Optimal deployment method for rechargeable IoT nodes based on trusted information coverage

Abstract

The present invention discloses a method for optimizing the deployment of rechargeable Internet of Things nodes based on trusted information coverage: a network model is constructed by combining environmental energy distribution and trusted information coverage; an integer linear programming rechargeable Internet of Things node optimization deployment model is constructed by taking environmental correlation into consideration using the constructed network model; and the proposed node optimization deployment model is solved using greedy and approximation methods. In the scenario of the rechargeable Internet of Things, the present invention mines environmental correlation from the perspective of information collaborative perception, combines energy distribution, and uses Kriging interpolation and root mean square error to estimate coverage error, rationally utilizes energy, and effectively reduces deployment costs; adopts greedy thinking and combines binomial methods to solve integer linear programming models in large-scale scenarios, with fast solution speed and high accuracy, high approximation ratio and high efficiency; the method is applicable to a variety of application scenarios, trusted information coverage model, and is applicable to coverage and deployment in different environments, weather, and terrains.

Description

Technical Field

[0001] The present invention belongs to the technical field of Internet of Things, and more specifically, relates to a method for optimizing deployment of rechargeable Internet of Things nodes based on trusted information coverage. Background Art

[0002] The IoT's perception layer contains a rich set of nodes capable of sensing various environments, objects, or facilities. For monitored targets, as the number of sensor samples increases, the accuracy of monitoring increases. For example, when monitoring the location of leaks in oil pipelines, increasing the sampling rate from 600 S / s to 4800 S / s can improve sensing accuracy from 2.3 meters to 0.3 meters. Furthermore, increasing the number of sensor data samples allows for more precise location of accident coordinates. However, the current challenge lies in limited network energy, making it difficult for sensor nodes to collect sufficient samples and transmit them to the fusion center.

[0003] Deployment is a key factor in determining IoT service quality. While proper deployment can improve data accuracy and conserve network resources, frequent battery replacement is impractical due to terrain and environmental constraints, and traditional networks will eventually deplete. To address this issue, a reasonable alternative is to deploy rechargeable nodes with energy harvesting capabilities. Energy harvesting nodes collect external energy through electromagnetic waves, mobile charging vehicles, solar panels, wind power, and other methods and convert it into electricity, significantly extending the network lifespan. However, the amount of energy available from the outside world is limited. IoT nodes may not be able to obtain sufficient energy to provide the required samples for their targets, resulting in a loss of monitoring accuracy.

[0004] Because nodes deployed in different locations may have different charging rates, optimal node placement is necessary. To reduce deployment costs for IoT energy harvesting nodes, a deployment optimization strategy is considered that minimizes the number of IoT nodes while ensuring coverage quality and monitoring accuracy. This optimized deployment effectively utilizes limited network resources and enables IoT nodes to more efficiently and effectively obtain external energy. Summary of the Invention

[0005] This paper investigates how to achieve the desired monitoring accuracy while minimizing sensor deployment costs, taking into account the characteristics of environmental variables, given limited energy availability. Deploying sensor nodes in locations with greater energy availability can compromise coverage quality, indirectly increasing the number of required sensor nodes. To this end, by comprehensively considering factors such as energy distribution, target distribution, sampling accuracy requirements, and network costs, sensor nodes are deployed rationally to achieve energy-neutral network operation, ultimately achieving permanent, trusted information coverage with minimal network cost.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for optimizing deployment of rechargeable IoT nodes based on trusted information coverage is provided, comprising the following steps:

[0007] (1) Constructing a network model based on environmental energy distribution and trusted information coverage includes the following sub-steps:

[0008] (1.1) According to the spatial correlation of the monitoring target, the range and root mean square error thresholds are set, and the sensing field is divided into sub-networks according to the range;

[0009] (1.2) Based on the energy distribution and target point distribution within the given / manually measured sensing area, determine the target point distribution location and determine the area and sub-network to be deployed;

[0010] (2) Using the network model constructed above, an integer linear programming rechargeable IoT node optimization deployment model is constructed considering environmental relevance, including the following sub-steps:

[0011] (2.1) The entire sensing field is divided into multiple sub-areas, each with a different energy distribution. Each sub-network to be deployed contains multiple sub-areas. For each sub-network to be deployed, based on the nature of environmental relevance and energy collaborative sensing, only one node can be placed in each area.

