An Optimal Deployment Method for Photoelectric-Radar Sensor Networks Based on Greedy Iteration

Abstract

The present invention discloses a method for optimizing the deployment of an optoelectronic radar sensor network based on greedy iteration, and relates to the technical fields of optoelectronic radar sensor networks, sensor deployment, etc. The present invention first obtains the minimum rectangle that can cover the area to be monitored, discretizes the input area, and then calculates the number of nodes required to cover the monitoring area. According to the number of nodes, the position coordinates of each node are obtained using uniform distribution, and the initial perception direction of each node is randomly initialized; then the perception direction and coverage area of ​​the node are updated in an iterative manner to obtain the perception direction sequence of the sensor node; finally, the perception direction is adjusted according to the sensor perception direction sequence to achieve coverage of the area to be monitored. The method achieves maximization of the perception coverage monitoring area at a lower network deployment cost by fixing the node position and adjusting the perception direction.

Description

Technical Field

[0001] The present invention relates to the technical fields of photoelectric-radar sensor networks, sensor deployment and the like, and in particular to a method for optimizing deployment of photoelectric-radar sensor networks based on greedy iteration. Background Art

[0002] Wireless sensor networks have the advantages of high practicality, flexible settings, and strong scalability. They communicate and transmit information through multi-hop, self-organization or collaboration, and can autonomously and intelligently perceive the surrounding environment and complete target tasks. They play an important role in border protection, motion tracking, military intelligence monitoring and other fields. For nodes in optoelectronic-radar sensor networks for border protection, deployment technology can deploy fixed, random or dynamically movable nodes in the monitoring area through various means. Different network node deployment methods will have a huge impact on the network's topology, connectivity, network lifetime and service quality. Due to the limited characteristics of the perception range of directed sensor nodes, their perception ability is closely related to the distance and relative position of the node from the target object. Due to the limited characteristics of the perception range of directed sensor nodes, their perception ability is closely related to the distance and relative position of the node from the target object. Lu Kezhong et al. proposed a greedy iterative algorithm to optimize the node position according to the incremental change of the regional coverage rate to seek the optimal solution for directed node coverage. Wang et al. compared two heuristic deployment optimization algorithms for rotatable directed sensor nodes in the scenarios of target object aggregation and random distribution, using relay nodes to ensure connectivity and reducing node usage through overlapping coverage between nodes. The rise and development of intelligent algorithms in recent years have provided new ideas and means for solving complex problems. The literature points out that genetic algorithms and simulated annealing algorithms can be used to solve the composition optimization problem of wireless sensor networks. However, their convergence speed and search time need to be further improved. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide an optoelectronic-radar sensor network optimization deployment method based on greedy iteration, which achieves maximization of the perception coverage monitoring area at a lower network deployment cost by fixing the node position and adjusting the perception direction.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] An optoelectronic-radar sensor network optimization deployment method based on greedy iteration includes the following steps:

[0006] Step 1: Obtain a minimum rectangle that can cover the area to be monitored, where the length of the minimum rectangle is Length and the width is Width;

[0007] Step 2: Divide the area to be monitored into multiple square grids with a side length of δ; discretize the sensor sensing direction with an accuracy of ε;

[0008] Step 3: Calculate the number of sensor nodes N1 required to cover the area to be monitored:

[0009]

[0010] Among them, β and R are the sensing half angle and sensing radius of the sensor respectively;

[0011] Step 4: Distribute the nodes evenly according to the number of sensor nodes and obtain the position coordinates p1, p2, …, p of each node. N , randomly initialize the initial perception direction of each node

[0012] Step 5: Set the initial iteration perception direction Set the update flag changed = true, and the node set S that needs to update the status = {1, 2, ..., N};

[0013] Step 6: Determine whether changed = true. If so, execute step 7; if not, execute step 13;

[0014] Step 7: For any node i in the set S, calculate its current private coverage area Use traversal to find the local optimal solution that maximizes the private coverage area. And calculate the private coverage area in the local optimal state The area of ​​private coverage is calculated as follows:

[0015]

