A method for deploying nodes in a three-dimensional wireless sensor network

Abstract

The present invention discloses a method for deploying nodes in a three-dimensional wireless sensor network. First, the measurement area is selected for initialization, and the space where it is located is divided into sub-regions. Secondly, nodes are initialized and deployed in the sub-region space. The initialization process is improved by using a greedy strategy. The Monte Carlo method is used to quickly calculate the uncovered area of the sub-region. The distances between the cluster center nodes and other nodes are calculated within the sub-region, and the initial positions of the nodes are optimized according to the objective function of tabu search. Finally, the overall nodes are optimized according to the Euclidean distance between the cluster center nodes and the occupancy ratio of the cluster center nodes in the sub-region, so as to adjust the working states of the nodes in the overall region and the sub-regions. The present invention comprehensively considers the uncertainty generated by the randomization of the preliminary deployment of nodes, and uses an improved K-means algorithm to quickly provide clustering centers, which can effectively improve the coverage rate of nodes in the three-dimensional wireless sensor network and reduce the problem of waste caused by over-deployment of nodes.

Description

Technical Field:

[0001] The present invention belongs to the technical field of wireless sensor networks, and particularly relates to a three-dimensional wireless sensor network node deployment method based on an improved K-means algorithm with a greedy strategy, which is used to improve the network coverage rate and energy efficiency. Background Art:

[0002] With the increasing complexity of environmental monitoring tasks, people have put forward higher requirements for the comprehensiveness and accuracy of data information. In order to meet these needs, usually, people will use sensors with sensing capabilities as nodes to build a spatial network for data collection and transmission tasks.

[0003] In view of the random deployment and wide distribution characteristics of nodes in wireless sensor networks, the communication distance and signal strength between nodes are susceptible to various factors in practical applications. In order to ensure the reliability and stability of the network and effectively improve the network performance, it is of great significance to study an appropriate coverage control method for improving the monitoring effect of directed sensor networks. For this reason, researchers have proposed a variety of optimization algorithms aiming to improve the efficiency and accuracy of data transmission.

[0004] So far, the research on wireless sensor network node deployment methods has mainly focused on two-dimensional environments. In Acta Physica Sinica, the paper written by scholar Fang Wei discloses "Wireless Sensor Network Coverage Control Deployment Strategy Based on Voronoi Diagram Blind Zones". This strategy first divides the monitoring area into Voronoi diagrams to determine the Thiessen polygons covered by each sensor node. Subsequently, by analyzing the coverage of vertices, the blind zones within the Thiessen polygons are identified, and the geometric center is constructed as the potential target position for sensor node movement, aiming to improve the network coverage. However, the research of this method is limited to two-dimensional environments, and the node sensing model has limitations. In addition, overemphasizing the overall node deployment may lead to a decrease in the measurement accuracy of local node deployment, thereby affecting the coverage rate of the sensor network in the measured area.

[0005] Scholar Liu Cuiping et al. proposed a sensor node deployment strategy based on the firefly swarm optimization algorithm in the paper "Wireless Sensor Node Deployment Based on Firefly Swarm Optimization Algorithm". This strategy first randomly initializes the deployment of sensor nodes, and then determines the movement direction of the nodes according to the calculated movement probability to achieve the effective deployment of the nodes. However, this method still faces problems such as possible excess in the number of node deployments and the existence of coverage blind zones in practical applications.

[0006] In the paper "Research on the Method of Making Patent Maps Based on the k-means Clustering Algorithm", scholar Qiu Honghua and his colleagues proposed an optimization method for the structure of making patent maps based on the k-means clustering algorithm. This method first classifies the bibliographic items of the patent documents involved in the research, including structured and unstructured items, and constructs a semantic network through the K-means clustering algorithm, and then completes the production of the patent map. However, the process of constructing the keyword matrix and performing clustering classification in the initial stage of this method is relatively complex, resulting in the inability to quickly determine the cluster center, increasing the difficulty of achieving the goal.

[0007] Therefore, a three-dimensional wireless sensor network node deployment method that can comprehensively consider the communication efficiency and coverage rate between the local area sensor network nodes and the overall area is needed.

