Sensor power allocation method and device for real-time location and energy optimization of unmanned aerial vehicles

Abstract

The present invention provides a sensor power allocation method and device for real-time drone position and energy optimization. The method includes, based on physical layer security theory, real-time optimization of the drone's spatial position to ensure that more sensors are within the drone's communication coverage, and a sensor selection strategy to ensure that data transmitted by the selected sensors is secure. Subsequently, using energy efficiency as a performance indicator, the selected sensors are allocated power with optimal energy efficiency, enabling the sensors to transmit as much stored sensor data as possible to the drone within limited energy. This method can effectively address data security and energy limitation issues in drone-based sensor data acquisition processes. Through technical means such as real-time drone position optimization, secure sensor selection, and sensor power allocation, the method maximizes sensor energy utilization efficiency while ensuring data transmission security.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless sensor data acquisition for unmanned aerial vehicles (UAVs), and in particular to a sensor power distribution method, device, and storage medium for optimizing the real-time position and energy of UAVs. Background Art

[0002] In distributed wireless sensor networks deployed temporarily or for short periods of time, sensor data collection is a real-world problem that needs to be solved. Using drones for sensor data collection is a good example of a practical application, as they offer advantages such as flexible deployment, good controllability, and low cost.

[0003] However, wireless sensor data collection based on drones requires consideration of data security and limited sensor energy. Due to the broadcast nature of ground-to-air wireless links, sensor data could be received by potentially unauthorized users, creating potential security risks. Furthermore, sensors are typically battery-powered, which has limited energy, impacting the duration of sensor data transmission.

[0004] Traditional encryption techniques based on cryptography are often used to secure wireless sensor network data. These techniques, however, pose challenges such as high key generation and management complexity and computational complexity. To address the limited energy consumption of sensors, the traditional approach is to transmit data at the lowest possible power to conserve energy, but this also results in a very low data rate. This power strategy is unsuitable for drone-based data collection, as drones have time constraints. Summary of the Invention

[0005] The present invention proposes a sensor power allocation method, device, and storage medium for real-time position and energy optimization of a drone, which can solve at least one of the technical problems in the background technology.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A sensor power allocation method for drones based on real-time position and energy optimization is proposed. Based on physical layer security theory, it leverages the spatial freedom provided by drones' excellent maneuverability and controllability for security design. By optimizing the drone's spatial position in real time, it ensures that more sensors are within the drone's communication coverage. Furthermore, through a sensor selection strategy, it ensures that the data sent by the selected sensors is secure.

[0008] Then, taking energy efficiency as the performance indicator, the power allocation of the selected sensors is optimized for energy efficiency, so that the sensors can send as much stored perception data as possible to the UAV under limited energy.

[0009] In another aspect, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0010] On the other hand, the present invention further discloses a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0011] As can be seen from the above technical solutions, the present invention's sensor power allocation method for real-time drone position and energy optimization, particularly through real-time drone position optimization and optimal sensor power allocation, improves data acquisition security and sensor energy efficiency. Specifically, the present invention addresses the data security and sensor energy limitations inherent in the use of drones to collect wireless sensor data. Based on physical layer security theory, the method utilizes technical means such as real-time optimization of the drone's spatial position, secure sensor selection, and optimal power allocation to ensure data security and high sensor energy efficiency.

[0012] Based on physical layer security theory, this invention leverages the spatial freedom afforded by drones' excellent maneuverability and controllability for security design. By optimizing the drone's spatial position in real time, this approach ensures that more sensors are within the drone's communication coverage. Furthermore, a sensor selection strategy ensures that the data transmitted by the selected sensors is secure. Next, using energy efficiency as a performance metric, the system allocates power to the selected sensors in an energy-efficient manner, ensuring that the sensors can transmit as much stored sensor data as possible to the drone within limited energy constraints.

[0013] The key points of the present invention are the real-time optimization algorithm for the optimal spatial position of UAVs, the sensor selection strategy considering the security of data transmission, and the sensor transmission power allocation algorithm with the best energy efficiency.

