A high-energy-efficiency sensor data security acquisition method based on a drone

Abstract

The application discloses a high-energy-efficiency sensor data security acquisition method based on a UAV, and comprises the following steps: S1, acquiring three-dimensional coordinates of K ground sensors and three-dimensional coordinates of an eavesdropper in a collection area of the UAV within T time; S2, establishing a UAV track and rate optimization problem model according to the information acquired in step S1; S3, solving the UAV track and rate optimization problem model established in step S2; and S4, setting a secret transmission rate threshold condition according to the UAV track obtained in step S3, selecting a suitable sensor to be woken up and activated, and sending data to the UAV. The application establishes a UAV track and rate optimization problem model with the maximum secret transmission energy efficiency of data acquisition as the target under the premise of ensuring information security, and realizes the safe data acquisition with the optimal energy efficiency by solving the optimal track and flight speed.

Description

Technical Field

[0001] This invention relates to the field of data acquisition, and in particular to a high-efficiency, secure sensor data acquisition method based on unmanned aerial vehicles (UAVs). Background Technology

[0002] Drones offer low-cost, highly controllable, and easily deployable missions, making them widely applicable in 5G mobile networks and future 6G networks. Using drones for IoT sensor data collection is a prime example of drone-based 5G network applications. This not only expands the data collection range but also reduces overall energy consumption through drone flight path planning and sensor selection.

[0003] Two main issues exist in drone-based wireless sensor data acquisition: data security and energy limitations. Due to the broadcast nature of air-to-ground wireless links, data transmitted by sensors could be received by unauthorized users, posing a potential information security risk. Furthermore, drones and sensors are typically battery-powered, and the limited battery capacity of drones significantly impacts the duration of data acquisition.

[0004] How to safely and efficiently use drones for sensor data acquisition under limited battery energy constraints is a significant challenge. To ensure sensor data security, physical layer security, as an optional technology, can flexibly adapt to changes in wireless channels and transmission environments through optimized information transmission strategy design, thus achieving information security. In scenarios where drones are used to collect sensor data, the fully controllable mobility and flexibility of drones provide a new perspective for physical layer security transmission design, namely, achieving safe and efficient data acquisition through optimized drone flight path design.

[0005] Many studies have focused on improving secure transmission rates through trajectory optimization or power allocation. However, they haven't considered the impact of energy constraints on sensors and UAVs on security performance, nor have they addressed the energy efficiency issues of secure transmission. Therefore, secure and energy-efficient data acquisition under energy constraints is a very worthwhile research problem. Summary of the Invention

[0006] To address the existing problems, this invention provides a high-energy-efficiency, secure sensor data acquisition method based on unmanned aerial vehicles (UAVs), the specific solution of which is as follows:

[0007] A high-energy-efficiency and secure sensor data acquisition method based on unmanned aerial vehicles (UAVs) includes the following steps:

[0008] S1. Obtain the three-dimensional coordinates of K ground sensors and the three-dimensional coordinates of the eavesdropper within the area collected by the drone within time T.

[0009] S2. Based on the information obtained in step S1, establish a model for optimizing the UAV trajectory and speed;

[0010] S3. Solve the UAV trajectory and speed optimization problem model established in step S2;

[0011] S4. Based on the drone trajectory obtained in step S3, set a secure transmission rate threshold, select a suitable sensor to be woken up and activated, and send data to the drone.

[0012] Preferably, the three-dimensional position of the k-th sensor in step S1 is represented as (s k ,0), where s k =(x k ,y k (x) represents the horizontal coordinates of the sensor; the eavesdropper's three-dimensional position is represented as (w,z), where w = (x) / (z) = (x / z) / ( ... e ,y e The horizontal coordinates of the eavesdropper are shown. The time T for the drone to perform data acquisition is divided into N equal time slots Δt, where each time slot is small enough that the drone's position can be considered constant within a time slot. Thus, the drone's trajectory can be represented as a series of three-dimensional spatial points. Where u[n]=(x[n],y[n]) represents the horizontal coordinate of the UAV in the nth time slot, and the velocity of the UAV in the nth time slot is denoted as v[n], v[n]=(v x [n],v y [n]), the UAV is in cruise mode when collecting data, with a fixed flight altitude of z, and its initial and final positions are denoted as u[0]=u0 and u[N]=u N .

