Wireless soil pressure sensor layout method

Abstract

The invention discloses a wireless soil pressure sensor layout method, and belongs to the field of sensor networks and agricultural internet of things. The problem that sensor arrangement in an existing sensor network does not consider poor accuracy of soil pressure data is solved. The method comprises the following steps: firstly, determining the area of a to-be-monitored region, and carrying out preliminary grid division on the to-be-monitored region by combining the sensing radius of a sensor and obtaining the size and the number of grids to obtain a preliminary grid layout; establishing a target function by minimizing the energy consumption of the grid center point, and obtaining a preliminary layout scheme of sensor nodes in the preliminary grid layout by adopting a genetic algorithm and the target function; arranging a wireless soil pressure sensor by adopting the preliminary arrangement scheme, performing soil pressure detection on a to-be-monitored area, obtaining soil pressure data of the area, and optimizing the preliminary arrangement scheme by utilizing the soil pressure data and adopting a genetic algorithm and the target function. The method is suitable for arrangement of the sensor network.

Description

Technical Field

[0001] The present invention belongs to the fields of sensor networks and agricultural Internet of Things. Background Art

[0002] With the development of agricultural intelligence, soil pressure, as an important parameter affecting crop growth, soil structure and irrigation effect, has gradually become one of the key monitoring indicators in precision agriculture. Most of the existing methods for deploying soil pressure sensors adopt a uniform grid layout, ignoring the spatial differences of soil pressure in different regions. Soil pressure often shows significant local fluctuations, and fixed deployment cannot efficiently cover all areas with pressure changes, resulting in low monitoring accuracy in some areas, creating blind spots in the monitoring of soil pressure changes in agricultural production, and thus affecting the accuracy of production decisions and crop growth monitoring. At the same time, the existing deployment methods of wireless sensor networks also face the problem of excessive energy consumption. In large-scale farmland, sensor nodes need to work stably for a long time, but as the number of nodes increases, the energy consumption of the sensors also increases. High energy consumption has become a bottleneck restricting the long-term and effective operation of wireless sensor networks. Especially when there is a lack of reasonable energy management and optimization mechanisms, the long-term stability and maintenance cost of the system are both affected. In addition, there are also deficiencies in the optimization of data transmission in the existing methods. In traditional wireless sensor networks, the data transmission between sensor nodes is often not carefully designed and optimized, resulting in too long transmission distances between sensor nodes in some areas, thereby increasing communication energy consumption and affecting data transmission efficiency. This not only reduces the real-time performance of the monitoring system but also leads to a waste of energy resources. Summary of the Invention

[0003] The present invention is to solve the problem that the sensor layout in the existing sensor network does not consider the poor accuracy of soil pressure data, and now provides a method for determining the deployment positions of wireless soil pressure sensors.

[0004] A method for deploying wireless soil pressure sensors according to the present invention includes:

[0005] Step 1: First, determine the area of the area to be monitored, combine the sensing radius of the sensor, obtain the grid size and the number of grids, perform a preliminary grid division on the area to be monitored, and obtain a preliminary grid layout;

[0006] Step 2: Establish an objective function to minimize the energy consumption of the grid center points, and use the genetic algorithm and the objective function to obtain a preliminary deployment plan for the sensor nodes in the preliminary grid layout;

[0007] Step 3: Deploy the wireless soil pressure sensors according to the preliminary deployment plan, conduct soil pressure detection on the area to be monitored, obtain the soil pressure data of this area, and use the soil pressure data, the genetic algorithm, and the objective function to optimize the preliminary deployment plan.

[0008] Further, in the present invention, in Step 1, the method for obtaining the grid size and the number of grids is as follows:

[0009] Calculate the total number of grids according to the size of the monitoring area and the sensing radius of the sensor:

[0010]

[0011] where W is the length of the monitoring area, H is the width of the monitoring area, g x = r, g y = r, r is the sensing radius of the sensor, denotes rounding up.

[0012] Further, in the present invention, in Step 2, the objective function is:

[0013]

[0014] E t (i) is the transmission energy consumption of sensor node i, and E r (i) is the reception energy consumption of sensor node i.

[0015] The constraint conditions of the objective function are:

[0016] Condition 1: The sensor node is the center point of the unit grid;

[0017]

[0018] x k represents whether a sensor is deployed at the center of the k-th unit grid. The deployment value is 1, otherwise it is 0; N represents the total number of unit grids;

[0019] Each grid unit is at least monitored and covered by one sensor node;

[0020]

[0021] S k represents all the sensor nodes that can cover grid unit k, S represents all the grid centers where sensors can be deployed, and d(c,k) represents the straight-line distance from sensor node c to grid unit k.