[0016] Where: A{·} is a function used to calculate the area of ​​the region. The grid-based sample point method is used to calculate the area of ​​the region, and the intersection points of the grid are used to approximate the coverage area of ​​the node; S area is the area to be monitored, φ(R,β,p i ,α i ) is the sensing area of ​​node i, S i ={j|1≤j≤N,j≠i,|p i ,p j |≤2R}, is the set of neighbor nodes of node i, φ(R,β,p j ,α j ) is the sensing area of ​​its neighboring nodes; the sensing area of ​​sensor i is a i is the center of the circle, the radius is R, the angle is 2β, and the angle between the midline and the x-axis is α isector area;

[0017] Step 8: Set the node set that needs to update the status And set the flag changed = false;

[0018] Step 9: For any node i, calculate the private coverage area and determine and Is it true? If yes, go to step 10; if no, go to step 12;

[0019] Step 10: For all neighbor nodes j of node i, determine ||p j ,φ(R,β,p i ,α' i )||<R or Is it true? If yes, set S = S∪{j}, if not, do nothing; where ||p,φ|| represents the minimum distance from a node to a certain sensing area;

[0020] Step 11: Update the sensing direction and private coverage area of ​​node i And set the flag changed = true;

[0021] Step 12: Execute step 6;

[0022] Step 13: After iteration, the sensing direction sequence of the sensor node is obtained:

[0023] Step 14: Adjust the sensing direction according to the obtained sensor node sensing direction sequence to achieve coverage of the area to be monitored.

[0024] The present invention has the following beneficial effects:

[0025] 1. Aiming at the regional coverage problem in the optoelectronic-radar sensor network, the present invention proposes an optoelectronic-radar sensor network optimization deployment method based on greedy iteration. The method reduces the network deployment cost while maximizing the perception coverage detection area by fixing the node position and adjusting the perception direction.

[0026] 2. The simulation results show that when the network deployment cost is low, the final coverage effect of the greedy iterative algorithm is still good and can achieve a coverage rate of more than 85%. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of the optoelectronic-radar perception model.

[0028] Figure 2 The node positions are evenly distributed.

[0029] Figure 3 This is the initial coverage effect of the node, with a coverage rate of 46.73%.

[0030] Figure 4 This is the final coverage effect of the node, with a coverage rate of 88.56%. DETAILED DESCRIPTION

[0031] 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 in conjunction with specific embodiments and with reference to the accompanying drawings.

[0032] An optoelectronic-radar sensor network optimization deployment method based on greedy iteration includes the following steps:

[0033] Step 1: Get the minimum rectangle that can cover the area to be monitored. Assume that the length of the minimum rectangle is Length and the width is Width;

[0034] Step 2: Discretize the input area. Divide the area to be monitored into a number of square grid points with a side length of δ. Discretize the sensor sensing direction with an accuracy of ε;

[0035] Step 3: Calculate the number of nodes required to cover the monitoring area: Among them, β and R are the sensing half angle and sensing radius of the sensor respectively;

[0036] Step 4: According to the number of nodes, use uniform distribution to obtain the position coordinates of each node p1, p2, ..., p N , randomly initialize the initial perception direction of each node

[0037] Step 5: Set the initial iteration perception direction Set the update flag changed = true, and the node set S that needs to update the status = {1, 2, ..., N};

[0038] Step 6: Determine whether changed = true. If so, execute step 7; if not, execute step 13;

[0039] Step 7: For any node i in the set S, calculate its current private coverage area Use traversal solution to find the local optimal solution that maximizes the private coverage area And calculate the private coverage area in the local optimal state The private coverage area is calculated as follows:

[0040]

[0041] Where: A{·} is a function used to calculate the area of ​​the region. The grid-based sample point method is used to calculate the area of ​​the region, and the intersection of the grid is used to approximate the coverage area of ​​the node. area is the area to be monitored, φ(R,β,p i ,α i ) is the sensing area of ​​node i, S i ={j|1≤j≤N,j≠i,|p i ,p j |≤2R}, is the set of neighbor nodes of node i, φ(R,β,p j ,α j ) is the sensing area of ​​its neighboring nodes. The sensing area of ​​sensor i is a i is the center of the circle, the radius is R, the angle is 2β, and the angle between the midline and the x-axis is α i sector area;

[0042] Step 8: Set the node set that needs to update the status and flag changed = false;

[0043] Step 9: For any node i, use the private coverage area calculated in step 6 to determine and Is it true? If yes, go to step 10; if no, go to step 12;

[0044] Step 10: For all neighbor nodes j of node i, determine ||p j ,φ(R,β,p i ,α' i )||<R or Is it true, where ||p,φ|| represents the minimum distance from a node to a certain sensing area. If yes, set S=S∪{j}; if no, do nothing.