[0012] (2.2) Based on the requirements of trusted information coverage and combined with actual scene parameters, define the target point coverage quality and determine whether the node collaborative sensing data meets the requirements;

[0013] (3) The proposed node optimization deployment model is solved using the greedy and approximate methods, which includes the following sub-steps:

[0014] (3.1) Design and construct a local greedy threshold approximation algorithm based on trusted information coverage to solve the optimal deployment model of rechargeable IoT nodes in (2);

[0015] (3.2) In response to some problems that cannot be solved in (3.1) and some existing defects, a global greedy search algorithm based on trusted information coverage is designed and constructed to solve the optimal deployment model of rechargeable IoT nodes in (2).

[0016] In one embodiment of the present invention, the step (1.1) is specifically as follows: according to the actual situation in the trusted information coverage and the environmental correlation, the CR range is set, and the sensor field F is divided into multiple sub-networks according to the CR, and the size of each sub-network is CR×CR; in each sub-network, for the unsampled spatial points, the ordinary Kriging interpolation function is used to calculate the estimated value of the environmental variable of the reconstruction point, that is, the sensor node s in the reconstruction neighborhood D(k) is used. iThe estimated value of the environmental variable is calculated by taking the weighted average of the measured values; the interpolation weight coefficient λ of the sensor nodes in the neighborhood i satisfy n is the sensor node s in the reconstruction neighborhood D(k) i the number of

[0017] Combined with the ordinary Kriging interpolation function, the root mean square error Φ(k) of the reconstructed point p is calculated. The calculation expression is: in and μ(k) are solved by the Lagrange multiplier method;

[0018] According to the definition of the trusted information coverage model, if Φ(k)>ε, that is, the root mean square error is greater than the set coverage threshold, the subgrid is covered, otherwise it is not covered.

[0019] In one embodiment of the present invention, the and μ(k) are solved by the Lagrange multiplier method, and the expressions are as follows:

[0020]

[0021] In one embodiment of the present invention, the step (1.2) is specifically as follows: the sensing field F is divided into sub-areas, each sub-area is represented by coordinates b(x, y), r(x, y) is the energy acquisition rate of each sub-area, and the sensor nodes s deployed in this area are i All the energy obtained in the area can be converted into electric energy storage nodes; according to the location of the target point set T that needs to be monitored, the subnetwork D(k) containing the target is marked as the subnetwork to be deployed. For each target point, E is the coverage quality, that is, the energy required to collect a certain number of samples.

[0022] In one embodiment of the present invention, the step (2.2) is specifically: As the optimization target, c(x,y) represents the number of sensor nodes placed in each sub-area. Since step (2.1) stipulates that only one node can be placed in each area, c(x,y) = {0,1}. Assume that there are q sensor nodes in the k-th reconstruction neighborhood D(k). According to different combinations of sensor nodes, the sensor nodes can be divided into different groups for joint sensing. Group data, Sq i is the set of sensors jointly sensed by q sensor nodes, Z(S k ,q) is the total combination of q sensor nodes’ joint sensing. The following example is for Z(S k ,q) detailed explanation: If S k ={s1,s2,s3},q=2, then Z(S k,2)={{s1,s2},{s1,s3},{s2,s3}}, Zset k,q Z(S k ,q) sets, representing all combinations;

[0023] In order to meet the coverage requirements of trusted information coverage, the sensor data needs to meet the threshold requirement Φ < ε. The sensor set is called the available sensor set USq i , similarly we can express U(S k ,q) and Uset k,q ;

[0024] When sensor nodes perform cooperative sensing, the data generation rate is equal to the node with the lowest data generation rate;

[0025] R(USq i )=MIN{r(x,y)|c(x,y)∈USq i}

[0026] The sum of all collaborative sensing data must meet the target data requirements, that is, the sum of the charging rates of each group of sensors must meet the target charging rate requirement E. The constraints are:

[0027]

[0028] In one embodiment of the present invention,

[0029] Sq i ={s1,s2,...,s q}

[0030]

[0031]

[0032] In one embodiment of the present invention,

[0033]

[0034] USq i ={Sq i |RMSE(Sq i )<ε}

[0035] U(S k ,q)={Sq1,Sq2,...,Sq n}

[0036] Use k,q ={U(S k ,1),U(S k ,q),...,U(S k,q)}.