[0022] Condition 2: The distance between nodes is less than the threshold D to ensure the stability of wireless communication;

[0023]

[0024] d(i, c) is the distance between sensor node i and sensor node c;

[0025] Condition 3: The total energy consumption of all nodes cannot exceed the preset maximum energy limit E max ;

[0026] E ≤ E max 。

[0027] Furthermore, in the present invention, in step three, the genetic algorithm and the objective function are used to optimize the preliminary layout method as follows:

[0028] According to the preliminary layout scheme of the sensors, collect the soil pressure data in the detection area, and use the soil pressure data to calculate the pressure change rate ΔY of each grid unit;

[0029] Judge whether the pressure change rate of each grid i is greater than the corresponding threshold ΔY i >Y 阈值 , if so, reduce the area of the corresponding grid unit, otherwise, increase the area of the corresponding grid unit;

[0030] Adopt the genetic algorithm to establish an objective function to minimize the energy consumption of the grid center point, and use the objective function to optimize the preliminary layout scheme of the sensor nodes to obtain the final positions of the sensor nodes.

[0031] Furthermore, in the present invention, the method for calculating the pressure change rate ΔY of each grid unit is as follows:

[0032]

[0033] where Y t is the pressure value at time t, and Y t-1 is the pressure value at time t - 1.

[0034] Furthermore, in the present invention, the specific formula for reducing the area of the corresponding grid unit:

[0035] G i =g x ×g y ×λ

[0036] In the formula, λ is the reduction coefficient.

[0037] Furthermore, in the present invention, the specific formula for increasing the area of the corresponding grid unit is:

[0038] G j =g x ×g y ×μ

[0039] Where μ is the expansion coefficient.

[0040] The present invention realizes the layout of wireless soil pressure sensors through regional grid division, energy consumption optimization algorithm and mobile grid optimization. This method can optimize the layout of sensor nodes while ensuring the monitoring accuracy of soil pressure, reduce the number of unnecessary node deployments, and reduce system energy consumption and deployment costs. By optimizing and adjusting the layout position of sensors, the monitoring coverage rate is improved, the data transmission efficiency is enhanced, and the accuracy and intelligence level of agricultural production are improved. This method has broad application prospects and is particularly suitable for precision agriculture, soil health monitoring and other environmental monitoring fields. Brief Description of the Drawings

[0041] Figure 1 It is a flowchart of the method described in the present invention. Detailed Embodiments

[0042] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0043] Detailed Embodiment 1: Refer to Figure 1 This detailed embodiment specifically describes a method for laying out wireless soil pressure sensors, including:

[0044] Step 1: First, determine the area of the area to be monitored, and combine the sensing radius of the sensor to obtain the grid size and the number of grids, and perform a preliminary grid division on the area to be monitored to obtain a preliminary grid layout;

[0045] Step 2: Establish an objective function to minimize the energy consumption of the grid center point, and use the genetic algorithm and the objective function to obtain a preliminary layout plan of the sensor nodes in the preliminary grid layout;

[0046] Step 3: Use the preliminary layout plan to lay out wireless soil pressure sensors, detect the soil pressure in the area to be monitored, obtain the soil pressure data in this area, and use the genetic algorithm and the objective function with the soil pressure data to optimize the preliminary layout plan.

[0047] Furthermore, in the present invention, in Step 1, the method for obtaining the grid size and the number of grids is:

[0048] Calculate the total number of grids according to the size of the monitoring area and the sensing radius of the sensor:

[0049]

[0050] Among them, W is the length of the monitoring area, H is the width of the monitoring area, and g x = r, g y = r, where r is the sensing radius of the sensor, represents rounding up.

[0051] Furthermore, in the present invention, in step two, the objective function is:

[0052]

[0053] E t (i) is the transmission energy consumption of sensor node i, and E r (i) is the reception energy consumption of sensor node i.

[0054] The constraint conditions of the objective function are:

[0055] Condition 1: The sensor node is the center point of the unit grid;

[0056]

[0057] x k represents whether a sensor is deployed at the center of the k-th unit grid. The deployment value is 1, otherwise it is 0; N represents the total number of unit grids;

[0058] Each grid unit is at least monitored and covered by one sensor node;

[0059]

[0060] S k represents all sensor nodes that can cover grid unit k, S represents all grid centers where sensors can be deployed, and d(c,k) represents the straight-line distance from sensor node c to grid unit k.