[0045] Step 11: Update the sensing direction and coverage area of ​​node i. And set the flag changed = true;

[0046] Step 12: Execute step 6;

[0047] Step 13: After iteration, the sensing direction sequence of the sensor node is obtained:

[0048] Step 14: Adjust the sensing direction according to the obtained sensor sensing direction sequence to achieve coverage of the area to be monitored.

[0049] Here is a more specific example:

[0050] An optoelectronic-radar sensor network optimization deployment method based on greedy iteration is proposed. The optimization goal of this method is to cover the largest monitoring area with the least number of sensors deployed.

[0051] Assume that N directional sensor nodes of the same type need to be deployed to sense and cover the monitored area S area , the perception model of the optoelectronic-radar sensor node is transformed into a four-tuple (p i ,α i ,β,R) to represent, Figure 1 Schematic diagram of the perception model. Figure 1 In, p i =(x i ,y i ) is the position of the sensor node, which is also the origin of the corresponding coordinate axis, α i is the angle between the fan midline and the positive direction of the x-axis, which is used to indicate the sensing direction of the node. i ∈[0,2π), β is the perception half angle, 2β is the perception angle, and R is the sector radius.

[0052] The coverage rate Cov of the monitoring area is defined as the proportion of the monitoring area covered by all nodes, expressed as

[0053]

[0054] Among them, A{·} is the function used to calculate the area of ​​the region, φ(R,β,p i ,α i ) is the perception area of ​​node i, which is a p i is the center of the circle, the radius is R, the angle is 2β, and the angle between the midline and the x-axis is α i sector area.

[0055] Assume that after placing the sensor nodes, the node positions are fixed and the sensing direction of the nodes can be rotated 360 degrees around the nodes, which is convenient for adjusting to any direction. Therefore, the coverage optimization problem can be expressed as: for a given S area , R, β, p1, p2, …, p N The sensor network is used to solve the optimal sensing direction sequence that maximizes the coverage rate.

[0056] The greedy iterative algorithm is used to optimize the perception direction sequence of the sensor node. and S i To rephrase the original question, and S i The private coverage area and neighbor node set of node i respectively.

[0057] The private coverage area of ​​node i is defined as the part of the monitoring area covered by node i and not covered by any other node. represents its area, that is

[0058]

[0059] Among them: S i Represents the set of neighbor nodes of node i, that is, S i ={j|1≤j≤N,j≠i,p i ,p j |≤2R}. For any two nodes i and j, if and only if the distance between them |p i ,p j When |≤2R, the perception areas of nodes i and j are likely to overlap, and these two nodes are defined as neighbor nodes.

[0060] The covered part of the monitoring area can be divided into two disjoint areas: the private coverage area of ​​node i and the part of the monitoring area covered by other nodes except i. Therefore, the coverage rate can also be expressed as

[0061]

[0062] Therefore, under the premise that the perception direction of other nodes remains unchanged, the perception direction of a certain node i can be adjusted by α i , so that The value of increases, thereby increasing Cov(α1,α2,…,α N ) value.

[0063] For a given S area , R, β, p1, p2, …, p N For a directed sensor network, if the function In α i ', then the node i is said to be in (α1, α2, …, α i ,…,α N )=(α1',α2',…,α i ',…,α' N ) is in the local optimal state. If all nodes are in (α1, α2, …, α i ,…,α N )=(α1',α2',…,α i ',…,α' N ) are all in the local optimal state, then (α1',α'2,…,α i ',…,α' N) is a local optimal direction sequence. Therefore, any optimal direction sequence must be a local optimal direction sequence, and vice versa. i ,…,α' N ) is a local optimal direction sequence, then the coverage can no longer be increased by adjusting the sensing direction of any node. We expect that the coverage at this time will be close to the optimal coverage.

[0064] After the greedy iterative algorithm returns a local optimal direction sequence, each node actually physically rotates its sensor according to this direction sequence.

