Unmanned aerial vehicle position and sensor power joint optimization method, device and storage medium

Abstract

The unmanned aerial vehicle position and sensor power joint optimization method, device and storage medium of the present application comprise the following steps: defining the security transmission energy utilization efficiency of data acquisition, establishing a mathematical optimization model of energy efficiency optimization considering the constraints of data security, maximum power and flight height, and giving the solution process of the problem, and then realizing the joint optimization of the real-time position of the unmanned aerial vehicle and the sensor transmission power. Through the joint optimization of the real-time position of the unmanned aerial vehicle and the sensor transmission power, the safety of sensor data acquisition is ensured, and the energy utilization efficiency of the sensor is improved as much as possible.

Description

Technical Field

[0001] This invention relates to the field of distributed wireless sensor network data acquisition technology based on unmanned aerial vehicles (UAVs), specifically to a method, device, and storage medium for joint optimization of UAV position and sensor power. Background Technology

[0002] Drones possess advantages such as high mobility, good controllability, flexible deployment, and low operating costs, making them promising candidates for data acquisition in distributed wireless sensor networks. However, using drones to collect sensor data faces two challenges. First, there's the issue of data transmission security, particularly the potential for unauthorized eavesdropping, tampering, or destruction of sensitive business data. Second, there's the limited energy of sensors. Since sensors are typically powered by batteries with limited energy, they are usually in low-power or dormant states, storing the perceived data in their own memory and only activating to transmit data during acquisition. How to enable sensors to transmit more data with limited energy is a problem that needs to be considered and solved.

[0003] Typically, wireless sensor network data security relies heavily on traditional encryption techniques based on cryptographic theory, which suffer from high complexity in key generation and management, as well as large computational demands. To address the limited energy of sensors, traditional methods use the lowest possible power to transmit data, conserving energy, but this results in very low data transmission rates. This transmission strategy is unsuitable for drone-based data acquisition, as drone data acquisition has strict time constraints. Summary of the Invention

[0004] The present invention proposes a method, device, and storage medium for joint optimization of UAV position and sensor power, which can at least solve one of the technical problems in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for joint optimization of UAV position and sensor power includes establishing an energy-efficient mathematical optimization model that considers constraints such as data security, maximum power, and flight altitude by defining the energy utilization efficiency of secure data acquisition transmission; the solution process is also given; and thus, joint optimization of UAV real-time position and sensor transmission power is achieved.

[0007] Furthermore, by defining the energy utilization efficiency of secure data acquisition transmission, a mathematical optimization model for optimal energy efficiency was established, taking into account constraints such as data security, maximum power, and flight altitude. Specifically, this includes...

[0008] Let the three-dimensional position coordinates of the drone and the eavesdropper be represented as (x, y, z) and (x, y, z) respectively. e ,y e ,ze There are I sensors in total, and the position coordinates of the i-th sensor are represented as (u i ,v i ,0), i=1,2,…,I; In practical applications, the drone locates the sensors and the eavesdropper to obtain their positions; the distances from the i-th sensor to the drone and the eavesdropper are respectively expressed as:

[0009]

[0010] Based on physical layer security technology, and considering sensor power constraints and flight altitude constraints, the following optimization problem is established with the goal of maximizing sensor energy efficiency:

[0011]

[0012] Where d i and l i As given in equations (1) and (2); p i This represents the transmission power of the i-th sensor; β0 = α0 / σ 2 α0 represents the channel power gain at a reference distance of 1 meter; σ 2 p represents the variance of Gaussian white noise; max This indicates the sensor's maximum power constraint; z min and z max These represent the minimum and maximum permitted flight altitudes for the drone; for ease of representation, X and P represent the drone's position vector and the sensor's power vector, respectively. X and P are optimization variables.

[0013] Furthermore, the problem-solving process specifically includes,

[0014] ① By introducing an auxiliary variable Γ, and giving Γ an initial value Γ (0) The optimal solution X of problem (3) * and P * The following problem is obtained by solving iteratively:

[0015]

[0016] Where the superscript (j) indicates the number of iterations, and the optimal solution to problem (4) is represented by X. (j) and P (j) This indicates that it will be used in the next iteration during the loop;

[0017] ② Problem (4) Equivalent transformation; introduction of slack variables and B = (b1, b2, ..., b I Problem (4) is equivalently transformed into the following optimization problem:

[0018]

[0019] The optimal solution to problem (4) is obtained by solving problem (5);

[0020] ③ Solve problem (5) iteratively; for a set of given values ​​corresponding to the optimization variables X, P, A, B Problem (5) can be approximated as the following problem:

[0021]

[0022] Where the superscript (n) represents the number of iterations;

[0023]

[0024] Given a set of initial values ​​for the optimization variables X, P, A, B By iteratively solving problem (6), the optimal solution to problem (5) can be obtained; In fact, the optimal solution to problem (6) obtained in the nth iteration will be used in the next iteration, and the corresponding optimal solution to problem (6) is expressed as: Thus, the optimal solution to problem (5) is obtained by iteratively solving problem (6) until convergence.