[0037] In one embodiment of the present invention, step (3) is specifically as follows: according to the variation range CR in the trusted information coverage, the entire sensing field is divided into (X / CR)×(Y / CR) reconstruction neighborhoods, and the problem is decomposed into sub-problems; the reconstruction area of the target position is searched and marked, and each marked single reconstruction area is regarded as a sub-problem of finding the optimal position of the sensor node.

[0038] In one embodiment of the present invention, the step (3.1) is specifically as follows: α(x, y) is the final position of the sensor in the sensing area, according to step (2.2), for each marked reconstruction neighborhood, Uset k,1 For the set of sensor node locations that meet the threshold requirement when only one sensor node is placed, the node locations that meet the single-node coverage requirement are selected based on the root mean square error (RMSE) and the threshold ε. The selected locations are sorted in descending order according to the size of the sub-region r(x, y). The greedy idea is used to select the top n locations to deploy sensor nodes, and calculate and determine whether the total rate that the node can provide meets the coverage quality E. When the coverage requirement is met, the top n locations are the final locations of the node deployment.

[0039] In one embodiment of the present invention, the step (3.2) is specifically as follows: β(x, y) is the position where the final sensor is placed in the sensing area, and all areas in the subnetwork D(k) are sorted in descending order according to the size of the sub-area charging rate r(x, y); maxR is set based on the greedy idea, and maxR is the maximum total charging rate when n sensor nodes are placed in D(k); node selection is iterated in a greedy manner, and the selected blocks will not enter the next iteration as options. Each iteration is performed on the basis of the previous iteration, and each time the node with the maximum overall benefit is selected, that is, the node is placed at the position corresponding to maxR until the coverage quality E is met.

[0040] In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:

[0041] (1) Low deployment cost. In the context of rechargeable IoT, the present invention explores environmental relevance from the perspective of information collaborative perception, combines energy distribution, uses Kriging interpolation and root mean square error to estimate coverage error, rationally utilizes energy, and effectively reduces deployment costs.

[0042] (2) High approximation ratio and efficiency. The present invention adopts the greedy idea and combines the binomial method to solve the integer linear programming model in large-scale scenarios. The solution speed is fast and the accuracy is high, with high approximation ratio and high efficiency.

[0043] (3) Strong versatility. The Internet of Things involved in the present invention is a universal network that is applicable to a variety of application scenarios. The trusted information coverage model adopted by the present invention is applicable to coverage and deployment in different environments, weather conditions, and terrains. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of a method for optimizing deployment of rechargeable IoT nodes based on trusted information coverage provided by the present invention;

[0045] Figure 2 Schematic diagram of the network structure covered by the trusted information in the present invention;

[0046] Figure 3 is a schematic diagram of the deployment of the method of the present invention in the sensing field area;

[0047] Figure 4 This is an example diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0049] like Figure 1 As shown, the present invention provides a method for optimizing deployment of rechargeable Internet of Things nodes based on trusted information coverage, comprising:

[0050] (1) Constructing a network model based on environmental energy distribution and trusted information coverage includes the following sub-steps:

[0051] (1.1) According to the spatial correlation of the monitoring target, the range and root mean square error thresholds are set, and the sensing field is divided into subnetworks according to the range; the step (1.1) is specifically as follows:

[0052] According to the actual situation of the credible information coverage and the environmental correlation, the CR range is set. According to the CR, the sensor field F is divided into multiple sub-networks, and the size of each sub-network is CR×CR. In each sub-network, for the unsampled spatial points, the ordinary Kriging interpolation function is used to calculate the estimated value of the environmental variable of the reconstruction point, that is, the sensor node s in the reconstruction neighborhood D(k) is used. i The estimated value of the environmental variable is calculated by taking the weighted average of the measured values; the interpolation weight coefficient λ of the sensor nodes in the neighborhood i satisfy n is the sensor node s in the reconstruction neighborhood D(k) i the number of

[0053] Combined with the ordinary Kriging interpolation function, the root mean square error Φ(k) of the reconstructed point p is calculated. The calculation expression is: in and μ(k) are solved by the Lagrange multiplier method, and the expressions are as follows:

[0054]

[0055] According to the definition of the trusted information coverage model, if Φ(k)>ε, that is, the root mean square error is greater than the set coverage threshold, the subgrid is covered, otherwise it is not covered.

