Sensor power distribution method and device for optimizing real-time position and energy of unmanned aerial vehicle

Abstract

According to the sensor power distribution method and device for optimizing the real-time position and energy of the unmanned aerial vehicle, based on the physical layer safety theory, the spatial position of the unmanned aerial vehicle is optimized in real time, more sensors are ensured to be located in the communication coverage range of the unmanned aerial vehicle, and the real-time position and energy of the unmanned aerial vehicle are optimized through a sensor selection strategy. It is ensured that data sent by the selected sensor is safe; and then, the energy efficiency is taken as a performance index, and power distribution with optimal energy efficiency is carried out on the selected sensor, so that the sensor can send the stored sensing data to the unmanned aerial vehicle as much as possible under limited energy. According to the method, the problems of data safety and energy limitation in the data acquisition process of the sensor based on the unmanned aerial vehicle can be effectively solved, and the energy utilization efficiency of the sensor is improved to the maximum extent on the premise of ensuring the safety of data transmission through the technical means of real-time optimization of the position of the unmanned aerial vehicle, safe sensor selection, sensor power distribution and the like.

Description

Technical Field

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

[0002] In a distributed wireless sensor network that is deployed temporarily or for a short period of time, sensor data collection is a practical problem that needs to be solved. Due to the advantages of flexible deployment, good controllability, and low cost, using drones for sensor data collection is a good application example.

[0003] However, wireless sensor data collection based on drones needs to consider data security issues and limited sensor energy during the collection process. Due to the broadcast characteristics of ground-to-air wireless links, the data sent by sensors may be received by potential illegal users, which makes data collection have potential information security issues. On the other hand, sensors are generally powered by batteries, which have very limited energy, which will affect the duration of sensor data transmission.

[0004] Generally, wireless sensor network data security mostly adopts traditional encryption technology based on cryptographic theory, which has problems such as high complexity of key generation and management and large amount of calculation. For the problem of limited sensor energy, the traditional method is to send data with the lowest possible power to save energy, but the data rate will also be very low. This power strategy is not suitable for drone-based data collection because drone data collection has a mission time limit. 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 real-time position and energy optimization of UAVs is based on physical layer security theory. It uses the good maneuverability and controllability of UAVs to provide spatial freedom for security design. It optimizes the spatial position of UAVs in real time to ensure that more sensors are within the communication coverage of UAVs. And through sensor selection strategy, it ensures that the selected sensors send data safely.

[0008] Then, taking energy efficiency as the 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.

[0009] On the other hand, 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, 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 executes the steps of the above method.

[0011] It can be seen from the above technical solutions that the sensor power allocation method for real-time position and energy optimization of drones of the present invention, especially through real-time position optimization of drones and optimal power allocation of sensors, improves the security of data collection and the energy utilization efficiency of sensors. Specifically, the present invention aims at the data security problems and sensor energy limitation problems existing in the process of using drones to collect wireless sensor data, and proposes technical means based on physical layer security theory, such as real-time optimization of drone spatial position, safe sensor selection and optimal power allocation, to ensure the security of collected data and high energy utilization efficiency of sensors.

[0012] Based on the physical layer security theory, the present invention uses the good mobility and controllability of drones to provide spatial freedom for security design. By optimizing the spatial position of drones in real time, more sensors are ensured to be within the communication coverage of drones, and the sensor selection strategy is used to ensure that the data sent by the selected sensors is safe. Then, with energy efficiency as the performance indicator, the selected sensors are allocated power with the best energy efficiency, so that the sensors can send as much stored perception data as possible to the drones under limited energy.

[0013] The key points of the present invention are to cover the optimal real-time optimization algorithm for the spatial position of the UAV, 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 the data security and energy limitation issues in the process of sensor data acquisition based on drones. 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 in 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 It is a schematic diagram of the energy efficiency of confidentiality transmission corresponding to the sensor when the position of the eavesdropper changes according to an embodiment of the present invention;

[0019] Figure 5 This is a schematic diagram of the confidentiality transmission rate corresponding to the sensor when the position of the eavesdropper changes according to an embodiment of the present invention;

[0020] Figure 6 Schematic diagram of the total power corresponding to the sensor when the position of the eavesdropper changes 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 drone 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 drone for security design, optimizing the spatial position of the drone in real time, ensuring that more sensors are within the communication coverage of the drone, and ensuring that the data sent by the selected sensor is safe through a sensor selection strategy;

[0023] Then, taking energy efficiency as the 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.

[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, through technical means such as real-time optimization of the drone's spatial position, safe sensor selection and optimal power allocation, data security is ensured while maximizing the utilization efficiency of the sensor's limited energy.

[0025] exist Figure 2 In the process of collecting sensor data, illegal users may eavesdrop on the data. 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 n=1,2,…,N. 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 to model the channel. 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 constraints on the flight altitude of the drone; 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, covering the optimal drone position. The specific steps are shown in Algorithm 1.

[0040] Algorithm 1: Real-time UAV Position Optimization with Optimal Coverage

[0041]

[0042]

[0043] (2) Ensure safe and effective sensor selection

[0044] 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

[0045]

[0046] (3) Energy-efficient sensor power distribution

[0047] Based on the optimal position of the drone (x * ,y * ,z * ) and the selected sensor set Ω, the energy-efficient sensor power allocation problem is modeled as follows:

[0048]

[0049] 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.