[0025] Furthermore, the solution process for the problem, specifically the implementation steps, are as follows:

[0026] S1, Input the three-dimensional position coordinates (x, y) of the eavesdropper and the sensor. e ,y e ,z e ) and (u i ,v i ,0), i=1,2,…,I;

[0027] S2, Given the initial value of the auxiliary variable Γ. (0) j:=0;

[0028] S3, j:=j+1;

[0029] S4. Given a set of initial values ​​for the optimization variables X, P, A, B. n:=0;

[0030] S5, n:=n+1;

[0031] S6, for a given Γ (j-1) and Solving problem (6) yields its optimal solution.

[0032]

[0033] S7. Determine whether the increment of the objective function value of problem (4) meets the convergence condition. If it does, continue to the next step. Otherwise, go to step S5 and iterate until the solution of problem (4) converges.

[0034] S8 Using X (j) and P (j) Find the objective function value of the original problem (3);

[0035] S9. Determine whether the increment of the objective function value of problem (3) satisfies the convergence condition. If it does, return the optimal solution X of problem (3). * :=X (j) P * :=P (j) Otherwise, proceed to step S3 and iterate until the solution to problem (3) converges.

[0036] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0037] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0038] As can be seen from the above technical solution, the UAV position and sensor power joint optimization method of the present invention ensures the security of sensor data acquisition and maximizes the energy utilization efficiency of the sensor by jointly optimizing the real-time position of the UAV and the sensor transmission power. Specifically, the present invention, based on physical layer security technology, comprehensively considers the security and energy limitations of sensor data acquisition, and achieves the goal of ensuring data transmission security while maximizing the energy utilization efficiency of the sensor by jointly optimizing the real-time position of the UAV and the sensor transmission power.

[0039] The key points of this invention are: by defining the energy utilization efficiency of secure data acquisition transmission, a mathematical optimization problem for optimal energy efficiency considering constraints such as data security, maximum power, and flight altitude is established; the solution process of the problem is given; and a joint optimization algorithm for UAV real-time position and sensor transmission power is proposed.

[0040] The advantages of this invention are: based on physical layer technology, it proposes a joint solution to the data security and energy limitation problems, and through joint optimization of UAV real-time location and sensor transmission power, it maximizes the energy utilization efficiency of sensors while ensuring data transmission security. Attached Figure Description

[0041] Figure 1 This is a flowchart of the present invention;

[0042] Figure 2 This invention provides a scenario for wireless sensor network data acquisition involving eavesdroppers.

[0043] Figure 3 The horizontal positions of all terminals when the position of the eavesdropper changes according to an embodiment of the present invention;

[0044] Figure 4 The secure transmission energy efficiency of the sensor when the location of the eavesdropper changes according to an embodiment of the present invention;

[0045] Figure 5 This refers to the security rate throughput of the sensor when the location of the eavesdropper changes according to an embodiment of the present invention.

[0046] Figure 6 This represents the total power of the sensor when the eavesdropper's position changes according to an embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0048] like Figure 1 As shown, an embodiment of the present invention provides a method for joint optimization of UAV position and sensor power, which includes defining the energy utilization efficiency of secure data acquisition transmission, establishing a mathematical optimization model that considers constraints such as data security, maximum power, and flight altitude; and providing the solution process for the problem; thereby achieving joint optimization of UAV real-time position and sensor transmission power.

[0049] Specifically, the embodiments of the present invention consider practical scenarios in which drones are used to collect distributed wireless sensor data, such as... Figure 2 As shown, by jointly optimizing the spatial position of the drone and the transmission power of the sensors, data security can be ensured while maximizing the utilization efficiency of the limited energy of the sensors.