[0014] The advantages of the present invention can effectively combat data security and energy limitation issues in the process of drone-based sensor data acquisition. Through technical means such as real-time optimization of drone positions, safe sensor selection and sensor power allocation, the energy utilization efficiency of sensors can be maximized while ensuring data transmission security. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flowchart of the method of the present invention;

[0016] Figure 2 A schematic diagram of a wireless sensor network data collection scenario considering data security according to an embodiment of the present invention;

[0017] Figure 3This is a schematic diagram of the horizontal positions of all terminals corresponding to changes in the position of the eavesdropper according to an embodiment of the present invention;

[0018] Figure 4 This is a schematic diagram of the energy efficiency of secure transmission corresponding to the sensor when the eavesdropper's position changes according to an embodiment of the present invention;

[0019] Figure 5 This is a schematic diagram of the confidential transmission rate of the sensor corresponding to the change of the eavesdropper's position according to an embodiment of the present invention;

[0020] Figure 6 Schematic diagram of the total power of the sensor corresponding to the change of the eavesdropper's position according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0022] like Figure 1 As shown, a sensor power allocation method for real-time position and energy optimization of a UAV according to an embodiment of the present invention includes, based on physical layer security theory, utilizing the spatial freedom provided by the good maneuverability and controllability of the UAV for security design, ensuring that more sensors are within the communication coverage of the UAV by optimizing the spatial position of the UAV in real time, and ensuring that the data sent by the selected sensors is secure through a sensor selection strategy;

[0023] Then, taking energy efficiency as the performance indicator, the power allocation of the selected sensors is optimized for energy efficiency, so that the sensors can send as much stored perception data as possible to the UAV under limited energy.

[0024] The embodiment of the present invention considers the actual scenario of using drones to collect distributed wireless sensor data, such as Figure 2 As shown in the figure, through technical means such as real-time optimization of the drone's spatial position, safe sensor selection and optimal power allocation, the efficiency of the sensor's limited energy can be maximized while ensuring data security.

[0025] exist Figure 2 In the process of collecting sensor data, the drone may be eavesdropped by illegal users. The three-dimensional position coordinates of the drone and the eavesdropper are represented as (x, y, z) and (x e ,y e ,z e ). There are N sensors in total, and the position coordinates of the nth sensor are expressed as In practical applications, drones locate sensors and eavesdroppers to obtain their locations. The distances from the nth sensor to the drone and the eavesdropper can be expressed as:

[0026]

[0027] The present invention includes three major steps, which are as follows.

[0028] (1) Real-time optimization of drone positions for optimal coverage

[0029] Considering that there are no obstacles blocking the ground-to-air wireless channel, the free space path loss model is used for channel modeling. According to the physical layer security theory, if the nth sensor is to have a positive confidentiality transmission rate, it must satisfy

[0030]

[0031] In order to enable more sensors to achieve a positive confidentiality transmission rate, the following optimization problem is established:

[0032]

[0033] Among them, z min ≤z≤z max Represents the constraint of the drone's flight altitude; It represents the difference in distance from the nth sensor to the drone and the eavesdropper. By optimizing the drone position (x, y, z), the goal is to minimize the maximum value of the distance difference corresponding to all sensors.

[0034] The steps to solve the above problem (4) are as follows:

[0035] ① By introducing a new auxiliary variable t, problem (4) can be equivalently transformed into the following problem:

[0036]

[0037] ② Problem (5) is solved by iteration. Use its upper bound function Substituting, we can get the problem (5) for a given value t of the auxiliary variable t (i) Approximate optimization problem at:

[0038]

[0039] Here, the superscript (i) indicates the i-th iteration. In this way, the optimal solution (x) of problem (5) can be obtained by iteratively solving problem (6) until convergence. * ,y * ,z *), that is, to cover the optimal drone position. The specific steps are shown in Algorithm 1.

[0040] Algorithm 1: Real-time drone position optimization with optimal coverage

[0041]

[0042] (2) Ensure safe and effective sensor selection

[0043] Only sensors that achieve a positive confidentiality rate are selected to be activated to send data to the drone, and the rest of the sensors should remain dormant to save power. That is, if the distance corresponding to the nth sensor satisfies should be selected to activate and send data. Therefore, the selected sensor set can be expressed as

[0044]

[0045] (3) Energy-efficient sensor power distribution

[0046] Based on the optimal position of the UAV (x * ,y * ,z * ) and the selected sensor set Ω, the energy-efficient sensor power allocation problem is modeled as follows:

[0047]

[0048] Among them, p n represents the transmission power of the nth sensor, p max is the maximum power constraint; μ n and is a constant, γ0 represents the channel power gain at a reference distance of 1 meter; σ 2 represents the Gaussian white noise variance.

[0049] The steps to solve the above problem (8) are as follows:

[0050] ①Introduce the auxiliary variable Γ and give it an initial value Γ (0) , the optimal solution to problem (8) The following problem can be solved by loop iteration:

[0051]

[0052] The superscript (j) represents the number of external iterations. The optimal solution to problem (9) is Indicates that it will be used for the next iteration during the loop.

