A wireless sensor network node positioning method based on trilateration improvement

Abstract

The application discloses a wireless sensor network node positioning algorithm based on improved trilateration, introduces reference triangle geometry and triangle in-point rule, and comprises the following steps: step 1, anchor node broadcasts data packet; step 2, the distance between unknown node and anchor node is calculated; step 3, N anchor nodes are selected to form a triangle; step 4, the reference triangle geometry threshold is set to screen the triangle; step 5, the triangle in-point rule is used to screen the triangle; step 6, the estimated coordinates of the unknown node are calculated by trilateration; step 7, the average absolute distance difference of the estimated coordinates of each unknown node is calculated; and step 8, the coordinates of the optimal node are selected and calculated. The application introduces the triangle in-point rule on the basis of the trilateration algorithm based on the reference triangle geometry, and effectively improves the positioning precision and efficiency of the node.

Description

Technical Field

[0001] This invention relates to a wireless sensor network node localization method based on trilateration, belonging to the field of wireless sensor network localization technology. Background Technology

[0002] Wireless Sensor Networks (WSNs) are distributed sensor networks composed of a group of sensor nodes in a self-organizing network configuration. They represent a novel technology that integrates multiple disciplines and has applications in various fields, including military, traffic management, smart homes, and healthcare. In WSNs, localization technology is a fundamental technology, playing a crucial role in its subsequent operation and application.

[0003] Configuring the Global Positioning System (GPS) to each network node is the simplest positioning method for wireless sensor networks. However, the high cost and energy limitations of GPS prevent it from being installed on every sensor node, so only a small number of beacon nodes are equipped with GPS modules.

[0004] In recent years, scholars both domestically and internationally have proposed numerous node localization algorithms for use in wireless sensor networks. Node localization algorithms are categorized into two types based on their ranging methods: range-based algorithms and range-free algorithms. Range-based algorithms primarily measure the distance or angle between nodes and calculate the coordinates of unknown nodes using methods such as trilateration or metric methods. Currently, the mainstream ranging methods include: Time of Arrival (TOA), Angle of Arrival (AOA), Time Difference of Arrival (TDOA), and Received Signal Strength Indicator (RSSI). Range-free algorithms mainly use network connectivity and trained matching models to indirectly measure location. They do not require additional hardware and are low-cost, but their localization performance is relatively poor. Typical distance-free algorithms include: Distance Vector-Hop (DV-Hop) algorithm, Centriod algorithm, Approximate Point Intriangulation (APIT) algorithm, and Multidimensional Scaling (MDS) algorithm.

[0005] Trilateration is a ranging-based localization algorithm that estimates the distance between an anchor node and an unknown node using RSSI (Reference Range Indicator Sequencing) and calculates the coordinates of the unknown node using trilateration. This algorithm has relatively low positioning costs and is suitable for various large-scale wireless sensor networks. However, its performance is limited by the ranging error of RSSI. In real-world environments, the complexity of the localization process introduces errors in RSSI ranging, meaning that the three circles measured in trilateration will never intersect at a single point, leading to a sharp decrease in positioning accuracy. Summary of the Invention

[0006] The main objective of this invention is to provide a wireless sensor network node localization method based on trilateration. By optimizing the anchor node selection method, the method introduces the geometry of the reference triangle (GRT) and the point in triangle (Point In Triangulation) criterion, which effectively improves the accuracy of target node localization.

[0007] To achieve the above objectives, this invention provides a wireless sensor network node localization method based on trilateration, comprising the following steps:

[0008] Step 1: Broadcast a data packet containing identity, location information, and transmission power to the network through each anchor node;

[0009] Step 2: The unknown node receives the broadcast data packet, obtains the anchor node's location information and transmission power, and converts the obtained transmission power into RSSI value and distance value;

[0010] Step 3: The unknown node selects the N nearest anchor nodes within its communication range and combines them to form a network. A triangle;

[0011] Step 4: Calculate the GRT value for each triangle and compare it with a pre-set threshold GRT. th Compare and exclude those with GRT values ​​greater than GRT. th A triangle;

[0012] Step 5: Perform a PIT test on the triangles that have passed the GRT threshold screening to exclude triangles that do not contain unknown nodes;

[0013] Step 6: Perform trilateration calculations on the three sides of each of the selected triangles to obtain the estimated coordinates of several unknown nodes;

[0014] Step 7: Calculate the average absolute difference of distances between the estimated coordinates of each unknown node;

[0015] Step 8: Take the average of the estimated coordinates of the unknown nodes with the smallest absolute difference in average distance among the first u nodes as the final node coordinates.

[0016] As a further improvement of the present invention, the calculation formula for converting RSSI value and distance value in step 2 is as follows:

[0017]

[0018] Where d0 is 1, P t This represents the energy emitted by the anchor node. X represents the path loss at a distance of d0 = 1. σ It is a Gaussian random variable with a mean of 0 and a variance of σ, where n is the propagation loss coefficient.

