An indoor AP positioning method for industrial enterprise wireless network security and threats

Abstract

The present invention belongs to the field of indoor wireless positioning in industrial scenarios, and proposes an indoor AP positioning method for industrial enterprise wireless network security and threats. The method first builds an indoor wireless simulation environment and deploys the AP nodes to be located and measurement points to collect the received RSS signal strength; then estimates the logarithmic distance path loss model parameters by deploying two auxiliary AP nodes and measurement points, and uses the estimated logarithmic distance path loss model parameters for the conversion between signal and node spacing; then enters the AP positioning, the stage of locking the smaller boundary range of the AP node to be located and the stage of predicting the position of the AP node. The present invention improves the data quality and the accuracy of indoor AP positioning; when the experimental environment is in a factory workshop environment with a large area, the invention will greatly improve the data quality by preliminarily locking the positioning area, reduce the cost of later data collection, and improve positioning accuracy.

Description

Technical Field

[0001] The present invention relates to the field of indoor wireless positioning in industrial scenarios, and in particular to an indoor AP positioning method for industrial enterprise wireless network security and threats. Background Art

[0002] With the advent of Industry 4.0, the manufacturing industry is gradually transforming towards intelligence and digitalization, which also means that industrial wireless networks are poised for a period of rapid development. In industrial scenarios, factories will introduce a large number of intelligent devices. Due to the unique characteristics of factories, these facilities can lead to dangerously large open spaces and a large number of wireless access points clustered indoors. Consequently, the demand for indoor location-based services (LBS) is increasing. The key to LBS is accurate positioning, which can provide significant convenience in daily life and production. In the face of major public health events, the application of location-based services in smart industrial scenarios warrants further exploration. As the coverage of industrial wireless networks expands, unknown access points (APs) can easily infiltrate large factory workshops. These APs can infiltrate the factory floor through various means. Failure to locate and track them in a timely manner can lead to immeasurable losses for the enterprise.

[0003] Currently, the Industrial Internet demonstrates distinct advantages, and demand for unmanned operations continues to grow. However, with the widespread use of industrial wireless networks and unmanned equipment, hidden access points (APs) often infiltrate these networks, making them difficult to detect in a timely manner. This can lead to data leaks and loss, resulting in significant economic losses for businesses. As intelligent technology becomes increasingly prevalent in the industrial sector, the demand for wireless devices will inevitably increase dramatically, along with the number of wireless access points (APs), making AP location information difficult to manage. Therefore, being able to pinpoint the location of each AP will physically protect the transmission security of a large number of wireless devices, enabling the timely detection of unknown APs and ensuring the smooth operation of factory floors.

[0004] However, existing AP node detection technologies still have the following problems:

[0005] (1) Data noise processing issues. Due to the inherent characteristics of RSS signals, such as the smaller the information error when the measurement point is closer to the AP, multipath fading, and shadow fading, the collected data often contains values that are not within a reasonable range.

[0006] (2) Channel model parameter optimization. The parameters of the logarithmic distance path loss model vary in different environments. Based on existing research, different scenarios have been roughly classified, and each category corresponds to a certain range of parameter thresholds. However, in actual applications, manually set parameters will lead to certain errors.

[0007] (3) Improve the establishment of AP indoor positioning model. Existing AP indoor positioning algorithms mainly include least squares method, weighted least squares method, particle swarm optimization algorithm, region partitioning iterative algorithm, etc. These methods all have certain limitations and can only improve positioning accuracy from a relatively single dimension.

[0008] (4) Positioning result evaluation. Regarding indoor AP positioning, the most important issue is improving accuracy. Therefore, the accuracy is evaluated multiple times by improving the positioning algorithm. However, in real-world deployments, other considerations include deployment costs. Summary of the Invention

[0009] Driven by the above research objectives, this paper proposes an indoor AP positioning method for industrial enterprise wireless network security and threats. To address the series of problems existing in indoor AP positioning in industrial scenarios, this paper proposes an indoor AP positioning method for industrial enterprise wireless network security and threats.