[0008] The information disclosed in this background art section is only intended to increase the understanding of the overall background of the present invention, and should not be regarded as an admission or any form of suggestion that this information constitutes the prior art already known to those of ordinary skill in the art. Summary of the Invention:

[0009] The purpose of the present invention is to provide a three-dimensional wireless sensor network node deployment method. By dividing the measurement area, improving the initialization process by using the greedy strategy, quickly calculating the uncovered area of the sub-region by using the Monte Carlo method, calculating the distance between the cluster center node and other nodes within the sub-region, and optimizing the initial position of the nodes according to the tabu search objective function; using the K-means algorithm to associate the nodes in different sub-regions to determine the optimal solution between and within the regions; in addition, adopting the concept of hierarchical splitting to divide the data set into several smaller clusters, and calculating the evaluation function value through the K-means algorithm to decide whether to merge the clusters, and then performing iterative updates until the number of iterations or the set threshold is met, so as to optimize the three-dimensional wireless sensor network node deployment, thereby overcoming the above-mentioned defects in the prior art.

[0010] To achieve the above object, the present invention provides a three-dimensional wireless sensor network node deployment method, and its steps are as follows:

[0011] S01. On the three-dimensional measurement area, select a cuboid measurement area to establish a three-dimensional rectangular coordinate system, divide the measurement area into three-dimensional grid, and randomly divide it into M sub-regions to obtain a sub-region set A i , where i = 1, 2, 3,..., M;

[0012] S02. Initialize the node deployment for each sub-region, and use the greedy strategy to deploy nodes within each sub-region so that each sub-region can be completely covered by the nodes in that region, obtaining a sub-region node set S i, and optimize the node positions according to the tabu search objective function, where, is a node in sub-region i, and j is the number of nodes in the sub-region; the node includes the communication radius and the three-dimensional coordinates of its own position which are the perpendicular distances from the node to the x-axis, y-axis, and z-axis of the coordinate system respectively;

[0013] S03. Divide it into different clusters according to the sub-region set, determine the cluster center nodes in different clusters through the K-means algorithm, and set the threshold ε for the distance between the remaining nodes and the cluster center nodes; judge the threshold ε for the distance between the remaining nodes and the cluster center nodes and perform iterative optimization according to the results to adjust the node positions so that the selected cluster center node positions are closer to the minimum average position of reaching the nodes in the cluster and reduce the overall distance deviation;

[0014] S04. Calculate the occupancy ratio η N of the cluster center nodes in each sub-region, and use the occupancy ratio of the cluster center nodes to judge whether to adjust the positions of the nodes;

[0015]

[0016] where, ω N is the number of all centroid points sensed by the cluster center node S N and ξ N is the number of nodes not sensed by the cluster center node.

[0017] Preferably, in the technical solution, the greedy strategy process of step S02 is as follows:

[0018] S21. For each sub-region, use the Monte Carlo method to estimate the uncovered positions of the nodes in the current region and generate a large number of random point sets in the sub-region;

[0019] The distance from the random point to the node can be calculated by the Euclidean distance formula:

[0020]

[0021] where dist is the distance from the random point to the deployed node. If dist is less than or equal to the communication radius then the random point is inside the sphere, otherwise, the random point is not inside the sphere;

[0022] S22. Count the number of random points inside the sphere and calculate the proportion η count of it in the total number of random points in the sub-region, and multiply this proportion by the volume of the cuboid of the sub-region to obtain the region coverage rate η cover ;

[0023] ηcover = η count × px area × py area × pz area (3),

[0024] wherein, px area is the value of the three - dimensional measurement area on the x - axis of the coordinate system, py area is the value of the three - dimensional measurement area on the y - axis of the coordinate system, pz area is the value of the three - dimensional measurement area on the z - axis of the coordinate system;

[0025] S23. Select an uncovered position in the sub - region and place a new node at this position to maximize the coverage range of the new node;

[0026] S24. The set S of sub - region nodes i is used as the initial solution of the tabu search. When a node moves by a γ, if the objective function λ of this node is greater than 1 / 2, then update the position of this node to the coordinates after movement; otherwise, do not change. The tabu list records this node and traverses the remaining nodes;

[0027]

[0028] wherein, η new is the coverage rate of the newly covered sub - region space, and η other is the coverage rate of the space covered by other nodes;

[0029]

[0030] wherein, γ is defined as a small step size for the movement of the node position;

[0031] S25. Repeat steps S21 - S24 for iterative update to update the sub - region until all sub - regions are completely covered by the nodes in this region and the initial position is optimized.