[0019] As a further improvement of the present invention, step 4 is specifically as follows: Unlike the classic trilateration algorithm, which uses the three nearest anchor nodes within the communication range of an unknown node for trilateration calculation, the reference triangle geometry GRT selects the anchor node combination based on the geometric relationship between the three anchor nodes:

[0020]

[0021] Among them, R max ,R min ,R md These represent the lengths of the longest side, the shortest side, and the middle longest side of the triangle, respectively.

[0022] As a further improvement of the present invention, step 5 specifically involves: when GRT th When the size is large, the number of triangles that pass the screening is large. Therefore, we first use the GRT threshold to exclude anchor node combinations with poor geometry, and then use the PIT criterion to further screen the anchor node combinations to exclude anchor node combinations that do not contain unknown nodes.

[0023] As a further improvement of the present invention, the basic principle of the PIT criterion is: when an unknown node moves in any direction, if there is a direction in which the movement causes the distance between the unknown node and the three anchor nodes to increase or decrease simultaneously, then it proves that the unknown node is outside the triangle; otherwise, it proves that the unknown node is inside the triangle.

[0024] As a further improvement of the present invention, step 6 specifically involves: performing three-sided measurement calculations on all triangles that have passed the screening, using the three angles of the triangle... Centered on the circle, the measured distance from the unknown node. Draw a circle with a radius of:

[0025]

[0026] Finally, the estimated coordinates of the M unknown nodes are obtained by solving the system of equations simultaneously.

[0027] As a further improvement of the present invention, step 7 includes:

[0028] Step 71: After obtaining the estimated coordinates of several unknown nodes, calculate the distance r between each estimated coordinate and the three anchor nodes. j :

[0029]

[0030] Among them, (x j ,y j () represents the coordinates of the anchor node. Represents the estimated coordinates of the unknown node;

[0031] Step 72: Calculate the absolute distance difference Δr between the estimated coordinates of each unknown node. j :

[0032]

[0033] in, This represents the measured distance between the unknown node and the anchor node.

[0034] As a further improvement of the present invention, step 7 further includes:

[0035] Step 73: Use the average distance absolute difference

[0036]

[0037] Where n = 3, and when The smaller the size, the better the positioning effect.

[0038] The beneficial effects of the present invention are as follows: The wireless sensor network node localization algorithm based on trilateration of the present invention introduces the triangle interior point criterion on the basis of the triangular geometry-based trilateration algorithm, optimizes the anchor node selection method, and thus effectively improves the accuracy and efficiency of target node localization. Attached Figure Description

[0039] Figure 1 This is a flowchart of the present invention.

[0040] Figure 2 The diagram shows the positioning error of the method of this invention and the classic Trilateral Localization (TL) and Adaptive Range-based Localization (ARBL) algorithms under different anchor node ratios.

[0041] Figure 3 The image shows the positioning error of the method of this invention and the TL and ARBL algorithms under different total number of nodes.

[0042] Figure 4 The image shows the positioning error of the method of this invention, the TL algorithm, and the ARBL algorithm under different noise standard deviations.

[0043] Figure 5 The diagram shows the runtime of the method and ARBL algorithm of this invention at different runs. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] like Figure 1 As shown, this invention proposes an improved wireless sensor network node localization algorithm based on trilateration. This method combines the ideas of GRT and PIT, addressing the errors generated by trilateration algorithms in complex environments, and improves the anchor node selection process, effectively enhancing node localization accuracy and efficiency. The main steps of the algorithm are as follows:

[0046] Step 1: Broadcast a data packet containing identity, location information, and transmission power to the network through each anchor node;

[0047] Step 2: The unknown node receives the broadcast data packet, obtains the anchor node's location information and transmission power, and converts the obtained transmission power into RSSI value and distance value;

[0048] Step 3: The unknown node selects the N nearest anchor nodes within its communication range and combines them to form a network. A triangle;

[0049] Step 4: Calculate the GRT value for each triangle and compare it with a pre-set threshold GRT. th Compare and exclude those with GRT values ​​greater than GRT. th A triangle;

[0050] Step 5: Perform a Point-In-Triangulation (PIT) test on the triangles that have passed the GRT threshold screening to exclude triangles that do not contain unknown nodes;

[0051] Step 6: Perform trilateration calculations on the three sides of each of the selected triangles to obtain the estimated coordinates of several unknown nodes;

[0052] Step 7: Calculate the average absolute difference of distances between the estimated coordinates of each unknown node;

[0053] Step 8: Take the average of the estimated coordinates of the unknown nodes with the smallest absolute difference in average distance among the first u nodes as the final node coordinates.