[0010] The technical solution of the present invention is an indoor AP positioning method for wireless network security and threats in industrial enterprises. The method first establishes an indoor wireless simulation environment and deploys the AP nodes to be located and measurement points to collect the received RSS signal strength. Then, the logarithmic distance path loss model parameters are estimated by deploying two auxiliary AP nodes and measurement points, and the estimated logarithmic distance path loss model parameters are used for the conversion between signal and node spacing. Then, AP positioning is performed, and AP positioning is divided into two stages. In the first stage, the locked measurement points set in the area to be located are randomly moved in the scene, and a smaller boundary range of the AP nodes to be located is found based on geometric relationships to reduce the deployment area of the positioning measurement points in the formal positioning process in the second stage. In the second stage, the positioning measurement points are deployed according to the boundary range obtained in the first stage, and the AP positioning is completed by the least squares method.

[0011] The specific steps for estimating the logarithmic distance path loss model parameters are as follows:

[0012] 1.1) Deploy two auxiliary AP nodes in the built indoor wireless simulation scene, and then collect the RSS signals of the auxiliary AP nodes by deploying multiple measurement points. Each auxiliary AP node corresponds to a set of RSS signal values. i (RSS i1 ,RSS i2 ,…,RSSij ) represents the RSS signals from the j measurement points obtained by the i-th auxiliary AP node;

[0013] 1.2) Preprocess the collected RSS signal through a Gaussian filter to remove low-probability data;

[0014] 1.3) The RSS signal is converted into the distance between the AP node and the measurement point using the logarithmic distance path loss model. The parameters P and γ of the logarithmic distance path loss model are estimated by regressing the preprocessed data.

[0015] The specific formula of the pretreatment is shown in formula (1);

[0016]

[0017] Among them, RSS i is the RSS information of a certain auxiliary AP node collected at different locking measurement points, μ is the mean RSS collected for each AP node, σ is the standard deviation of this group of RSS; n is the total number of RSS collected for each AP node.

[0018] The logarithmic distance path loss model is shown in formula (2):

[0019]

[0020] Where P is the path loss at the reference distance d0, γ is the path loss exponent, d is the distance from the transmitting node, d0 is the unit distance from the transmitting node, and X g is a random variable with mean 0 and standard deviation σ.

[0021] The AP positioning is divided into two stages: a stage of locking a smaller boundary range of the AP node to be positioned and a stage of predicting the position of the AP node.

[0022] The phase of locking the smaller boundary range of the AP node to be located includes the following steps:

[0023] Based on the geometric relationship between the AP nodes and the locked measurement point, the boundary range of the AP nodes to be located in the area is accurately locked. After repeated random movements, the locked measurement point is finally moved to the center of the two AP nodes to be located. At this time, a circle is drawn with this center position as the center and the distance between the two AP nodes as the diameter to determine the preliminary range of the AP nodes to be located.

[0024] Since the RSS signal has the characteristic that the closer to the AP node, the stronger the signal, the distance is judged by the signal strength received by the locked measurement point during the movement; when the locked measurement point is closer to the center of the two AP nodes to be located, the sum of the signal strengths between the locked measurement point and the AP nodes to be located at both ends should be minimized and as close as possible. The formula for this part is shown in formula (3);

[0025]

[0026] in, It is the distance from the locked measurement point to one of the two AP nodes. is the distance from the measurement point to the other of the two AP nodes; θ is the threshold of the absolute value of the distance difference between the measurement point and the two AP nodes, To lock the distance between the measurement point and a certain AP node;

[0027] The measurement point is randomly moved around the room repeatedly, and the point with the smallest sum of distances is selected as the center of the circle where each two AP nodes to be located are located.

[0028] After all the circle centers are determined, circles are drawn with different centers and half the distance between each two AP nodes as the radius. Finally, the outermost boundaries of these circles are locked with the minimum rectangular area.