[0050] exist Figure 2 In this context, during the process of collecting sensor data, drones may be subject to unauthorized eavesdropping. The three-dimensional position coordinates of the drone and the eavesdropper are represented as (x, y, z) and (x+y+z), respectively. e ,y e ,z e There are I sensors in total, and the position coordinates of the i-th sensor are represented as (u... i ,v i(i, 0), i = 1, 2, ..., I. In practical applications, the drone locates sensors and eavesdroppers to obtain their positions. The distances from the i-th sensor to the drone and the eavesdropper can be expressed as:

[0051]

[0052] (1) Modeling the energy efficiency optimization problem based on physical layer security

[0053] Based on physical layer security technology, and considering sensor power constraints and flight altitude constraints, the following optimization problem is established with the goal of maximizing sensor energy efficiency:

[0054]

[0055] Where d i and l i As given in equations (1) and (2); p i This represents the transmission power of the i-th sensor; β0 = α0 / σ 2 α0 represents the channel power gain at a reference distance of 1 meter; σ 2 p represents the variance of Gaussian white noise; max This indicates the sensor's maximum power constraint; z min and z max These represent the minimum and maximum permitted flight altitudes for the drone; for ease of representation, X and P represent the drone's position vector and the sensor's power vector, respectively. X and P are optimization variables.

[0056] (2) Solution method for energy efficiency optimization problem

[0057] ① By introducing an auxiliary variable Γ, and giving Γ an initial value Γ (0) The optimal solution X of problem (3) * and P * The following problem can be solved by iteratively solving it:

[0058]

[0059] Where the superscript (j) indicates the number of iterations, and the optimal solution to problem (4) is represented by X. (j) and P (j) This indicates that it will be used in the next iteration during the loop.

[0060] ② Equivalent transformation of problem (4). Introduce slack variables. and B = (b1, b2, ..., b I Problem (4) is equivalently transformed into the following optimization problem:

[0061]

[0062] The optimal solution to problem (4) can be obtained by solving problem (5).

[0063] ③ Solve problem (5) iteratively. For a set of given values ​​corresponding to the optimization variables X, P, A, B Problem (5) can be approximated as the following problem

[0064]

[0065] Where the superscript (n) represents the number of iterations;

[0066]

[0067] Given a set of initial values ​​for the optimization variables X, P, A, B By iteratively solving problem (6), the optimal solution to problem (5) can be obtained. In fact, the optimal solution to problem (6) obtained in the nth iteration will be used in the next iteration, and the corresponding optimal solution to problem (6) is expressed as: Thus, the optimal solution to problem (5) can be obtained by iteratively solving problem (6) until convergence.

[0068] The specific steps of the original problem (3) are shown in Algorithm 1:

[0069] Algorithm 1: Joint Optimization Algorithm for UAV Real-Time Position and Sensor Transmit Power

[0070]

[0071]

[0072] In summary, this invention, based on physical layer security technology, comprehensively considers the security and energy limitations of sensor data acquisition. By jointly optimizing the real-time position of the UAV and the sensor transmission power, it aims to ensure data transmission security while maximizing sensor energy utilization efficiency.

[0073] To verify the performance of the algorithm in this invention, the joint optimization algorithm for UAV real-time position and sensor transmission power proposed in this invention is compared with the maximum distance difference minimization algorithm with fixed power. The basic idea of ​​the latter algorithm is to optimize the UAV position to cover more sensors, and then activate the sensors with positive secure transmission rates to transmit data at maximum power. Consider a 1000×1000m... 2 In this context, consider a region where sensors are arranged in a regular grid pattern to more comprehensively measure certain data. Also, consider that the eavesdropper's movement trajectory is a curve to better intercept data. Its flight altitude is fixed at 70m. It should be noted that the algorithm proposed in this invention is applicable to any sensor distribution and the location of the eavesdropper. Simulation parameters are shown in Table 1 below.

[0074] Table 1 Simulation Parameters

[0075]

[0076]

[0077] As the location of the eavesdropper changes, the locations of the various communication terminals change as follows: Figure 3 As shown in the figure. Simulation results show that the drone always maintains the minimum permissible flight altitude when the eavesdropper's position changes. To clearly observe the position changes, the three-dimensional positions of the drone and the eavesdropper are projected onto a horizontal plane. The simulation results demonstrate that, to ensure data security, the drone position obtained by both algorithms adaptively adjusts to the eavesdropper's position as the eavesdropper's position changes.

[0078] Figure 4 The energy efficiency of two algorithms was compared as the eavesdropper's position changed. The horizontal coordinate reflects the change in the eavesdropper's position. Figure 4 As can be seen, the energy efficiency of the joint optimization algorithm proposed in this invention is significantly higher than that of the maximum distance difference minimization algorithm. At the extreme ends of the energy efficiency curve, the joint optimization algorithm achieves higher secure transmission energy efficiency. It is worth noting that the secure rate throughput of the joint optimization algorithm is also higher than that of the maximum distance difference minimization algorithm, as... Figure 5 As shown. However,

[0079] However, the total power consumed by joint optimization is not always greater than the total power consumed by the maximum distance difference minimization algorithm. For example... Figure 6 As shown in the total power curve, at the two extremes of the curve, the joint optimization algorithm consumes less power but achieves higher secure throughput and secure transmission energy efficiency.