[0013] Preferably, the model for optimizing the UAV trajectory and rate in step S2 is established as follows:

[0014]

[0015] in, ξ(U,V) represents the energy efficiency of secure data transmission for UAV data acquisition, and is a function of trajectory U and velocity V. This represents the distance from the k-th sensor to the eavesdropper. This refers to the drone's propulsion power. C1 and C2 are parameters related to the drone's weight, wingspan, and air density. P max V max V acc These are the maximum power, speed, and acceleration of the drone, respectively; B is the channel bandwidth; and γ0 is the reference signal-to-noise ratio of the signal received by the drone.

[0016] Preferably, the solution process in step S3 is as follows:

[0017] S31. Perform parametric programming; specifically, introduce slack variables. and Treating energy efficiency ξ as a parameter, given an initial value of ξ... (0) The optimization problem model can be solved iteratively by solving the following parametric programming problem.

[0018]

[0019] Where, ξ (j) This indicates that the energy efficiency obtained in the previous iteration is used in the current iteration, and is updated after the current iteration ends and used in the next iteration;

[0020] S32, First-order Taylor approximation; specifically, the solution of step S31 needs further transformation, for a given value of the optimization variable (U,V,Φ,Θ) in step S31. and in step S31 and ||v[n]|| 2 Approximating with a first-order Taylor expansion:

[0021]

[0022] Using the two Taylor approximations above, we can further transform the process, given an initial value for the optimization variable (U,V,Φ,Θ). The optimal solution can be obtained by iteratively solving the following problem:

[0023] Preferably, in step S4, appropriate sensors are selected to transmit data for the UAV to receive. Specifically, during each time slot of the UAV's mission, some sensors may be unable to effectively transmit data due to insufficient secure transmission rate. Therefore, in each time slot, sensors with a secure transmission rate greater than a given threshold can be selected to activate and transmit data, thereby improving the energy utilization efficiency of the sensors. The secure transmission rate is expressed as... Given a threshold of R0, the sensor selection rule is as follows: Equivalent to: if Then the k-th sensor is selected to activate and transmit data in the n-th time slot; otherwise, it is not activated to transmit data.

[0024] The present invention also discloses a computer-readable storage medium containing a computer program, which, when executed, performs the above-described high-efficiency sensor data security acquisition method based on unmanned aerial vehicles.

[0025] The present invention also discloses a computer system, including a processor and a storage medium, wherein a computer program is stored on the storage medium, and the processor reads from the storage medium and runs the computer program to execute the above-mentioned high-efficiency sensor data security acquisition method based on UAV.

[0026] The beneficial effects of this invention are as follows:

[0027] This invention addresses the security and energy constraints of using drones for wireless sensor data acquisition in the Internet of Things (IoT). Based on physical layer security theory and technology, it optimizes the drone's flight path and effectively selects the time for transmitting ground sensor data. This maximizes the energy efficiency of secure data transmission while ensuring information security, achieving optimal energy-saving data acquisition. Specifically, this invention considers factors such as the drone's mission duration, maximum power constraints, maximum speed, and acceleration constraints. With the goal of maximizing the energy efficiency of secure data transmission, it establishes a drone trajectory and speed optimization problem model. By solving for the optimal trajectory and flight speed, it achieves optimal energy-saving and secure data acquisition. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is an application scenario for sensor data acquisition based on unmanned aerial vehicles in this invention;

[0030] Figure 2 This is the simulation parameter table in the embodiments of the present invention;

[0031] Figure 3 This is the flight path curve of the UAV when the number of sensors is 40 according to the present invention;

[0032] Figure 4 This is the energy efficiency curve for secure transmission of data acquisition in this invention. Detailed Implementation

[0033] 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 only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] A high-energy-efficiency and secure sensor data acquisition method based on unmanned aerial vehicles (UAVs) includes the following steps:

[0035] S1. Obtain the three-dimensional coordinates of K ground sensors and the three-dimensional coordinates of the eavesdropper within the area collected by the drone within time T.

