A software-defined warehouse bottom sensor network concave-convex surface coverage strategy method

Abstract

The present application relates to a kind of software-defined granary bottom sensing network concave-convex surface coverage strategy method.The present application includes the following steps: first, establish granary bottom single node coverage perception mathematical model, start with the vertex of curved surface, use the inclined plane of multiple small change amount of discrete slope to approximate simulation concave-convex surface, the coverage area of node and inclined plane is calculated by the position coordinate of node, and then the coverage of convex-concave surface is calculated;Second, design granary bottom network irregular curved surface non-redundant coverage scheme;Finally, realize software-defined granary sensing network multi-objective coverage optimization strategy.The present application proposes a general coverage rate calculation method for different concave-convex surfaces, so as to accurately calculate the coverage rate of concave-convex surface.Under the condition of limited energy of sensor node and limited computing power, the present application proposes a sensing network coverage strategy deployed on the irregular curved surface of granary bottom, and improves the coverage rate of granary bottom network by reasonably deploying sensor nodes.

Description

Technical Field

[0001] This invention relates to a software-defined coverage strategy method for the concave and convex surfaces of a grain silo bottom sensor network, belonging to the field of sensor technology. Background Technology

[0002] With the advancement of agricultural technology in my country, the application of Wireless Sensor Networks (WSN) technology in agriculture has become increasingly widespread. Currently, the development trend in grain storage is to place multiple periodically sampling sensor nodes within a defined space, enabling them to operate for extended periods and complete more tasks within a given timeframe. Software-defined networking (SDN) is currently driving significant changes in the internet field. Based on their characteristics, SDN grain silo sensor networks can be categorized into three types: top coverage, middle coverage, and bottom coverage. Middle coverage involves three-dimensional space, while top and bottom coverage are two-dimensional planar coverage problems. Middle and top coverage problems are relatively easier than bottom coverage. For example, in the two two-dimensional planar coverage problems, the unevenness of the top plane of the grain silo is controllable; however, due to uncontrollable factors such as terrain and wear, the bottom of the grain silo is often an uneven, curved surface, which increases the difficulty of deploying nodes in the bottom sensor network. The coverage problem of a wireless sensor network at the bottom of a grain silo can be viewed as a function of node location and node communication radius. Given that the sensing radius of each sensor node is known, the node's location information determines the network coverage. Node location and deployment are closely related; therefore, research on wireless sensor network coverage is inseparable from research on node deployment and scheduling. Coverage is a fundamental problem in sensor networks. Node coverage addresses this by optimizing the allocation of network resources through methods such as sensor node deployment and routing path selection, thereby improving the quality of services such as sensing, monitoring, communication, and surveillance.

[0003] Therefore, this invention proposes a software-defined coverage strategy for the concave and convex surfaces of the bottom sensor network of a grain silo. Summary of the Invention

[0004] This invention provides a software-defined coverage strategy method for the concave and convex surfaces of a grain silo bottom sensor network. This method aims to overcome the complex environment of a grain silo bottom by simulating convex and concave surfaces using multiple discrete sloped planes, transforming the continuous problem of calculating the coverage rate of the concave and convex surface into a discrete problem. Reasonable and effective deployment of nodes in the network reduces the number of sensor nodes, saves costs, improves network coverage, enhances network operability, and improves network performance.

[0005] The technical solution of the present invention is: a software-defined coverage strategy method for the concave and convex surfaces of a grain silo bottom sensor network, the method comprising the following steps:

[0006] Step 1: First, construct a coverage perception mathematical model for a single node at the bottom of the grain warehouse. Starting from the vertex of the surface, measure and estimate the slope of each small change. Then, use a plane with discrete slopes of multiple small changes to approximate the concave and convex surfaces. Then, combine the position coordinates of the nodes to calculate the theoretical coverage area of ​​the corresponding nodes on the slope, and then calculate the coverage rate of the convex and concave surfaces of each node.