[0050] The steps to solve the above problem (8) are as follows:

[0051] 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:

[0052]

[0053] The superscript (j) indicates the number of external iterations. The optimal solution to problem (9) is Indicates that it will be used in the next iteration during the loop.

[0054] ② Problem (9) needs further solution. Use its lower bound function Substituting, we get the following problem:

[0055]

[0056] 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. represents the optimal solution of problem (10) obtained at the kth iteration, which is used in the next iteration. The corresponding optimal solution of problem (10) is expressed as In this way, the optimal solution to 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.

[0057] Algorithm 2: Energy-efficient sensor power allocation

[0058]

[0059]

[0060] In order to verify the performance of the present invention, the data collection scheme of 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 collection scheme of real-time optimization of UAV position combined with sensor selection and fixed power allocation. Considering that sensors are randomly distributed in 1000×1000m 2 The eavesdropper is on line y e =x e The aircraft flies at a fixed altitude of 120 m. The simulation parameters are shown in Table 1.

[0061] Table 1 Simulation parameters

[0062] parameter Numeric <![CDATA[γ0]]> -20dB <![CDATA[σ 2 ]]> -100dBm N 20 <![CDATA[p max ]]> 1W <![CDATA[z max ]]> 1000m <![CDATA[z min ]]> 100m

[0063] As the eavesdropper's position changes, the position of each communication terminal changes as follows: Figure 3As shown in the figure. The simulation results show that when the eavesdropper's position changes, the drone always maintains the lowest allowed altitude. In order to clearly observe the position change, the three-dimensional positions of the drone and the eavesdropper are projected onto the horizontal plane. Obviously, as the eavesdropper's position changes, the drone will adaptively adjust its position to ensure that more sensors are covered within the safe transmission range. It can be seen from the simulation results that considering the security of communication coverage, the drone's position is affected by both the sensor position and the eavesdropper's position.

[0064] 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 confidentiality transmission may result in a lower confidentiality 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 consumes less power, 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 confidentiality transmission energy efficiency and confidentiality transmission rate also vary with the eavesdropper's position.

[0065] On the other hand, 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.

[0066] On the other hand, the present invention further 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 executes the steps of the above method.

[0067] In another embodiment provided in the present application, a computer program product comprising instructions is also provided. When the computer program product is executed on a computer, the computer executes the sensor power allocation method for real-time position and energy optimization of any drone in the above embodiments.

[0068] 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 in the above methods.

[0069] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, 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 may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may 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 may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may 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 integrated. The available medium may 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 Solid State Disk (SSD)), etc.

[0070] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0071] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0072] 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 the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. 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 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 the 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 communication coverage of drones. And through the sensor selection strategy, it is ensured that the data sent by the selected sensors is safe. Then, taking energy efficiency as the 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.

2. The sensor power allocation method for real-time position and energy optimization of unmanned aerial vehicles according to claim 1 is characterized by: By optimizing the spatial position of the drone in real time, more sensors are ensured to be within the drone communication coverage, 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 is no obstacle 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 constraints on the flight altitude of the drone; 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; 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 * ), i.e., the drone position with the best coverage.

3. The sensor power allocation method for real-time position and energy optimization of unmanned aerial vehicles according to claim 2 is characterized by: By optimizing the spatial position of the drone in real time, more sensors can be ensured to be within the drone 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, given 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) satisfies the convergence condition. If so, return the optimal solution (x) of problem (5). * ,y * ,z * )=(x (i) ,y (i) ,z (i) ), otherwise go to step S13 and iterate until convergence.

4. The sensor power allocation method for real-time position and energy optimization of unmanned aerial vehicles according to claim 3 is characterized by: 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 satisfy the positive confidentiality rate That is, if the distance corresponding to the nth sensor satisfies should be selected to activate and send data; therefore, the selected sensor set is expressed as 5. The sensor power allocation method for real-time position and energy optimization of unmanned aerial vehicles according to claim 4 is characterized by: 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 drone under limited energy. Based on the optimal position of the drone (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) indicates 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 get the following problem: Given the optimization variable p n Initial value of Problem (9) is solved by iterative loop to obtain problem (10); where the superscript (k) represents the number of internal iteration loops, represents the optimal solution of problem (10) obtained at the kth iteration, which is used in the next iteration. The corresponding optimal solution of problem (10) is expressed as In this way, the optimal solution to problem (9) is obtained by iteratively solving problem (10) until convergence. That is, the optimal transmit power of the sensor.

6. The sensor power allocation method for real-time position and energy optimization of unmanned aerial vehicles according to claim 5, characterized in that: Taking energy efficiency as the performance indicator, the selected sensors are allocated power with the best 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 n∈Ω, k:=0; S25, k:=k+1; S26. For a given Γ (j-1) and Solve problem (10) to obtain 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) satisfies the convergence condition. If so, return the optimal solution of problem (8). Otherwise, go to step S23 and iterate until the solution to problem (8) converges.

7. 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 6.

8. 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 method according to any one of claims 1 to 6.

Images