[0053] ② Problem (9) needs further solution. Use its lower bound function Substituting , we can get the following problem:

[0054]

[0055] Given the optimization variable p n Initial value of Problem (9) can be solved by iterative loop to solve problem (10). The superscript (k) represents the number of internal iteration loops. The optimal solution of problem (10) obtained at the kth iteration is used in the next iteration. The optimal solution of problem (10) is expressed as In this way, the optimal solution of problem (9) can be obtained by iteratively solving problem (10) until convergence. That is, the optimal transmission power of the sensor. The specific steps are shown in Algorithm 2.

[0056] Algorithm 2: Energy-efficient sensor power allocation

[0057]

[0058] In order to verify the performance of the present invention, the data acquisition scheme of the real-time optimization of UAV position combined with sensor selection and optimal power allocation proposed in the embodiment of the present invention is compared with the data acquisition scheme of the real-time optimization of UAV position combined with sensor selection and fixed power allocation. Considering that the sensors are randomly distributed in 1000×1000m 2 In the area. The eavesdropper is on line y e =x e The aircraft flew at a fixed altitude of 120 m. The simulation parameters are shown in Table 1 below.

[0059] Table 1 Simulation parameters

[0060]

[0061]

[0062] As the eavesdropper's position changes, the position of each communication terminal changes as follows: Figure 3 As shown in the figure, the simulation results show that the drone always maintains the minimum permitted 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. Clearly, as the eavesdropper's position changes, the drone adaptively adjusts its position to ensure that more sensors are covered within the secure transmission range. The simulation results show that, considering the security of communication coverage, the drone's position is affected by both the sensor position and the eavesdropper's position.

[0063] Figure 4 The energy efficiency of two data acquisition schemes is compared when the eavesdropper's position changes. The horizontal coordinate reflects the change of the eavesdropper's position. Figure 4 It can be seen that the real-time optimization of UAV position combined with sensor selection and power optimal allocation can significantly improve the energy efficiency of data collection. However, maximizing the energy efficiency of confidential transmission may lead to a lower confidential transmission rate, as shown in Figure 5 As shown in , the confidentiality rate of the power allocation scheme with the best energy efficiency is lower than that of the fixed power scheme. Naturally, a low confidentiality rate also requires less power consumption, such as Figure 6 As shown. Figure 6 In the figure, the total power of the corresponding sensors varies greatly when the eavesdropper is in different positions. This means that the set of sensors selected to activate and send data is different when the eavesdropper's position changes. Therefore, the resulting confidential transmission energy efficiency and confidential transmission rate also vary with the eavesdropper's position.

[0064] In another aspect, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0065] On the other hand, the present invention further discloses a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0066] In another embodiment provided in the present application, a computer program product comprising instructions is also provided, which, when executed on a computer, enables the computer to execute the sensor power allocation method for real-time position and energy optimization of any drone in the above embodiments.

[0067] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts of the above methods.

[0068] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When software is used for implementation, it can be implemented in whole or in part in the form of 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, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. 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 includes one or more available media integrations. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0069] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0070] Each embodiment in this specification is described in a related manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiment is generally similar to the method embodiment, so the description is relatively simple. For related parts, refer to the description of the method embodiment.

[0071] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A sensor power allocation method for real-time position and energy optimization of unmanned aerial vehicles, characterized in that: Based on physical layer security theory, the good maneuverability and controllability of drones provide spatial freedom for security design. By optimizing the spatial position of drones in real time, more sensors are ensured to be within the drone's communication coverage. Sensor selection strategies are also used to ensure that the data sent by the selected sensors is secure. Then, using energy efficiency as a performance indicator, the selected sensors are allocated power with optimal energy efficiency, so that the sensors can send as much stored perception data as possible to the UAV under limited energy. By optimizing the spatial position of the drone in real time, more sensors are ensured to be within the drone's communication coverage, specifically including: Assume that the three-dimensional coordinates of the drone and the eavesdropper are (x, y, z) and (x e ,y e ,z e ); There are N sensors in total, and the position coordinates of the nth sensor are expressed as In practical applications, drones locate sensors and eavesdroppers to obtain their locations; The distances from the nth sensor to the drone and the eavesdropper are expressed as: Considering that there are no obstacles blocking the ground-to-air wireless channel, the free space path loss model is used for channel modeling. According to the physical layer security theory, if the nth sensor is to have a positive confidentiality transmission rate, it must satisfy In order to enable more sensors to achieve a positive confidentiality transmission rate, the following optimization problem is established: Among them, z min ≤z≤z max Represents the constraint of the drone's flight altitude; represents the difference in distance from the nth sensor to the drone and the eavesdropper. By optimizing the drone's position (x, y, z), the goal is to minimize the maximum value of the distance difference corresponding to all sensors. The steps to solve formula (4) are as follows: 1) By introducing a new auxiliary variable t, problem (4) can be equivalently transformed into the following problem: 2) Problem (5) is solved by iteration; the first constraint in problem (5) is Use its upper bound function Substituting, we can get the problem (5) for a given value t of the auxiliary variable t (i) Approximate optimization problem at: The superscript (i) indicates the i-th iteration; the optimal solution (x) of problem (5) is obtained by iteratively solving problem (6) until convergence. * ,y * ,z * ), which is the drone position with the best coverage.

