiving site. If the receiving power at the receiving site is greater than or equal to the receiving site sensitivity, it means that the site is covered by the transmitting site and its coverage is marked as "1". Otherwise, the site is not covered and its coverage is marked as "0". Step 2-6: Calculate the single-transmitter-multiple-receiver coverage rate of the survey point to the target deployment area; Traversal receiving sites, obtain the coverage of all receiving sites, and calculate the Single-transmitter-multiple-receiver coverage rate of each inspection point in the target deployment area : (2) In the formula For the The single-transmitter-multiple-receiver coverage rate of each inspection point in the target deployment area is For the launch site Coverage conditions at each receiving site; Step 2-7: Take the next grid point of the current grid terrain model as the inspection point and add the inspection point to the inspection set Repeat steps 2-3 to 2-6 until all grid points of the grid terrain model are used as inspection points; Step 2-8: Test set The elements in the array are sorted in descending order according to the size of the single-transmitter-multiple-receiver coverage obtained by the element as the inspection point, and the first Elements are included in the initial candidate set in, and from Eliminate this elements; Generate thresholds for candidate sets; Step 2-9: Initial candidate set All elements in are considered as inspection points, and the initial candidate set All elements in the image are used as inspection points at the same time, and the Longley-Rice model is used to obtain the multi-transmission receiving power of all receiving sites. The multi-transmission and multi-receiving coverage of all elements as inspection points is calculated based on the multi-transmission receiving power of all receiving sites. That is, the multi-transmission and multi-receiving coverage is obtained when multiple transmitting sites transmit at the same time and multiple receiving sites receive at the same time. ; if , then jump to step 2-8; if , then the initial candidate set All elements in the set are candidate sets ;in is the target coverage, is the coverage margin.

4. The WSN node optimization deployment method based on the greedy DBSCAN algorithm according to claim 1, characterized in that: Based on the candidate set, the DBSCAN algorithm is used to cluster the candidate set. The steps to obtain the classification center are as follows: Step 3-1: Based on the candidate set, apply the DBSCAN algorithm to perform clustering and weight reduction; First, set the cluster radius to 1 and calculate the candidate set The multiple transmission and multiple reception coverage of all elements in ,like , then randomly increase the cluster radius and calculate the multiple transmission and multiple reception coverage of all elements again , until , the most recent satisfaction The result is taken as the classification result; The classification result is to convert the candidate set The elements in are divided into several categories and noise; Step 3-2: Calculate cluster centers; Record candidate set The multi-transmission receiving power of the receiving site in the classification is calculated, and according to the classification results, the element with the highest single-transmission multi-receiver coverage in each category is used as the center of the classification; for each noise point, each noise point is treated as a separate category, and the point is used as the center of the classification.

5. The WSN node optimization deployment method based on the greedy DBSCAN algorithm according to claim 4 is characterized in that: Set the receive sensitivity range and assign corresponding colors based on the multi-transmit receive power ranges obtained in step 3-2 to obtain a coverage visualization map. The base map of the coverage visualization map is a satellite image of the deployment area.

6. A terminal device comprising a processor, a memory, and a computer program stored in the memory; characterized in that: When the processor executes the computer program, the WSN node optimization deployment method based on the greedy DBSCAN algorithm according to any one of claims 1 to 5 is implemented.

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