[0032] Preferably, in the technical solution, the iterative process of the K - means algorithm in step S03 is as follows:

[0033] S31. Randomly select nodes in the sub - region as the initial cluster center nodes to obtain the initial cluster center node set C = {S 1 , S 2 ,..., S N}, where S N represents the initial cluster center node of the sub - region, and N represents the number of initial cluster center nodes in the sub - region;

[0034] S32. For each node, calculate its distances from each initial cluster center node, and assign it to the nearest cluster;

[0035] Among them, the formula for the average Euclidean distance between a node and each cluster center node is:

[0036]

[0037] Among them, D i is the average Euclidean distance between a node and each cluster center node, dists is the distance function from the remaining nodes except the cluster center nodes to the cluster center nodes after randomization, and Count is the sum of the number of initial cluster center nodes randomly generated in the selected sub-region within the area;

[0038] dists = dist i ×c 1 (7),

[0039] Among them, dist i is the Euclidean distance from a node to the remaining cluster center nodes, and the random factor c 1 is a number generated between [0.9, 1] using the function random;

[0040] S33. Set that if the difference in distances between the cluster center node of this cluster and two remaining nodes except the cluster center nodes is less than the set threshold ε, then delete the two nodes, and add a new node to make the distances to the cluster center node equal and the sum of the distances to the original positions of the two nodes the shortest;

[0041] S34. Repeat the above steps S32 - S33 until the maximum number of iterations is reached or the execution positions of all nodes no longer change.

[0042] Preferably, in the technical solution, the judgment process of the occupancy ratio of the cluster center nodes in step S04 is as follows:

[0043] S41. For each cluster, update the cluster center node of each cluster; let all nodes within each cluster be the cluster center nodes within their respective clusters again, and calculate their occupancy ratios of the cluster center nodes in turn;

[0044] S42. Calculate the occupancy ratio of the cluster center nodes based on all nodes in each sub-region, and take the one with the lowest occupancy ratio of the cluster center nodes in the area as the new cluster center node;

[0045] S43. Repeat the above steps S41 - S42 until the positions of all cluster center nodes no longer change or the maximum number of iterations is reached, and obtain the final set of positions of the cluster center nodes dots.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention divides the area to be measured into individual small intervals, deploys according to the greedy strategy in terms of node initialization, uses the Monte Carlo method to quickly estimate the spatial utilization rate of the initialized nodes and reduces the large impact on the subsequent judgment iteration due to the initialization randomness, so that each sub-region is completely covered. According to the sub-region set, it is divided into different clusters, the center point of each cluster is determined, the trouble of falling into the local optimal solution loop is reduced by introducing a random factor, and a threshold for the distance between the node and the center point is set, and the result is iteratively optimized through the K-means algorithm to adjust the node position. The position of the sensor nodes is adjusted by using the occupancy ratio of the cluster center nodes to optimize the node deployment between sub-regions; it overcomes the problem that the random initial clustering center given by the K-means algorithm during clustering initialization will cause large fluctuations in the clustering result due to the randomness of the node initialization deployment, making the clustering result unstable and easily falling into the local optimum and ignoring the global problem. Moreover, the K-means algorithm improved by the greedy strategy has a low operation time complexity and a fast convergence speed, can reduce the large impact on the subsequent judgment iteration due to the initialization randomness, effectively improve the deployment efficiency and the wireless sensor network coverage rate, and optimize the problem that the randomness of the node initialization deployment in the traditional K-means algorithm has a great impact on the selection of the initial clustering center and the clustering result, ensuring the improvement of the reliability and stability of the network. BRIEF DESCRIPTION OF THE DRAWINGS:

[0048] Figure 1 It is a flowchart of a three-dimensional wireless sensor network node deployment method of the present invention;

[0049] Figure 2 It is a schematic diagram of the overall area division of the present invention;

[0050] Figure 3 It is a schematic diagram of single-region node deployment of the present invention;

[0051] Figure 4 It is a schematic diagram of node judgment division between different regions of the present invention. DETAILED DESCRIPTION OF THE INVENTION:

[0052] The following describes the specific implementation manners of the present invention in detail, but it should be understood that the protection scope of the present invention is not limited by the specific implementation manners.