[0007] Step 2: Design an irregular curved surface non-redundant coverage scheme for the grain silo bottom network. Step 2 includes the following: Based on the coverage of each node on the grain silo bottom, considering the detection range of the nodes, the distance between nodes, and the dynamic and static states of the nodes, design an irregular curved surface non-redundant optimized coverage scheme for the grain silo bottom network. This will initially improve the coverage rate of the grain silo bottom network, enabling the nodes in the network to meet the basic requirements of connectivity and sensing coverage area, ensuring network services, and providing a foundation for realizing a software-defined multi-target coverage optimization strategy for the grain silo bottom sensor network.

[0008] Step 3: Finally, implement the software-defined multi-target coverage optimization strategy for the grain silo sensor network. Step 3 includes the following: Finally, combining the resilience of nodes and the distribution factors of robust nodes under network damage conditions, design a network repair mechanism and a scheme to improve resilience when damaged. Through precise coverage of important targets and coverage of most targets, achieve precise coverage of all targets on the uneven surface of the entire grain silo bottom. While improving the accuracy in terms of coverage, optimize the implementation cost, and thus realize the software-defined multi-target coverage optimization strategy for the grain silo sensor network.

[0009] As a further aspect of the present invention, the specific steps of Step 1 are as follows:

[0010] Step 1.1: Since the bottom of the granary is a curved surface, its coverage area is abstracted from two dimensions to three dimensions. A height z is added to the two-dimensional array o(x,y), thus becoming o(x,y,z). Starting from the vertex of the surface, a convex-concave surface is approximated by a plane with discrete slopes of multiple small variations. Any one of these planes is taken as the two-dimensional plane B of the target detection region A. The plane is divided into multiple discrete slopes. Assuming the equation of each discrete slope with small variations is αx + βy + γ = 0, β ≠ 0, then the slope of each small variation under two-dimensional conditions is: (β≠0); α, β, and γ are all coefficients of the discrete slope equation; k is the slope after discretization;

[0011] Step 1.2: Deploy N sensor nodes in the target detection area A, with a node sensing radius of r. Then, the sensor node set is represented as node. i , i∈{1,2,...,n};

[0012] Step 1.3, Spatial Node n i If the coordinates of the target point are node = (x, y, z), then the distance between the target point and the sensor node is:

[0013]

[0014] Step 1.4, the event where a point within the target area is covered by a sensor node is defined as c. i Then the probability Pc of this event occurring is... i That is, the point (x, y, z) is detected by the sensor node. i The probability of being covered:

[0015]

[0016] Step 1.5: The coverage rate F of the target detection area A is the ratio of the coverage area of ​​the sensor node set to the area of ​​the monitoring area.

[0017] The angle θ between the node location and the bottom plane of the grain silo is:

[0018] θ∈(0,π) and β≠0

[0019] In summary, the area S of the inclined plane op The area S of the concave-convex surface S is approximately equal to the area of ​​the concave-convex surface S. S ;

[0020]

[0021] Because point o(x,y,z) is detected by sensor node node i The events covered are in a 0-1 model, Pc i When the value is 1, the node is considered to be i The coverage area is π*r 2 Then the coverage F of the target detection region A is:

[0022]

[0023] Step 1.6: Calculate the maximum coverage rate with a limited number of sensor nodes:

[0024]

[0025] Where, x i Represents the coordinates of the sensor node on the x-axis, y-axis iThe y-coordinate of the sensor node is represented by Th, which represents the threshold number of sensor nodes. nodei This refers to the coverage area of ​​a single node.

[0026] The beneficial effects of this invention are:

[0027] 1. This invention simulates convex and concave planes using multiple discrete slopes, transforming the problem of calculating the coverage of continuous concave and convex surfaces into a discrete problem, thereby reducing the amount of computation, lowering the time complexity, and accelerating the calculation speed.