[0029] The AP node location prediction stage is specifically as follows:

[0030] After establishing the smaller boundary range where the AP node to be located is located, the positioning measurement points are deployed in a grid manner within the range; the signal strength of different AP nodes is collected at each positioning measurement point and filtered through a Gaussian filter; the filtered signal data is substituted into the logarithmic distance path loss model with the determined estimated parameters to complete the distance conversion; finally, since there will be a certain error between the estimated parameters and the actual parameters, the threshold range of the Gaussian filter and the logarithmic distance path loss model parameters is expanded, and the least squares method is used to realize the prediction of the AP position to be located. The formula for this part is shown in formula (4);

[0031]

[0032] Where X represents the horizontal coordinate vector of the AP node to be located, and Y represents the vertical coordinate vector of the AP node to be located; x1, x2,…, x n Represents the horizontal coordinates of different positioning measurement points, y1, y2, ..., y n Represents the vertical coordinates of different positioning measurement points, d1, d2, ..., d n Represents the node distance converted from the signal strength between different nodes;

[0033] After expansion and calculation, it is expressed as: Q = AT, as shown in formula (5):

[0034]

[0035] Make the objective function Minimize and obtain the final solution, that is, the coordinates of the AP node to be located, as shown in formula (6):

[0036] T=(A T A) -1 A T Q(6).

[0037] The present invention has the following beneficial effects: By estimating the parameters of the logarithmic distance path loss model and performing Gaussian filtering on the signal data, the present invention further improves data quality during the signal-to-distance conversion process. Furthermore, by locking onto a smaller area where the AP node to be located resides, the least squares regression method is used to predict the AP location, thereby improving the accuracy of indoor AP positioning. When the experimental environment is a large factory workshop, the invention significantly improves data quality by initially locking onto the positioning area, reducing subsequent data collection costs and improving positioning accuracy. It also facilitates regular monitoring and maintenance of AP nodes, reducing safety risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flowchart for indoor AP positioning in industrial scenarios;

[0039] Figure 2 It is the geometric relationship diagram between the AP node to be located and the measurement point;

[0040] Figure 3 The area where the AP node to be located is located after preliminary estimation;

[0041] Figure 4 is the cumulative distribution function of indoor AP positioning error under different methods;

[0042] Figure 5 Box plot of indoor AP positioning error under different methods. DETAILED DESCRIPTION

[0043] The implementation scheme of the present invention is as follows:

[0044] An indoor AP positioning method for industrial enterprise wireless network security and threats, its overall structure is as follows Figure 1 As shown, it mainly includes the following steps:

[0045] 1. Logarithmic distance path loss model parameter estimation stage:

[0046] (1.1) Deploy auxiliary AP nodes in the virtual wireless indoor simulation scene, and then collect the RSS signals of these auxiliary AP nodes by deploying certain measurement points. Each AP node corresponds to a set of RSS signal values, such as: AP i (RSS i1 ,RSS i2 ,…,RSS ij ) represents the RSS signals from j measurement points obtained by the i-th auxiliary AP node.

[0047] (1.2) The collected data is preprocessed using a Gaussian filter to remove data with low probability. The specific formula is shown in formula (1). The present invention converts the RSS signal into the distance between the AP node and the measurement point using the logarithmic distance path loss model. The logarithmic distance path loss model is shown in formula (2), where PL0 and γ are highly susceptible to environmental influences. The present invention implements parameter estimation of the logarithmic distance path loss model (LDPL) by regressing the preprocessed data.

[0048]

[0049] 2. Positioning stage:

[0050] After the LDPL model parameter estimation is completed, the AP positioning phase officially begins. The positioning phase is divided into two stages: the phase of locking the smaller boundary range of the AP node to be located and the phase of predicting the AP position.

[0051] (2.1) The stage of locking the smaller boundary range of the AP node to be located. In an ideal situation, the boundary range of the AP node to be located in the area can be accurately locked according to the geometric relationship between the AP node and the locked measurement point. Figure 2 As shown in the figure, AP1 and AP2 are the AP nodes to be located, and MP represents the locked measurement point. After multiple random moves, MP finally moves to the center of the two AP nodes to be located. At this time, a circle is drawn with the current locked measurement point as the center and the distance between the two AP nodes to be located as the diameter. The two AP nodes to be located must be on this circle, so the preliminary range of the AP nodes to be located can be determined.

[0052] Because RSS signals are stronger the closer they are to the AP, the present invention uses the signal strength received by the locked measurement point during movement to determine the signal strength. As the locked measurement point approaches the center of the two APs, the sum of the signal strengths between the measurement point and the APs to be located at both ends should be minimized and as close as possible. This is shown in Equation (3).