[0054] The formula for converting RSSI values ​​and distance values ​​in step 2 is as follows:

[0055]

[0056] Where d0 is 1, P t This represents the energy emitted by the anchor node. X represents the path loss at a distance of d0 = 1. σ It is a Gaussian random variable with a mean of 0 and a variance of σ, where n is the propagation loss coefficient.

[0057] Step 4 specifically involves: Unlike the classic trilateration algorithm which uses the three nearest anchor nodes within the communication range of an unknown node for trilateration calculations, the reference triangle geometry GRT selects the anchor node combination based on the geometric relationship between the three anchor nodes.

[0058]

[0059] Among them, R max ,R min ,R md Let GRT represent the lengths of the longest, shortest, and middle longest sides of the triangle, respectively, and let GRT ∈ [0.5, 1]. When GRT = 0.5, the triangle formed by the anchor nodes is an equilateral triangle, which has the best geometric shape. When GRT = 1, the three anchor nodes are collinear, which has the worst geometric shape. This effectively solves the problem of the trilateration algorithm having a single method for selecting anchor nodes during positioning.

[0060] Step 5 specifically involves: when GRT th When the size is large, the number of triangles that pass the screening is large. Therefore, the GRT threshold is first used to exclude anchor node combinations with poor geometry, and then the PIT criterion is used to further filter the anchor node combinations, excluding those that do not contain the unknown node. The basic principle of the PIT criterion is: when the unknown node moves in any direction, if there is a direction in which the movement causes the distance between the unknown node and the three anchor nodes to increase or decrease simultaneously, then it proves that the unknown node is outside the triangle; otherwise, it proves that the unknown node is inside the triangle.

[0061] Step 6 specifically involves: performing three-sided measurements on all triangles that passed the screening, using the three angles of the triangle as the basis for the calculation. Centered on the circle, the measured distance from the unknown node. Draw a circle with a radius of:

[0062]

[0063] Finally, the estimated coordinates of the M unknown nodes are obtained by solving the system of equations simultaneously.

[0064] Step 7 specifically involves:

[0065] Step 71: After obtaining the estimated coordinates of several unknown nodes, calculate the distance r between each estimated coordinate and the three anchor nodes. j :

[0066]

[0067] Among them, (x j ,y j () represents the coordinates of the anchor node. Represents the estimated coordinates of the unknown node;

[0068] Step 72: Calculate the absolute distance difference Δr between the estimated coordinates of each unknown node. j :

[0069]

[0070] in, This represents the measured distance between the unknown node and the anchor node; the smaller the absolute difference in distance, the higher the positioning accuracy.

[0071] Step 73: To achieve the best positioning results, we use the average absolute difference of distance.

[0072]

[0073] Where n = 3, and when The smaller the size, the better the positioning effect.

[0074] The formula used in step 8 is:

[0075] The absolute positioning errors of the wireless sensor network node localization algorithm based on trilateration of the present invention were compared with those of existing algorithms under different anchor node ratios, different total number of nodes, and different noise standard deviations. The experimental parameters were selected as follows:

[0076] 60-140 nodes are randomly distributed within a 100×100 square area. The communication radius of unknown nodes and anchor nodes is 35 meters. The proportion of anchor nodes is 15%-35%, and the noise standard deviation is 1-3. Furthermore, the parameter N in the algorithm of this invention is set to 6, and GRT... th=0.95, u=6. To further reduce randomness errors, all simulation results were obtained by running the simulation 100 times under the same conditions.

[0077] Experiment 1: Compare the positioning errors of the algorithm of this invention with the TL algorithm and ARBL algorithm under different anchor node ratios.

[0078] from Figure 2 It can be seen that within a 100×100 square area, comparing the positioning error of the algorithm of this invention with other trilateration algorithms under different anchor node ratios, where the communication radius is fixed at 35 meters, the total number of nodes is fixed at 100, and the noise standard deviation is fixed at 2, it can be found that when the anchor node ratio is 25%, the positioning error of the algorithm of this invention is 1.45 meters and 3.76 meters lower than that of the TL algorithm and ARBL algorithm, respectively.

[0079] Experiment 2: Compare the positioning errors of the algorithm of this invention with the TL algorithm and ARBL algorithm under different total number of nodes.

[0080] from Figure 3 It can be seen that within a 100×100 square area, when the positioning error of the algorithm of this invention is compared with other trilateration algorithms under different total number of nodes, with the communication radius fixed at 35 meters, the anchor node ratio fixed at 20%, and the noise standard deviation fixed at 2, it can be found that when the total number of nodes is 100, the positioning error of the algorithm of this invention is 1.4 meters and 4.8 meters lower than that of the TL algorithm and ARBL algorithm, respectively.

[0081] Experiment 3: Compare the positioning errors of the algorithm of this invention with the TL algorithm and ARBL algorithm under different noise standard deviations.