[0028] 2. The design should meet the requirements of the nodes in the network to reach the sensing coverage area, satisfy the condition of maximizing the coverage area, reduce the number of sensor nodes in the network, extend the life cycle of the software-defined grain warehouse sensor network, and thus improve its quality of service (QoS). Attached Figure Description

[0029] Figure 1 This is a two-dimensional cross-section in this invention; a schematic diagram of the coverage area of ​​a single node;

[0030] Figure 2 This is a flowchart of the present invention;

[0031] Figure 3 This is a schematic diagram of the coverage area of ​​a single node in this invention;

[0032] Figure 4 This is a schematic diagram of the node sensing process in this invention;

[0033] Figure 5 This is the initial node distribution diagram in this invention;

[0034] Figure 6 This is the optimized node distribution diagram in this invention. Detailed Implementation

[0035] Example 1: As Figures 1-6 As shown, a software-defined coverage strategy method for the uneven surface of a grain silo bottom sensor network includes the following steps:

[0036] Step 1: First, construct a coverage perception mathematical model for a single node at the bottom of the grain warehouse. Starting from the vertex of the surface, measure and estimate the slope of each small change. Then, use a plane with discrete slopes of multiple small changes to approximate the concave and convex surfaces. Then, combine the position coordinates of the nodes to calculate the theoretical coverage area of ​​the corresponding nodes on the slope, and then calculate the coverage rate of the convex and concave surfaces of each node.

[0037] In order to formalize the problem to be solved, symbolic representations are first defined based on network parameters and known conditions, as shown in Table 1 below.

[0038] Table 1. Definitions of constants in the design of the grain silo bottom cover sensor network.

[0039]

[0040] Based on the design principles of the coverage strategy, the symbolic variables shown in Table 2 below need to be defined;

[0041] Table 1. Variable Definitions in the Design of the Grain Silo Bottom Cover Sensor Network

[0042]

[0043]

[0044] The specific steps of Step 1 are as follows:

[0045] Step 1.1: Since the bottom of the granary is a curved surface, its covering surface is abstracted from two dimensions to three dimensions; a height z is added to the two-dimensional array o(x,y), thus becoming o(x,y,z); starting from the vertex of the curved surface, the concave and convex surfaces are approximated by inclined planes with discrete slopes of multiple small variations, taking a two-dimensional cross-section as an example. Figure 1 As shown; take any inclined plane as the two-dimensional plane B of the target detection region A, and divide the inclined plane into multiple discrete inclined lines. Assume that the equation of each discrete inclined line with small changes is αx + βy + γ = 0, β ≠ 0, then the slope of each small change under two-dimensional conditions is:

[0046] α, β, and γ are all coefficients of the discrete slope equation; k is the slope after discretization.

[0047] Step 1.2: Deploy N sensor nodes in the target detection area A, with a node sensing radius of r. Then, the sensor node set is represented as node. i , i∈{1,2,...,n}; First Figure 4 This demonstrates the perception process of nodes in space, with the coverage area of ​​a single node as shown. Figure 3 As shown;

[0048] For any sensor node, there is node i ={x i y i , z i , r} represents the node (x i ,y i ,z i A monitoring sphere with center r and radius r, where r > 0;

[0049] Step 1.3, Spatial Node n iIf the coordinates of the target point are node = (x, y, z), then the distance between the target point and the sensor node is:

[0050]

[0051] Step 1.4, the event where a point within the target area is covered by a sensor node is defined as c. i Then the probability Pc of this event occurring is... i That is, the point (x, y, z) is detected by the sensor node. i The probability of being covered:

[0052]

[0053] r = 2 is the sensing radius of the node;

[0054] Step 1.5: The coverage rate F of the target detection area A is the ratio of the coverage area of ​​the sensor node set to the area of ​​the monitoring area.