[0053] Unless otherwise clearly stated, throughout the specification and claims, the term "comprising" or its variations such as "comprises" or "including" etc. will be understood to include the stated elements or components, without excluding other elements or other components.

[0054] As Figure 1 shown, a three-dimensional wireless sensor network node deployment method, the steps of which are:

[0055] S01. On the three-dimensional measurement area, select a cuboid measurement area to establish a three-dimensional rectangular coordinate system, divide the measurement area into three-dimensional grids, and randomly divide it into M sub-areas to obtain the sub-area set A i , where i = 1, 2, 3,..., M;

[0056] For example, as Figure 2 shown, in the three-dimensional measurement area, select a cuboid as the measurement area, and divide the space therein to obtain 320 small cuboid sub-areas and 100 node numbers. The selected node communication radius is 50;

[0057] S02. Initialize the node deployment for each sub-area, and use the greedy strategy to deploy nodes within each sub-area so that each sub-area can be completely covered by the nodes in that area, obtaining the sub-area node set S i , and optimize the node positions according to the tabu search objective function, where is a node in sub-area i, and j is the number of nodes in the sub-area; the node includes the communication radius and the three-dimensional coordinates of its own position are the perpendicular distances from this node to the x-axis, y-axis, and z-axis of the coordinate system respectively;

[0058] S21. For each sub-area, use the Monte Carlo method to estimate the positions not covered by the nodes in the current area, and generate a large number of random point sets within the sub-area;

[0059] The distance from the random point to the node can be calculated by the Euclidean distance formula:

[0060]

[0061] where dist is the distance from the random point to the deployed node. If dist is less than or equal to the communication radius then this random point is inside the sphere, otherwise, this random point is not inside the sphere;

[0062] S22. Count the number of random points inside the sphere and calculate the proportion η of the total number of random points in the sub-area count , and multiply the proportion of the random points inside the sphere with the communication radius by the volume of the cuboid of the sub-area to obtain the area coverage rate η cover ;

[0063] η cover = η count × px area × py area × pz area (3),

[0064] Among them, px area is the value of the three-dimensional measurement area on the x-axis of the coordinate system, py area is the value of the three-dimensional measurement area on the y-axis of the coordinate system, pz area is the value of the three-dimensional measurement area on the z-axis of the coordinate system;

[0065] S23. Select an uncovered position in the sub-region and place a new node at this position to maximize the coverage range of the new node;

[0066] As Figure 3 shown, the number of random points inside the sphere in sub-region i is 2,100, and the total number of random points generated in this sub-region is 21,000. At this time, η cover is 2,100 / 21,000, the coverage rate is 0.1, and the steps need to be repeated to continue generating new nodes;

[0067] S24. The sub-region node set S i is used as the initial solution of the tabu search. When the node moves by a γ, if the objective function λ of this node is greater than 1 / 2, then update the position of this node to the coordinates after movement; otherwise, do not change. The tabu list records this node and traverses the remaining nodes;

[0068]

[0069] Among them, η new is the coverage rate of the newly covered sub-region space, and η other is the coverage rate of covering the covered space of other nodes;

[0070]

[0071] Among them, γ is defined as a small step size for the movement of the node position;

[0072] S25. Repeat steps S21 - S24 for iterative update, update the sub-region until all sub-regions are completely covered by the nodes in this region and optimize the initial position;

[0073] S03. Divide it into different clusters according to the sub-region set, determine the cluster center nodes in different clusters through the K-means algorithm, and set the threshold ε for the distance between the remaining nodes and the cluster center nodes; judge the threshold ε for the distance between the remaining nodes and the cluster center nodes and perform iterative optimization according to the result, adjust the node position to make the selected cluster center node position closer to the minimum average position of the nodes in the cluster, and reduce the overall distance deviation;