[0053]

[0054] in, It is the distance from the locked measurement point to one of the two AP nodes. is the distance from the measurement point to the other of the two AP nodes; θ is the threshold of the absolute value of the distance difference between the measurement point and the two AP nodes, To lock the distance between the measurement point and a certain AP node;

[0055] The measurement point is randomly moved in the room and iterated multiple times. Finally, the point with the closest distance and the smallest sum of distances is selected as the center of the circle where each two AP nodes are located. When all the centers are determined, circles with different centers and half of the distance between each two AP nodes as the radius are drawn. Finally, the outermost boundaries of these circles are locked with the minimum rectangular area, such as Figure 3 As shown (under ideal conditions). In real-world situations, signal noise inevitably leads to deviations in center positioning. However, as long as this deviation is kept within a certain range, it will not significantly impact the positioning phase. If the room is large enough and the AP nodes to be located are relatively close together, initially locking the range will help improve the efficiency of the subsequent positioning process.

[0056] (2.2) AP node location prediction phase. After establishing a small area where the AP to be located is located in the first phase, positioning measurement points are deployed in a grid pattern within this area. Signal strengths of different AP nodes are collected at each positioning measurement point and filtered through a Gaussian filter. The filtered signal data is substituted into the channel model after parameter estimation to complete the distance conversion. Finally, the Gaussian filter and channel model parameters are dynamically modified, and the least squares method is used to predict the location of the AP to be located. This part of the formula is shown in Equation (4).

[0057]

[0058] After expansion and calculation, it is expressed as: Q = AT, as shown in formula (5):

[0059]

[0060] In order to minimize the objective function, the final solution can be obtained, that is, the coordinates of the AP node to be located, as shown in formula (6):

[0061] T=(A T A) -1 A T Q(6)

[0062] 3. Testing phase:

[0063] To verify the effectiveness of this method for indoor AP positioning, the coordinates of 100 AP nodes were randomly generated during the test phase. Four AP nodes were deployed in each experiment, for a total of 25 experiments, all in a 60m*60m room.

[0064] During the testing phase, 25 experiments were conducted using the proposed method, the least squares method with Gaussian filtering, and the least squares method, respectively, to obtain 300 positioning results. The errors between these experimental results and the true results were statistically analyzed. The experiment analyzed the positioning errors generated by the three methods using the cumulative distribution function (CDF), as shown in the following figure: Figure 4 CDF refers to the probability sum less than or equal to a certain value, as shown in formula (7). Figure 4 It can be found that the CDF of the method proposed in the present invention can approach the probability value 1 as quickly as possible, so the method proposed in the present invention has a good positioning effect.

[0065] CDF(e)=P(x≤e)(7)

[0066] At the same time, the experiment draws box plots for the positioning results obtained by the three methods, such as Figure 5 The average positioning errors of the proposed method, the least squares method with Gaussian filtering, and the least squares method are 6.14m, 6.85m, and 7.76m, respectively. The proposed method has a certain improvement in accuracy compared to other methods.

[0067] It should be noted that the above description is a specific embodiment provided in conjunction with specific content, and the present invention is not limited to the above description. Any method or structure similar to the present invention, or any method that makes several substitutions or deductions based on the concept of the present invention, shall be considered as the scope of protection of the present invention.

Claims

1. An indoor AP positioning method for industrial enterprise wireless network security and threats, characterized by: This indoor AP positioning method, which focuses on wireless network security and threats in industrial enterprises, first builds an indoor wireless simulation environment and deploys the AP nodes to be located and measurement points to collect the received RSS signal strength. Then, two auxiliary AP nodes and measurement points are deployed to estimate the logarithmic distance path loss model parameters. The estimated logarithmic distance path loss model parameters are used to convert signals to node distances. Then, we enter AP positioning, which is divided into two stages: the stage of locking the smaller boundary range of the AP node to be located and the stage of predicting the AP node position; The AP positioning process has two phases: in the first phase, the locked measurement points set in the area to be positioned are randomly moved in the scene, and the smaller boundary range where the AP node to be positioned is located is found based on the geometric relationship to reduce the deployment area of the positioning measurement points in the second phase of the formal positioning process; In the second stage, positioning measurement points are deployed based on the boundary range obtained in the first stage, and AP positioning is completed using the least squares method.