[0055] The angle θ between the node location and the bottom plane of the grain silo is:

[0056]

[0057] In summary, the area S of the inclined plane op The area S of the concave-convex surface S is approximately equal to the area of ​​the concave-convex surface S. S ;

[0058]

[0059] Because point o(x,y,z) is detected by sensor node node i The events covered are in a 0-1 model and i is sufficiently small, Pc i When the value is 1, the node is considered to be i The coverage area is π*r 2 Then the coverage F of the target detection region A is:

[0060]

[0061] S nodei =*r 2 (7)

[0062] Step 1.6: Calculate the maximum coverage rate with a limited number of sensor nodes:

[0063]

[0064] Where, x i Represents the coordinates of the sensor node on the x-axis, y-axis i The y-coordinate of the sensor node is represented by Th, which represents the threshold number of sensor nodes.nodei The coverage area of ​​a single node. In Step 1.6, the maximum coverage rate is the objective function, which requires maximizing the coverage area of ​​all sensing nodes while keeping the number of nodes less than a pre-set threshold.

[0065] Step 2: Based on the coverage of each node to the bottom of the grain silo, and considering factors such as the detection range of the nodes, the distance between nodes, and the dynamic and static states of the nodes, design an irregular curved surface non-redundant optimized coverage scheme for the grain silo bottom network. This will initially improve the coverage of the grain silo bottom network, enabling the nodes in the network to meet the basic requirements of connectivity and sensing coverage area, ensuring network services, and providing a foundation for realizing the software-defined multi-target coverage optimization strategy for the grain silo bottom sensor network.

[0066] Step 3: Finally, considering the resilience of nodes and the distribution of robust nodes under network damage conditions, design a network repair mechanism and a solution to improve resilience when damaged. By accurately covering important targets and covering most targets, achieve accurate coverage of all targets on the uneven surface of the entire grain silo bottom. Improve accuracy in terms of coverage while optimizing implementation costs, thereby realizing a software-defined grain silo sensor network multi-target coverage optimization strategy.

[0067] Figure 5 , 6 The distribution of the initialized nodes and the optimized nodes in three-dimensional space (i.e., the uneven bottom of the grain silo) is shown respectively.

[0068] In Step 1.1, the slope for estimating small changes in the concave-convex surface is represented by constraint equation (1). In Step 1.3, the node spacing and distance from the target in three-dimensional space are represented by constraint equation (2) to estimate whether there is coverage and overlapping areas. In Step 1.4, the judgment on whether all target nodes are covered and detected is represented by constraint equation (3). In Step 1.5, the angle between the node position and the concave-convex surface of the grain silo bottom used to calculate the area coverage rate is specified by constraint equation (4), that is, the angle between the xoy plane. From the above parameters, the total area of ​​the inclined plane that can be covered and detected by the concave-convex surface of the grain silo bottom as specified by constraint equation (5) can be calculated. When the change is extremely small, it is approximately equal to the area of ​​the concave-convex surface of the grain silo bottom. Single node i Coverage detection area S nodei As specified by constraint (7), the total area of ​​the grid coverage detection on the concave and convex surface of the grain silo bottom when all nodes reach the maximum coverage is specified by constraint (8).

[0069] This invention simulates convex and concave surfaces using multiple discrete sloped planes, transforming the problem of calculating the coverage of continuous concave and convex surfaces into a discrete problem. In a grain silo bottom sensor network, the area is largely an irregular, concave-convex plane. Starting from the vertices of the surface, multiple discrete sloped planes with small variations are used to approximate the concave and convex surfaces. The coverage area of ​​the nodes relative to the slopes is calculated using the node's position coordinates, thus determining the coverage rate of the convex and concave surfaces. The coverage area of ​​a node in the deployment area is not only related to the node's sensing radius but also influenced by the node's position and the curvature of the concave and convex surfaces. Deploying both static and dynamic sensor nodes ensures that the nodes in the network meet the requirements for connectivity and sensing coverage, guaranteeing the network's continuous and reliable service quality. This provides a new approach and theoretical basis for the coverage of grain silo environmental monitoring networks.