[0061] Condition 2: The distance between nodes is less than the threshold D to ensure the stability of wireless communication;

[0062]

[0063] d(i,c) is the distance between sensor node i and sensor node c;

[0064] Condition 3: The total energy consumption of all nodes cannot exceed the preset maximum energy limit E max ;

[0065] E ≤ E max .

[0066] Further, in the present invention, in step three, the genetic algorithm and the objective function are used to optimize the preliminary layout method as follows:

[0067] According to the preliminary layout scheme of the sensors, collect the soil pressure data in the detection area, and use the soil pressure data to calculate the pressure change rate ΔY of each grid unit;

[0068] Judge whether the pressure change rate of each grid i is greater than the corresponding threshold ΔY i >Y 阈值 , if so, reduce the area of the corresponding grid unit, otherwise, increase the area of the corresponding grid unit;

[0069] Adopt the genetic algorithm to establish an objective function to minimize the energy consumption of the grid center point, and use the objective function to optimize the preliminary layout scheme of the sensor nodes to obtain the final positions of the sensor nodes.

[0070] Further, in the present invention, the method for calculating the pressure change rate ΔY of each grid unit is as follows:

[0071]

[0072] where Y t is the pressure value at time t, and Y t-1 is the pressure value at time t-1.

[0073] Further, in the present invention, the specific formula for reducing the area of the corresponding grid unit:

[0074] G i =g x ×g y ×λ

[0075] In the formula, λ is the reduction coefficient.

[0076] Further, in the present invention, the specific formula for increasing the area of the corresponding grid unit is:

[0077] G j =g x ×g y ×μ

[0078] In the formula, μ is the expansion coefficient.

[0079] Specific application process:

[0080] For the method for arranging wireless soil pressure sensors described in this embodiment, the objective of constructing the optimization model is to minimize the energy consumption of the nodes while meeting the monitoring coverage requirements. The proposed algorithm includes regional grid division, energy consumption optimization algorithm, and mobile grid optimization, which can significantly reduce the energy consumption of the sensors while maintaining high-precision monitoring.

[0081] First, construct the objective function to minimize the energy consumption of nodes:

[0082]

[0083] E t (i) is the transmission energy consumption of node i, and E r (i) is the reception energy consumption of node i;

[0084] Constraints:

[0085] (1) Sensor nodes can only be deployed at the center points of unit grids

[0086]

[0087] x k represents whether a sensor is deployed at the center of the k-th unit grid. The deployment value is 1, otherwise it is 0; N represents the total number of unit grids.

[0088] (2) Each grid cell is at least monitored and covered by one sensor node

[0089]

[0090] S k represents all sensor nodes that can cover grid cell k, S represents all grid centers where sensors can be deployed, d(c,k) represents the straight-line distance from sensor node c to grid cell k, and r represents the sensing radius of the sensor.

[0091] (3) The distance between nodes is less than the threshold D to ensure the stability of wireless communication;

[0092]

[0093] d(i,c) is the distance between sensor node i and sensor node c.

[0094] The total energy consumption of all nodes cannot exceed the preset maximum energy limit E max ;

[0095] E ≤ E max

[0096] Then, perform regional grid division, energy consumption optimization, and mobile grid optimization in sequence;

[0097] (1) Regional grid division;

[0098] ① Determine the size of the monitoring area: Select the size W×H of the monitoring area.

[0099] ② Calculate the grid size: Determine the grid size g according to the sensing radius r of the sensorx ×g y , where g x =r, g y =r.

[0100] ③ Calculate the total number of grids: where represents rounding up.

[0101] (2) Energy consumption optimization

[0102] ① Initialize the population: Randomly generate the initial population, and each individual in the population represents a sensor deployment scheme.

[0103] ② Evaluate the fitness: Define the fitness function with the goal of minimizing the energy consumption of nodes, and the function measures the quality of sensor positions.

[0104] ③ Selection, crossover, and mutation. Select individuals with higher fitness to enter the next generation. Generate new individuals by exchanging part of the genes of parental individuals. Randomly flip genes with a lower probability to introduce diversity and prevent the algorithm from falling into local optima.

[0105] ④ Select the optimal solution: After iterating multiple generations, select the scheme with the optimal fitness as the final node deployment scheme.

[0106] (3) Moving grid optimization

[0107] ① Data acquisition: According to the preliminary sensor deployment scheme, obtain soil pressure data and analyze the distribution of soil pressure.

[0108] Collect the soil pressure data uploaded by sensors and analyze its spatio-temporal distribution. Calculate the pressure change rate ΔY of each grid cell.