[0074] S31. Randomly select nodes in the sub-region as the initial cluster center nodes to obtain the initial cluster center node set C = {S1 , S 2 ,..., S N}}, where S N represents the initial cluster center node of the sub-region, and N represents the number of initial cluster center nodes of the sub-region;

[0075] S32. For each node, calculate its distance from each initial cluster center node and assign it to the nearest cluster;

[0076] Among them, the formula for the average Euclidean distance between a node and each cluster center node is:

[0077]

[0078] Among them, D i is the average Euclidean distance between a node and each cluster center node, dists is the distance function from the remaining nodes except the cluster center nodes to the cluster center nodes after randomization, and Count is the sum of the number of initial cluster center nodes randomly generated in the selected sub-region within the area;

[0079] dists = dist i × c 1 (7),

[0080] Among them, dist i is the Euclidean distance from a node to the remaining cluster center nodes, and the random factor c 1 is a number generated in the range of [0.9, 1] using the function random;

[0081] S33. Set that if the difference in distances between the cluster center node of this cluster and two other nodes except the cluster center nodes is less than the set threshold ε, then delete the two nodes and add a new node to make the distance to the cluster center node equal and the sum of the distances to the original positions of the two nodes the shortest;

[0082] As Figure 4 shown, given the position of the cluster center node (3, 6, 7), set the threshold to 1 unit length, and there are two node positions (6, 11, 11) and (8, 9, 11). It can be obtained that the difference in distances between the two other points is 0, which is less than the set threshold, and a new node is generated for replacement.

[0083] S34. Repeat the above steps S32 - S33 until the maximum number of iterations is reached or the positions of all nodes no longer change;

[0084] S04. Calculate the occupancy ratio η N of the cluster center nodes in each sub-region, and use the occupancy ratio of the cluster center nodes to determine whether to adjust the positions of the nodes;

[0085]

[0086] where ω N is the number of all centroid points sensed by the cluster center node S N and ξ N is the number of nodes not sensed by the cluster center node.

[0087] S41. For each cluster, update the cluster center node of each cluster; let all nodes within each cluster be the cluster center node within its own cluster again, and calculate the occupancy ratio of its cluster center node in turn;

[0088] S42. Calculate the occupancy ratio of its cluster center node according to all nodes within each sub-region, and take the one with the lowest occupancy ratio of the cluster center node within the region as the new cluster center node;

[0089] S43. Repeat the above steps S41 - S42 until the positions of all cluster center nodes no longer change or reach the maximum number of iterations, and obtain the final set of cluster center node positions dots.

[0090] The foregoing description of specific exemplary embodiments of the present invention is for purposes of illustration and exemplification. These descriptions are not intended to limit the invention to the precise forms disclosed, and obviously, many changes and variations are possible in light of the above teachings. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the present invention and its practical applications, so that those skilled in the art can implement and utilize various different exemplary embodiments of the present invention, as well as various different selections and changes. The scope of the present invention is intended to be defined by the claims and their equivalents.

Claims

1. A three-dimensional wireless sensor network node deployment method, the steps of which are: S01. In the three-dimensional measurement area, a rectangular measurement area is selected to establish a three-dimensional rectangular coordinate system, and the measurement area is divided into three-dimensional grids, and randomly divided into M sub-areas to obtain a sub-area set A. i , where i = 1, 2, 3, ..., M; S02. Initialize node deployment in each sub-area, and use a greedy strategy to deploy nodes in each sub-area so that each sub-area can be fully covered by the nodes in the area, and obtain the sub-area node set S i , and optimize the node position according to the taboo search objective function, where is a node in sub-region i, j is the number of nodes in the sub-region; node Including communication radius and the three-dimensional coordinates of its own position are the vertical distances from the node to the x-axis, y-axis, and z-axis of the coordinate system respectively; S03, dividing the sub-region sets into different clusters, determining the cluster center nodes in different clusters by using the K-means algorithm, and setting a threshold ε of the distance between the remaining nodes and the cluster center node; judging the threshold ε of the distance between the remaining nodes and the cluster center node and iteratively optimizing according to the result, adjusting the node position, so that the position of the selected cluster center node is closer to the minimum average position of the nodes in the cluster, and reducing the overall distance deviation; S04. Calculate the cluster center node occupancy ratio η in each sub-region N ,The cluster center node occupancy ratio is used to determine whether to adjust the node position; Among them, ω N is the cluster center node S N The number of all centroids perceived, ξ N is the number of nodes that are not sensed by the cluster center node.