[0082] from Figure 4 It can be seen that within a 100×100 square area, the positioning error of the algorithm of this invention is compared with other trilateration algorithms under different total number of nodes. The anchor node ratio is fixed at 20% and the communication radius is fixed at 35 meters. It can be found that when the noise standard deviation is 2.5, the positioning error of the algorithm of this invention is 1.55 meters and 5.02 meters lower than the TL algorithm and ARBL algorithm, respectively.

[0083] Experiment 4: Compare the running time of the algorithm of this invention and the ARBL algorithm under different runs.

[0084] from Figure 5 It can be seen that within a 100×100 square area, comparing the running time of the algorithm of this invention with that of the ARBL algorithm under different number of positioning attempts, where the anchor node ratio is fixed at 20% and the communication radius is fixed at 35 meters, it can be found that the running time of the algorithm of this invention is shorter than that of the ARBL algorithm under any number of attempts.

[0085] In summary, the improved wireless sensor network node localization algorithm based on trilateration of the present invention effectively improves the accuracy and efficiency of target node localization by introducing the triangle interior point criterion on the basis of the triangular geometry-based trilateration algorithm and optimizing the anchor node selection method.

[0086] This invention utilizes the GRT and PIT criteria to select the optimal anchor node for trilateration, effectively improving positioning accuracy. This makes the invention applicable to most wireless sensor networks and effectively solves the problem of poor node positioning accuracy caused by environmental factors.

[0087] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A wireless sensor network node localization method based on trilateration, characterized in that, Includes the following steps: Step 1: Broadcast a data packet containing identity, location information, and transmission power to the network through each anchor node; Step 2: The unknown node receives the broadcast data packet, obtains the anchor node's location information and transmission power, and converts the obtained transmission power into RSSI value and distance value; Step 3: The unknown node selects the N nearest anchor nodes within its communication range and combines them to form a network. A triangle; Step 4: Calculate the GRT value for each triangle and compare it with a pre-set threshold GRT. th Compare and exclude those with GRT values ​​greater than GRT. th The reference triangle geometry (GRT) selects the anchor node combination based on the geometric relationship between the three nearest anchor nodes within the communication range of the unknown node, unlike the classic triangular localization algorithm which calculates the three sides of the unknown node's triangle. Among them, R max ,R min ,R md These represent the lengths of the longest side, the shortest side, and the middle longest side of the triangle, respectively. Step 5: Perform a PIT test on the triangles that have passed the GRT threshold screening to exclude triangles that do not contain unknown nodes; Step 6: Perform trilateration calculations on the three sides of each of the selected triangles to obtain the estimated coordinates of several unknown nodes; Step 7: Calculate the average absolute difference of distances between the estimated coordinates of each unknown node; Step 8: Take the average of the estimated coordinates of the unknown nodes with the smallest absolute difference in average distance among the first u nodes as the final node coordinates.

2. The method according to claim 1, characterized in that, The formulas for converting RSSI values ​​and distance values ​​in step 2 are as follows: Where d0 is 1, P t This represents the energy emitted by the anchor node. Xσ represents the path loss at a distance of d0 = 1, where Xσ is a Gaussian random variable with a mean of 0 and a variance of σ, and n is the propagation loss coefficient.

3. The method according to claim 1, characterized in that, Step 5 specifically involves: when GRT th When the size is large, the number of triangles that pass the screening is large. Therefore, we first use the GRT threshold to exclude anchor node combinations with poor geometry, and then use the PIT criterion to further screen the anchor node combinations to exclude anchor node combinations that do not contain unknown nodes.

4. The method according to claim 3, characterized in that: The basic principle of the PIT criterion is: when an unknown node moves in any direction, if there exists a direction in which the distance between the unknown node and the three anchor nodes increases or decreases simultaneously, then it proves that the unknown node is outside the triangle; otherwise, it proves that the unknown node is inside the triangle.

5. The method according to claim 1, characterized in that, Step 6 specifically involves: performing three-sided measurements on all triangles that passed the screening, and calculating the three angles of the triangles. Centered on the circle, the measured distance from the unknown node. Draw a circle with a radius of: Finally, the estimated coordinates of the M unknown nodes are obtained by solving the system of equations simultaneously.

6. The method according to claim 1, characterized in that, Step 7 includes: Step 71: After obtaining the estimated coordinates of several unknown nodes, calculate the distance r between each estimated coordinate and the three anchor nodes. j : Among them, (x j ,y j () represents the coordinates of the anchor node. Represents the estimated coordinates of the unknown node; Step 72: Calculate the absolute distance difference Δr between the estimated coordinates of each unknown node. j : in, This represents the measured distance between the unknown node and the anchor node.

7. The method according to claim 6, characterized in that, Step 7 further includes: Step 73: Using the average distance absolute difference Where n = 3, and when The smaller the size, the better the positioning effect.

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