[0056] (1.2) According to the given / manually measured energy distribution and target point distribution within the sensing area, the target point distribution position is determined, and the area to be deployed and the sub-network to be deployed are determined; the step (1.2) is specifically as follows:

[0057] The sensing field F is divided into sub-areas, and each sub-area is represented by the coordinate b(x, y). r(x, y) is the energy acquisition rate of each sub-area. The sensor nodes s deployed in this area are i All the energy obtained in the area can be converted into electric energy storage nodes; according to the location of the target point set T that needs to be monitored, the subnetwork D(k) containing the target is marked as the subnetwork to be deployed. For each target point, E is the coverage quality, that is, the energy required to collect a certain number of samples.

[0058] This step creates Figure 2 The network structure covered by the trusted information shown.

[0059] (2) Using the network model constructed above, an integer linear programming rechargeable IoT node optimization deployment model is constructed considering environmental relevance, including the following sub-steps:

[0060] (2.1) The entire sensing field is divided into multiple sub-areas, each with a different energy distribution. Each sub-network to be deployed contains multiple sub-areas. For each sub-network to be deployed, based on the nature of environmental relevance and energy collaborative sensing, only one node can be placed in each area.

[0061] (2.2) Based on the requirements for trusted information coverage and in combination with actual scenario parameters, the target point coverage quality is defined to determine whether the node collaborative sensing data meets the requirements; the step (2.2) is specifically as follows:

[0062] Deployment costs As the optimization target, c(x,y) represents the number of sensor nodes placed in each sub-area. Since step (2.1) stipulates that only one node can be placed in each area, c(x,y) = {0,1}. Assume that there are q sensor nodes in the k-th reconstruction neighborhood D(k). According to different combinations of sensor nodes, the sensor nodes can be divided into different groups for joint sensing. Group data, Sq i is the set of sensors jointly sensed by q sensor nodes, Z(S k ,q) is the total combination of q sensor nodes’ joint sensing. The following example is for Z(S k ,q) detailed explanation: If S k ={s1,s2,s3},q=2, then Z(S k ,2)={{s1,s2},{s1,s3},{s2,s3}}, Zset k,q Z(S k ,q) sets, representing all combinations;

[0063] Sq i ={s1,s2,...,s q}

[0064]

[0065]

[0066] In order to meet the coverage requirements of trusted information coverage, the sensor data needs to meet the threshold requirement Φ < ε. The sensor set is called the available sensor set USq i , similarly we can express U(S k ,q) and Uset k,q ;

[0067]

[0068] USq i ={Sq i |RMSE(Sq i )<ε}

[0069] U(S k ,q)={Sq1,Sq2,...,Sq n}

[0070] Use k,q ={U(S k ,1),U(S k ,q),...,U(S k ,q)}

[0071] When sensor nodes perform cooperative sensing, the data generation rate is equal to the node with the lowest data generation rate;

[0072] R(USq i )=MIN{r(x,y)|c(x,y)∈USq i}

[0073] The sum of all collaborative sensing data must meet the target data requirements, that is, the sum of the charging rates of each group of sensors must meet the target charging rate requirement E. The constraints are:

[0074]

[0075] This step creates Figure 3 The deployment of the sensing field area is shown in the figure. Each square represents the reconstruction neighborhood of the sensing field, the triangle represents the monitored target point, and the gray area represents the target point in the reconstruction area, which is the reconstruction neighborhood involved in the calculation.