2. A three-dimensional wireless sensor network node deployment method according to claim 1, characterized in that: The greedy strategy process of step S02 is: S21. For each sub-region, the Monte Carlo method is used to estimate the uncovered locations of the nodes in the current region, and a large number of random point sets are generated in the sub-region; The distance from a random point to a node can be calculated using the Euclidean distance formula: Where dist is the distance from the random point to the deployed node. If dist is less than or equal to the communication radius If , the random point is inside the ball, otherwise, the random point is not inside the ball; S22. Count the number of random points in the ball and calculate the proportion η of the total number of random points in the sub-area. count , and multiply the ratio by the volume of the sub-region cuboid to obtain the region coverage η cover ; or cover =the count ×px area ×py area ×pz area (3), Among them, px area is the value of the three-dimensional measurement area on the x-axis of the coordinate system, py area is the value of the three-dimensional measurement area on the y-axis of the coordinate system, pz area is the value of the three-dimensional measurement area on the z-axis of the coordinate system; S23, selecting an uncovered position in the sub-area, and placing a new node at the position so that the coverage of the new node is maximized; S24, sub-region node set S i As the initial solution of the tabu search, when the node When moving a γ, if the node satisfies the objective function λ greater than 1 / 2, the node position is updated to the coordinates after the move, otherwise, it is unchanged, the taboo table records the node, and traverses the remaining nodes; Among them, η new is the coverage rate of the new covered sub-region space, η other is the coverage ratio of the space covered by other nodes; Among them, γ is defined as the node A small step in position movement; S25. Repeat steps S21-S24 to perform iterative update, update the sub-regions, until all sub-regions are completely covered by the nodes of the region and the initial position is optimized.

3. A three-dimensional wireless sensor network node deployment method according to claim 1, characterized in that: The iterative process of the K-means algorithm in step S03 is: S31, the sub-region selects random nodes in the region as the initial cluster center nodes, and obtains the initial cluster center node set C = {S1, S2, ..., S N }, where S N represents the initial cluster center node of the sub-region, and N represents the number of initial cluster center nodes of the sub-region; S32, for each node, calculate its distance from the central node of each initial cluster, and assign it to the nearest cluster; Among them, the average Euclidean distance formula between the node and each cluster center node is: Among them, D i is the average Euclidean distance between the node and each cluster center node, dists is the distance function from the rest of the nodes to the cluster center node after randomization, and Count is the sum of the initial number of cluster center nodes in the randomly generated sub-regions in the region; dists=dist i ×c1(7), Among them, dist i is the Euclidean distance from the node to the center nodes of other clusters, and the random factor c1 is a number between [0.9, 1] generated by the function random; S33, if the difference between the distance between the cluster center node of the cluster and two nodes other than the cluster center node is less than the set threshold ε, the two nodes are deleted, and a new node is added so that the distance to the cluster center node is equal and the sum of the distances to the original positions of the two nodes is the shortest; S34. Repeat the above steps S32-S33 until the maximum number of iterations is reached or the execution positions of all nodes do not change.

4. A three-dimensional wireless sensor network node deployment method according to claim 1, characterized in that: The process of determining the cluster center node occupancy ratio in step S04 is as follows: S41, for each cluster, update the cluster center node of each cluster; let all nodes in each cluster be the cluster center nodes in their clusters again, and calculate their cluster center node occupancy ratios in turn; S42, calculating the cluster center node occupancy ratio of all nodes in each sub-region, and taking the cluster center node with the lowest occupancy ratio in the region as the new cluster center node; S43, repeating the above steps S41-S42 until the positions of all cluster center nodes no longer change or the maximum number of iterations is reached, and obtaining the final cluster center node position set dots.

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