2. The indoor AP positioning method for industrial enterprise wireless network security and threats according to claim 1 is characterized in that: The specific steps for estimating the logarithmic distance path loss model parameters are as follows: 1.1) Deploy two auxiliary AP nodes in the built indoor wireless simulation scene, and then collect the RSS signals of the auxiliary AP nodes by deploying multiple measurement points. Each auxiliary AP node corresponds to a set of RSS signal values. i (RSS i1 ,RSS i2 ,…,RSS ij ) represents the RSS signals from the j measurement points obtained by the i-th auxiliary AP node; 1.2) Preprocess the collected RSS signal through a Gaussian filter to remove low-probability data; 1.3) The RSS signal is converted into the distance between the AP node and the measurement point using the logarithmic distance path loss model. The parameters P and γ of the logarithmic distance path loss model are estimated by regressing the preprocessed data.

3. The indoor AP positioning method for industrial enterprise wireless network security and threats according to claim 2 is characterized in that: The specific formula of the pretreatment is shown in formula (1); Among them, RSS i is the RSS information of a certain auxiliary AP node collected at different locking measurement points, μ is the mean RSS collected from each AP node, σ is the standard deviation of this group of RSS; n is the total number of RSS collected from each AP node.

4. The indoor AP positioning method for industrial enterprise wireless network security and threats according to claim 2 or 3 is characterized in that: The logarithmic distance path loss model is shown in formula (2): Where P is the path loss at the reference distance d0, γ is the path loss exponent, d is the distance from the transmitting node, d0 is the unit distance from the transmitting node, and X g is a random variable with mean 0 and standard deviation σ.

5. The indoor AP positioning method for industrial enterprise wireless network security and threats according to claim 1 is characterized in that: The phase of locking the smaller boundary range of the AP node to be located includes the following steps: Based on the geometric relationship between the AP nodes and the locked measurement point, the boundary range of the AP nodes to be located in the area is accurately locked. After repeated random movements, the locked measurement point is finally moved to the center of the two AP nodes to be located. At this time, a circle is drawn with this center position as the center and the distance between the two AP nodes as the diameter to determine the preliminary range of the AP nodes to be located. Since the RSS signal has the characteristic that the closer to the AP node, the stronger the signal, the distance is judged by the signal strength received by the locked measurement point during the movement; when the locked measurement point is closer to the center of the two AP nodes to be located, the sum of the signal strengths between the locked measurement point and the AP nodes to be located at both ends should be minimized and as close as possible. The formula for this part is shown in formula (3); in, It is the distance from the locked measurement point to one of the two AP nodes. is the distance from the measurement point to the other of the two AP nodes; θ is the threshold of the absolute value of the distance difference between the measurement point and the two AP nodes, To lock the distance between the measurement point and a certain AP node; The measurement point is randomly moved around the room repeatedly, and the point with the smallest sum of distances is selected as the center of the circle where each two AP nodes to be located are located. After all the circle centers are determined, circles are drawn with different centers and half the distance between each two AP nodes as the radius. Finally, the outermost boundaries of these circles are locked with the minimum rectangular area.

6. The indoor AP positioning method for industrial enterprise wireless network security and threats according to claim 1 is characterized in that: The AP node location prediction stage is specifically as follows: After establishing the smaller boundary range of the AP node to be located, the positioning measurement points are deployed in a grid manner within the range; the signal strength of different AP nodes is collected at each positioning measurement point and filtered through a Gaussian filter; the filtered signal data is substituted into the logarithmic distance path loss model with determined estimation parameters to complete the distance conversion; finally, the threshold range of the Gaussian filter and the logarithmic distance path loss model parameters is expanded, and the least squares method is used to predict the position of the AP to be located. The formula for this part is shown in Equation (4); Where X represents the horizontal coordinate vector of the AP node to be located, and Y represents the vertical coordinate vector of the AP node to be located; x1, x2,…, x n Represents the horizontal coordinates of different positioning measurement points, y1, y2, ..., y n Represents the vertical coordinates of different positioning measurement points, d1, d2, ..., d n Represents the node distance converted from the signal strength between different nodes; After expansion and calculation, it is expressed as: Q = AT, as shown in formula (5): Make the objective function Minimize and obtain the final solution, that is, the coordinates of the AP node to be located, as shown in formula (6): T=(A T A) -1 A T Q(6)。

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