[0076] (3) Using greedy and approximate methods to solve the proposed node optimization deployment model;

[0077] Specifically, based on the range CR in the trusted information coverage, the entire sensing field is divided into (X / CR) × (Y / CR) reconstruction neighborhoods, and the problem is decomposed into sub-problems; the reconstruction area of the target location is searched and marked, and each marked single reconstruction area is regarded as a sub-problem of finding the optimal location of the sensor node. It includes the following sub-steps:

[0078] (3.1) Design and construct a local greedy threshold approximation algorithm based on trusted information coverage to solve the optimal deployment model of rechargeable IoT nodes in (2); the specific steps of step (3.1) are as follows:

[0079] α(x,y) is the final position of the sensor in the sensing area. According to step (2.2), for each marked reconstruction neighborhood, Uset k,1 For the set of sensor node locations that meet the threshold requirement when only one sensor node is placed, the node locations that meet the single-node coverage requirement are selected based on the root mean square error (RMSE) and the threshold ε. The selected locations are sorted in descending order according to the size of the sub-region r(x, y). The greedy idea is used to select the top n locations to deploy sensor nodes, and calculate and determine whether the total rate that the node can provide meets the coverage quality E. When the coverage requirement is met, the top n locations are the final locations of the node deployment.

[0080] (3.2) In response to some problems that cannot be solved in (3.1) and some existing defects (such as Figure 4In order to solve the defects in (2), when the coverage quality E is 140, the method (3.1) needs to be deployed at locations b(2, 4), b(4, 2) and b(3, 4), and ultimately requires 3 nodes; obviously, only deploying nodes at b(2, 4) and b(1, 6) can meet the coverage requirements. A global greedy search algorithm based on trusted information coverage is designed and constructed to solve the optimal deployment model of rechargeable IoT nodes in (2). The specific steps (3.2) are as follows:

[0081] β(x,y) is the final sensor placement position within the sensing area. All regions in the subnetwork D(k) are sorted in descending order according to the subregion charging rate r(x,y). MaxR is set based on the greedy idea, where maxR is the maximum total charging rate when n sensor nodes are placed in D(k). Node selection is performed iteratively in a greedy manner. Selected blocks will not be carried forward to the next iteration as alternatives. Each iteration is performed based on the previous iteration, and the node with the maximum overall benefit is selected each time, that is, the node is placed at the position corresponding to maxR, until the coverage quality E is met.

[0082] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing the deployment of rechargeable Internet of Things nodes based on trusted information coverage, characterized in that: The following steps are involved: (1) Constructing a network model based on environmental energy distribution and trusted information coverage, including the following sub-steps: (1.1) According to the spatial correlation of the monitoring target, the range and root mean square error thresholds are set, and the sensing field is divided into subnetworks according to the range; (1.2) Based on the energy distribution and target point distribution within the given manually measured sensing area, determine the target point distribution location and determine the area and sub-network to be deployed; (2) Using the network model constructed above, an integer linear programming rechargeable IoT node optimization deployment model is constructed considering environmental relevance, including the following sub-steps: (2.1) The entire sensing field is divided into multiple sub-areas, each with a different energy distribution. Each sub-network to be deployed contains multiple sub-areas. For each sub-network to be deployed, based on the nature of environmental relevance and energy collaborative sensing, only one node can be placed in each area. (2.2) Based on the requirements for trusted information coverage and combined with actual scenario parameters, define the target point coverage quality and determine whether the node collaborative sensing data meets the requirements; (3) The proposed node optimization deployment model is solved using the greedy and approximate methods, which includes the following sub-steps: (3.1) Design and construct a local greedy threshold approximation algorithm based on trusted information coverage to solve the optimal deployment model of rechargeable IoT nodes in (2); (3.2) In response to some of the problems that cannot be solved in (3.1) and some of the existing defects, a global greedy search algorithm based on trusted information coverage is designed and constructed to solve the optimal deployment model of rechargeable IoT nodes in (2).

2. The method for optimizing deployment of rechargeable Internet of Things nodes based on trusted information coverage according to claim 1, characterized in that: The step (1.1) is specifically as follows: Set according to the actual situation and environmental relevance of the trusted information coverage Variation, according to The sensing field Divide into multiple sub-networks, each sub-network size is In each subnetwork, for the unsampled spatial points, the ordinary Kriging interpolation function is used to calculate the estimated value of the environmental variables of the reconstruction points, that is, the reconstruction neighborhood is used Sensor nodes within The estimated value of the environmental variable is calculated by taking the weighted average of the measured values; the interpolation weight coefficient of the sensor nodes in the neighborhood is satisfy , To rebuild the neighborhood Sensor nodes within the number of Calculate the reconstruction points by combining the ordinary Kriging interpolation function The root mean square error , the calculation expression is: ,in and Solved by Lagrange multiplier method; According to the definition of the trusted information coverage model, if , that is, if the root mean square error is greater than the set coverage threshold, the subnetwork is covered, otherwise it is not covered.