[0080] exist Figure 6 In the simulation, the power curve obtained by the maximum distance difference minimization algorithm fluctuates significantly. This is because sometimes only a portion of the sensors are selected for data transmission, while the remaining sensors remain dormant to conserve energy. However, from the perspective of secure transmission energy efficiency, this simple sensor energy-saving selection strategy is not optimal. Due to the global optimization of UAV position and sensor power, the joint optimization algorithm can achieve higher secure transmission energy efficiency. It is worth noting that the joint optimization algorithm also has an implicit sensor selection function. Simulation results show that if the channel quality of a sensor is too poor to support the requirements of secure data transmission, no power will be allocated to it; that is, the sensor will not be activated and will remain in a dormant state.

[0081] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0082] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0083] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the UAV position and sensor power joint optimization methods described in the above embodiments.

[0084] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0085] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0086] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0087] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0088] 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for joint optimization of UAV position and sensor power, characterized in that, This includes establishing a mathematical optimization model for optimal energy efficiency by defining the energy utilization efficiency of secure data acquisition transmission, taking into account constraints such as data security, maximum power, and flight altitude; the solution process is also given; and joint optimization of UAV real-time position and sensor transmission power is achieved. By defining the energy utilization efficiency of secure data acquisition transmission, a mathematical optimization model for optimal energy efficiency was established, taking into account constraints such as data security, maximum power, and flight altitude. Specifically, this includes... Let the three-dimensional position coordinates of the drone and the eavesdropper be represented as follows: and ; Total The sensor, the first The position coordinates of each sensor are represented as follows: , In practical applications, drones are used to locate sensors and eavesdroppers, thereby obtaining their positions; The distances from each sensor to the drone and the eavesdropper are represented as follows: （1） （2）； Based on physical layer security technology, and considering sensor power constraints and flight altitude constraints, the following optimization problem is established with the goal of maximizing sensor energy efficiency: （3） in and As given in equations (1) and (2); Indicates the first The transmission power of each sensor; , This represents the channel power gain at a reference distance of 1 meter. This represents the variance of Gaussian white noise; This indicates the sensor's maximum power constraint. and This indicates the minimum and maximum permitted flight altitudes for drones; for ease of reference, and These represent the position vector of the UAV and the power vector of the sensor, respectively. , ; and These are optimization variables.

2. The method for joint optimization of UAV position and sensor power according to claim 1, characterized in that: The solution process for the problem specifically includes, ① By introducing auxiliary variables , and given An initial value The optimal solution to problem (3) and The following problem is obtained by solving iteratively: （4） Among them, superscript The optimal solution to problem (4) is expressed as the number of iterations. and This indicates that it will be used in the next iteration during the loop; ② Problem (4) Equivalent transformation; introduction of slack variables and Problem (4) can be equivalently transformed into the following optimization problem: （5） The optimal solution to problem (4) is obtained by solving problem (5); ③ Solve problem (5) iteratively; for the optimization variables A corresponding set of given values Problem (5) can be approximated as the following problem: （6） Among them, superscript Indicates the number of iterations; ； ； ； ； Given optimization variables A corresponding set of initial values By iteratively solving problem (6), the optimal solution to problem (5) can be obtained; Actually, it's the first The optimal solution to problem (6) obtained in the next iteration will be used in the next iteration, and the optimal solution to problem (6) will be expressed as follows: Thus, the optimal solution to problem (5) is obtained by iteratively solving problem (6) until convergence.

3. The method for joint optimization of UAV position and sensor power according to claim 2, characterized in that: The solution process, specifically the implementation steps, are as follows: S1, Input the three-dimensional position coordinates of the eavesdropper and the sensor. and , ; S2, Given auxiliary variables initial value , ; S3、 ； S4. Given optimization variables A corresponding set of initial values , ; S5、 ； S6, for a given and Solving problem (6) yields its optimal solution. ; S7. Determine whether the increment of the objective function value of problem (4) meets the convergence condition. If it does, continue to the next step. Otherwise, go to step S5 and iterate until the solution of problem (4) converges. S8 , ,use and Find the objective function value of the original problem (3); S9. Determine whether the increment of the objective function value of problem (3) satisfies the convergence condition. If it does, return the optimal solution of problem (3). , Otherwise, proceed to step S3 and iterate until the solution to problem (3) converges.

4. A computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 3.

5. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as claimed in any one of claims 1 to 3.

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