2. The sensor power allocation method for real-time position and energy optimization of a UAV according to claim 1, characterized in that: By optimizing the spatial position of the drone in real time, we can ensure that more sensors are within the drone's communication coverage. The specific implementation steps are as follows: S11, input the eavesdropper's location (x e ,y e ,z e ) and sensor location S12. Give the initial value t of the auxiliary variable t (0) , i:=0; S13, i:=i+1; S14. For a given t (i-1) , solve problem (6) and get its optimal solution (x (i) ,y (i) ,z (i) ,t (i) ); S15, determine whether the increment of the objective function value of problem (5) meets the convergence condition, and if so, return the optimal solution (x * ,y * ,z * )=(x (i) ,y (i) ,z (i) ), otherwise go to step S13 and iterate until convergence.

3. The sensor power allocation method for real-time position and energy optimization of a UAV according to claim 2, characterized in that: And through the sensor selection strategy, ensure that the selected sensor sends data safely. The specific steps are as follows Only sensors that achieve a positive confidentiality rate are selected to be activated to send data to the drone, and the rest of the sensors should remain dormant to save power; The sensor must meet the following conditions to achieve a positive confidentiality rate: That is, if the distance corresponding to the nth sensor satisfies It should be selected to activate sending data; Therefore, the selected sensor set is expressed as 4. The sensor power allocation method for real-time position and energy optimization of a UAV according to claim 3, characterized in that: Taking energy efficiency as the performance indicator, the selected sensors are allocated with the best energy efficiency, so that the sensors can send as much stored perception data as possible to the UAV under limited energy. Based on the optimal position of the UAV (x * ,y * ,z * ) and the selected sensor set Ω, the energy-efficient sensor power allocation problem is modeled as follows: Among them, p n represents the transmission power of the nth sensor, p max is the maximum power constraint; μ n and is a constant, γ0 represents the channel power gain at a reference distance of 1 meter; σ 2 represents the variance of Gaussian white noise; The steps to solve the above problem (8) are as follows: ①Introduce the auxiliary variable Γ and give it an initial value Γ (0) , the optimal solution to problem (8) The following problem can be solved by loop iteration: The superscript (j) represents the number of external iterations. The optimal solution to problem (9) is Indicates that it will be used for the next iteration during the loop; ② Problem (9) needs further solution. Use its lower bound function Substituting , we can get the following problem: Given the optimization variable p n Initial value of Problem (9) is solved by iterative loop to solve problem (10); where the superscript (k) represents the number of internal iteration loops, The optimal solution of problem (10) obtained at the kth iteration is used in the next iteration. The optimal solution of problem (10) is expressed as In this way, the optimal solution of problem (9) is obtained by iteratively solving problem (10) until convergence. That is, the optimal transmission power of the sensor.

5. The sensor power allocation method for real-time position and energy optimization of a UAV according to claim 4, characterized in that: Taking energy efficiency as the performance indicator, the power allocation of the selected sensors is optimized for energy efficiency, so that the sensors can send as much stored perception data as possible to the drone under limited energy. The specific implementation steps are as follows: S21, input the optimal position of the drone (x * ,y * ,z * ) and the selected sensor set Ω; S22. Give the initial value Γ of the auxiliary variable Γ (0) , j:=0; S23, j:=j+1; S24, given power variable p n Initial value of k:=0; S25, k:=k+1; S26. For a given Γ (j-1) and Solve problem (10) to get its optimal S27, judging whether the increment of the objective function value of problem (9) satisfies the convergence condition, if so, proceeding to the next step, otherwise going to step 5 and iterating repeatedly until the solution of problem (9) converges; S28, use Find the objective function value of problem (8); S29, determine whether the increment of the objective function value of problem (8) meets the convergence condition, and if so, return the optimal solution of problem (8) Otherwise, go to step S23 and iterate until the solution of problem (8) converges.

6. A computer-readable storage device storing a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 5.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 5.

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