[0070] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for coverage strategy of software-defined warehouse floor sensor network concave-convex surface, characterized in that: The method comprises the following steps: Step 1, first construct a single node of the grain bottom covering perception mathematical model, starting from the vertex of the curved surface, measuring and estimating the slope of each small change, then using the discrete slope of multiple small changes to approximate the inclined plane to simulate the concave and convex surface, then combining the position coordinates of the nodes, calculating the corresponding node's theoretical coverage area of the inclined surface, and further calculating the coverage rate of each node convex and concave surface; Step 2, design a non-redundant coverage scheme for the irregular curved surface of the grain bottom network; Step 3, finally realize the multi-objective coverage optimization strategy of software-defined grain bottom sensor network; The specific steps of Step 1 are as follows: Step1.1, since the bottom of the granary is curved, the covering surface is abstracted from two-dimensional to three-dimensional; on the basis of two-dimensional array o(x, y), the height z is increased, thereby becoming o(x, y, z); starting from the vertex of the curved surface, the concave and convex surfaces are simulated by using a plurality of discrete slope change amounts, a two-dimensional plane B of the target detection area A is taken out as a target detection area A, the inclined plane is divided into a plurality of discrete inclined lines, and it is assumed that the equation of each small change amount of the discrete inclined line is , , then the slope of each small change amount under the two-dimensional condition is: , ; , , are the coefficients of the discrete inclined line equation; is the discrete slope. Step 1.2, in the target detection area A, the number of sensor nodes is N, and the node sensing radius is r, then the sensor node is represented as node i , i∈{1,2,...,n}. Step 1.3, target point P The coordinates of the target point are node = (x, y, z). The distance between the target point and the sensor node is: ； Step 1.4, the event that a point in the target region is covered by a sensor node is defined as c i The probability of this event occurring is Pc i The probability that a point (x, y, z) is covered by a sensor node node i is ； Step 1.5, the coverage rate F of the target detection area A is the ratio of the coverage area of the sensor node set to the area of the monitoring area; The angle between the location of the node and the bottom plane of the granary Is: , and ; In summary, the inclined plane area S op Approximately the area S of the concave-convex surface S S ; ； Because the point o(x, y, z) is covered by the sensor node node i The event is 0-1 model, Pc i 1 is considered node i The coverage area is The coverage rate F of the target detection area A is: ； Step 1.6, calculate the maximum coverage rate under the condition of limited sensor nodes: ； wherein, x i denotes the coordinate of the sensor node on the x-axis, y i denotes the coordinate of the sensor node on the y-axis, Th denotes the sensor node number threshold, S nodei is the coverage area of a single node. 2.The software-defined warehouse floor sensor network coverage strategy method of claim 1, wherein: Step 2 comprises the following: According to the coverage of each node on the grain bottom, considering the detection range of the node, the node spacing, the dynamic state factor of the node, designing a non-redundant optimization coverage scheme for the irregular curved surface of the grain bottom network, initially improving the coverage rate of the grain bottom network, making the nodes in the network reach the connectivity and the basic requirements of the sensing coverage area, guaranteeing the network service, and providing basic conditions for realizing the multi-objective coverage optimization strategy of software-defined grain bottom sensor network.

3. The method of claim 1, wherein the method further comprises: determining a number of the plurality of sensors to be activated based on the number of the plurality of sensors and the number of the plurality of bins. Step 3 comprises the following: Finally, combined with the invulnerability of the node, the distribution factor of the robust node under the damaged state of the network, design the network repair mechanism and the scheme to improve the invulnerability under damage, through the accurate coverage of important targets and the coverage of multiple targets, realize the accurate coverage of each target on the entire grain bottom concave and convex surface, improve the accuracy rate in the coverage degree level, optimize the implementation cost, and then realize the multi-objective coverage optimization strategy of software-defined grain sensor network.

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