[0109]

[0110] ② Adjust the grid size: According to the soil pressure data, adjust the position and size of the grid cells. Areas with large pressure changes need to reduce the grid size to increase the sensor deployment density; while areas with small pressure changes can appropriately increase the grid size to reduce the deployment of sensor nodes.

[0111] If the pressure change ΔY in a certain area i i >Y 阈值 , then reduce the grid cell, and the formula is as follows

[0112] G i =g x ×g y ×λ

[0113] In the formula, λ is the reduction coefficient.

[0114] If the pressure change ΔY in a certain area j j <Y 阈值 , then expand the grid cell, and the formula is as follows

[0115] G j =g x ×g y ×μ

[0116] In the formula, μ is the expansion coefficient. Y in the present invention 阈值 is set according to the actual situation.

[0117] ③ Optimize the node positions: After adjusting the grid boundary, enter the energy consumption optimization algorithm again to ensure that at least one node covers the monitoring in each new grid area, while avoiding energy waste between nodes.

[0118] Although the present invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed, as long as they do not depart from the spirit and scope of the present invention as defined by the appended claims. It should be understood that different dependent claims and the features described herein can be combined in a manner different from that described in the original claims. It should also be understood that the features described in connection with a single embodiment can be used in other described embodiments.

Claims

1. A method for deploying a wireless soil pressure sensor, characterized in that: include: Step 1: determine the area of ​​the area to be monitored, obtain the grid size and the number of grids based on the sensing radius of the sensor, perform preliminary grid division on the area to be monitored, and obtain a preliminary grid layout; Step 2: Establish an objective function to minimize the energy consumption of the grid center point, and use a genetic algorithm and the objective function to obtain a preliminary layout plan of the sensor nodes in the preliminary grid layout; Step three: deploy wireless soil pressure sensors using the preliminary deployment plan, perform soil pressure detection on the monitored area, obtain soil pressure data for the area, and use the soil pressure data, genetic algorithm and the objective function to optimize the preliminary deployment plan.

2. A method for deploying a wireless soil pressure sensor according to claim 1, characterized in that: In step 1, the method for obtaining the grid size and the number of grids is: According to the size of the monitoring area and the sensing radius of the sensor, calculate the total number of grids: Where W is the length of the monitoring area, H is the width of the monitoring area, g x = r, g y =r, r is the sensing radius of the sensor, Indicates rounding up.

3. A method for deploying a wireless soil pressure sensor according to claim 2, characterized in that: In step 2, the objective function is: E t (i) is the transmission energy consumption of sensor node i, E r (i) is the receiving energy consumption of sensor node i. The constraints of the objective function are: Condition 1: The sensor node is the center point of the unit grid; x k Indicates whether a sensor is deployed at the center of the k-th unit grid. If yes, the value is 1, otherwise, it is 0. Each grid cell is monitored and covered by at least one sensor node; S k represents all sensor nodes that can cover grid unit k, S represents all grid centers where sensors can be deployed, and d(c,k) represents the straight-line distance from sensor node c to grid unit k. Condition 2: The distance between nodes is less than the threshold D, ensuring the stability of wireless communication; d(i,c) is the distance between sensor node i and sensor node c; Condition 3: The total energy consumption E of all nodes cannot exceed the preset maximum energy limit E max ; E≤E max 。 4. A method for deploying a wireless soil pressure sensor according to claim 3, characterized in that: In step 3, the genetic algorithm and the objective function are used to optimize the preliminary layout method: According to the preliminary layout plan of sensors, soil pressure data of the detection area is collected, and the pressure change rate ΔY of each grid unit is calculated using the soil pressure data; Determine the pressure change rate ΔY of each grid i i Is it greater than the corresponding threshold Y 阈值 , if yes, reduce the corresponding grid unit area, otherwise, expand the corresponding grid unit area; A genetic algorithm is used to establish an objective function to minimize the total energy consumption of the grid center point. The objective function is used to optimize the preliminary layout plan of the sensor nodes to obtain the final position of the sensor nodes.

5. A method for deploying wireless soil pressure sensors according to claim 4, characterized in that: The method for calculating the pressure change rate ΔY of each grid cell is: Among them, Y t is the pressure value at time t, Y t-1 is the pressure value at time t-1.

6. A method for deploying a wireless soil pressure sensor according to claim 5, characterized in that: Specific formula for reducing the corresponding grid unit area: G i =g x ×g y ×λ In the formula, λ is the reduction coefficient.

7. A method for deploying wireless soil pressure sensors according to claim 6, characterized in that: The specific formula for expanding the corresponding grid unit area is: G j =g x ×g y ×μ Where μ is the expansion coefficient.

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