3. The method for optimizing deployment of rechargeable Internet of Things nodes based on trusted information coverage according to claim 2, characterized in that: described and Solved by Lagrange multiplier method, the expression is as follows: 。 4. The method for optimizing deployment of rechargeable Internet of Things nodes based on trusted information coverage according to claim 1 or 2, characterized in that: The step (1.2) is specifically as follows: The sensing field Divide into sub-regions, using coordinates Represents each sub-region, The energy acquisition rate of each sub-area, the sensor nodes deployed in this area All the energy obtained in the area can be converted into electric energy storage nodes; the target point set to be monitored as needed Location, marking the subnetwork containing the target is the sub-network to be deployed, for each target point, is the coverage quality, that is, the energy required to collect a certain number of samples.

5. The method for optimizing deployment of rechargeable Internet of Things nodes based on trusted information coverage according to claim 1 or 2, characterized in that: The step (2.2) is specifically as follows: Deployment costs As the optimization goal, represents the number of sensor nodes placed in each sub-area. Since step (2.1) stipulates that only one node can be placed in each area, , assuming that Reconstructed neighborhood There are Sensor nodes can be divided into different groups according to different combinations of sensor nodes. Group data, For use The set of sensor nodes jointly sensed, for The following example shows all possible combinations of sensor nodes that are jointly sensed. Detailed explanation: If ,but , for The set of , representing all combinations; In order to meet the coverage requirements of trusted information coverage, the data sensed by the sensor needs to meet the threshold requirements The sensor set is called the available sensor set , similarly, it can be expressed as and ; When sensor nodes perform cooperative sensing, the data generation rate is equal to the node with the lowest data generation rate; The sum of all collaborative sensing data must meet the target data requirements, that is, the sum of the charging rates of each group of sensors must meet the target charging rate requirements , whose constraints are: 。 6. The method for optimizing deployment of rechargeable Internet of Things nodes based on trusted information coverage according to claim 5, characterized in that: 。 7. The method for optimizing deployment of rechargeable Internet of Things nodes based on trusted information coverage according to claim 5, characterized in that: 。 8. The method for optimizing deployment of rechargeable Internet of Things nodes based on trusted information coverage according to claim 1 or 2, characterized in that: The step (3) is specifically as follows: According to the variation in credible information coverage , the entire sensing field is divided into Reconstruct the neighborhood and decompose the problem into sub-problems; The reconstruction area of the target location is searched and marked, and each marked single reconstruction area is regarded as a sub-problem of finding the optimal location of the sensor node.

9. The method for optimizing deployment of rechargeable Internet of Things nodes based on trusted information coverage according to claim 1 or 2, characterized in that: The step (3.1) is specifically as follows: is the final sensor placement position in the sensing area. According to step (2.2), for each marked reconstruction neighborhood, The set of sensor node locations that meet the threshold requirement when only one sensor node is placed is calculated based on the root mean square error RMSE and the threshold , select the node location that meets the single node coverage requirements; according to the sub-area The selected locations are sorted in descending order by the size of the node; the top n locations are selected in turn using the greedy idea to deploy sensor nodes, and the total rate that the node can provide is calculated and judged to see whether it meets the coverage quality. ; When the coverage requirements are met, the first n positions are the final positions of node deployment.

10. The method for optimizing deployment of rechargeable Internet of Things nodes based on trusted information coverage according to claim 1 or 2, characterized in that: The step (3.2) is specifically as follows: The final sensor placement position in the sensing area is determined based on the sub-area charging rate. The size of the subnetwork All regions in the system are sorted in descending order; based on the greedy idea , For The maximum total charging rate when n sensor nodes are placed in; Node selection is performed iteratively in a greedy manner. The selected blocks will not enter the next iteration as alternatives. Each iteration is performed on the basis of the previous iteration, and each time the node with the largest overall benefit is selected, that is, Place nodes at corresponding positions until the mass is covered Be satisfied.

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