[0065] Specifically, the steps of this method are as follows:

[0066] Assume that the simulation parameters are set as shown in Table 1.

[0067] Table 1 Simulation parameter settings

[0068] Simulation parameters Numeric Radar sensor’s sensing radius R 10km Radar sensor’s perception angle 2β 90° Grid accuracy δ 0.1 The precision of the GIA algorithm's perceived direction discretization ε π / 100 Increment d in the GIA algorithm 0 Maximum number of iterations T 20

[0069] (1) Assume that the monitoring area is a rectangular area with a length of 100 km and a width of 30 km, that is, S area =100×30=3000km 2 , the sensing radius of the photoelectric sensor is 1km and the sensing angle is 90 degrees.

[0070] (2) Through It is estimated that the number of radar sensors required to cover this area is about 39. Therefore, it is assumed that the number of radar sensor nodes N=39.

[0071] (3) The positions of nodes are evenly distributed in the monitoring area, and the initial sensing direction is randomly generated. The evenly distributed node positions are as follows: Figure 2 As shown, the dots are the locations of the nodes.

[0072] (4) The initial coverage effect of the node is as follows Figure 3 As shown in Figure 1, the initial coverage rate at this time is 46.73%. It can be seen that the effective sensing area of ​​most nodes is small, and the sensing areas of some nodes overlap, resulting in a large number of parts in the monitoring area that are not covered by nodes.

[0073] (5) The node positions are fixed and a greedy iterative algorithm is used to adjust the node's perception direction until all nodes do not need to update their status.

[0074] (6) After the iteration, the sensor’s sensing direction is adjusted according to the calculated sensing direction sequence. The final node coverage effect is as follows: Figure 4 As shown, the coverage is 88.56%.

Claims

1. A method for optimizing deployment of optoelectronic-radar sensor networks based on greedy iteration, characterized in that: The following steps are involved: Step 1: Obtain a minimum rectangle that can cover the area to be monitored, where the length of the minimum rectangle is Length and the width is Width; Step 2: Divide the area to be monitored into multiple square grids with a side length of δ; discretize the sensor sensing direction with an accuracy of ε; Step 3: Calculate the number of sensor nodes N1 required to cover the area to be monitored: Among them, β and R are the sensing half angle and sensing radius of the sensor respectively; Step 4: Distribute the nodes evenly according to the number of sensor nodes and obtain the position coordinates p1, p2, ..., p of each node. N , randomly initialize the initial perception direction of each node Step 5: Set the initial iteration perception direction Set the update flag changed = true, and the node set S that needs to update the status = {1, 2, ..., N}; Step 6: Determine whether changed = true. If so, execute step 7; if not, execute step 13; Step 7: For any node i in the set S, calculate its current private coverage area Use traversal to find the local optimal solution that maximizes the private coverage area. And calculate the private coverage area in the local optimal state The area of ​​private coverage is calculated as follows: Where: A{·} is a function used to calculate the area of ​​the region. The grid-based sample point method is used to calculate the area of ​​the region, and the intersection points of the grid are used to approximate the coverage area of ​​the node; S area is the area to be monitored, φ(R, β, p i , α i ) is the sensing area of ​​node i, S i ={j|1≤j≤N, j≠i, |p i , p j |≤2R}, is the set of neighbor nodes of node i, φ(R, β, p j , α j ) is the perception area of ​​node i’s neighbor node j; the perception area of ​​node i is a i is the center of the circle, the radius is R, the angle is 2β, and the angle between the midline and the x-axis is α i sector area; Step 8: Set the node set that needs to update the status And set the flag changed = false; Step 9: For any node i, calculate the private coverage area and determine and Is it true? If yes, go to step 10; if no, go to step 12; Step 10: For all neighbor nodes j of node i, determine ||p j ,φ(R,β,p i , α′ i )||≤R or Is it true? If yes, set S = SU{j}, if not, do nothing; where ||p, φ|| represents the minimum distance from a node to a certain sensing area; Step 11: Update the sensing direction and private coverage area of ​​node i And set the flag changed = true; Step 12: Execute step 6; Step 13: After iteration, the sensing direction sequence of the sensor node is obtained: Step 14: Adjust the sensing direction according to the obtained sensor node sensing direction sequence to achieve coverage of the area to be monitored.

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