[0036] S2. Based on the information obtained in step S1, establish a model for optimizing the UAV trajectory and speed;

[0037] S3. Solve the UAV trajectory and speed optimization problem model established in step S2;

[0038] S4. Based on the drone trajectory obtained in step S3, set a secure transmission rate threshold, select a suitable sensor to be woken up and activated, and send data to the drone.

[0039] This invention addresses the security and energy efficiency issues of wireless sensor data acquisition in the Internet of Things (IoT). It proposes utilizing a drone equipped with a data acquisition device for data collection. By optimizing the drone's flight path and selecting appropriate sensors, it aims to collect as much data as possible with limited energy, achieving high-efficiency and secure data acquisition. Application scenarios include... Figure 1 The three-dimensional position of the k-th sensor in step S1 is represented as (s k ,0), where s k =(x k ,y k (x) represents the horizontal coordinates of the sensor; the eavesdropper's three-dimensional position is (w,z), where w = (x) / (z) = (x / z) / ( ... e ,y e The horizontal coordinates of the eavesdropper are shown. The time T for the drone to perform data acquisition is divided into N equal time slots Δt, where each time slot is small enough that the drone's position can be considered constant within a time slot. Thus, the drone's trajectory can be represented as a series of three-dimensional spatial points. Where u[n]=(x[n],y[n]) represents the horizontal coordinate of the UAV in the nth time slot, and the velocity of the UAV in the nth time slot is denoted as v[n], v[n]=(v x [n],v y [n]), the UAV is in cruise mode when collecting data, with a fixed flight altitude of z, and its initial and final positions are denoted as u[0]=u0 and u[N]=u N .

[0040] In practical applications, drones are used to locate sensors and eavesdroppers, obtaining their positions, and then an optimization problem model is established. Problem modeling uses the energy efficiency of secure data transmission as the optimization objective, considering the following factors:

[0041] (1) Maximum power constraint of UAV;

[0042] (2) Maximum speed and acceleration constraints of the UAV;

[0043] (3) Consistency before and after discrete waypoints.

[0044] The model for optimizing the UAV trajectory and speed is established in step S2 as follows:

[0045]

[0046] in, ξ(U,V) represents the energy efficiency of secure data transmission for UAV data acquisition, and is a function of trajectory U and velocity V. This represents the distance from the k-th sensor to the eavesdropper. This refers to the drone's propulsion power. C1 and C2 are parameters related to the drone's weight, wingspan, and air density. P max V max V acc These are the maximum power, speed, and acceleration of the drone, respectively; B is the channel bandwidth; and γ0 is the reference signal-to-noise ratio of the signal received by the drone.

[0047] The solution process for step S3 based on step S2 is as follows:

[0048] S31. Perform parametric programming; specifically, introduce slack variables. and Treating energy efficiency ξ as a parameter, given an initial value of ξ... (0) The optimization problem model can be solved iteratively by solving the following parametric programming problem:

[0049]

[0050] Where, ξ (j) This indicates that the energy efficiency obtained in the previous iteration is used in the current iteration, and is updated after the current iteration ends and used in the next iteration;

[0051] S32, First-order Taylor approximation; specifically, the solution of step S31 needs further transformation, for a given value of the optimization variable (U,V,Φ,Θ) in step S31. and in step S31 and ||v[n]|| 2 Approximating with a first-order Taylor expansion:

[0052]

[0053] Using the two Taylor approximations above, we can further transform the process, given an initial value for the optimization variable (U,V,Φ,Θ). The optimal solution can be obtained by iteratively solving the following problem:

[0054] Step S4 selects appropriate sensors to transmit data for the UAV to receive. Specifically, during each time slot of the UAV's mission, some sensors may be unable to effectively transmit data due to insufficient secure transmission rate. Therefore, in each time slot, sensors with a secure transmission rate greater than a given threshold can be selected to activate and transmit data, thereby improving the energy efficiency of the sensors. The secure transmission rate is expressed as... Given a threshold of R0, the sensor selection rule is as follows: Equivalent to: if Then the k-th sensor is selected to activate and transmit data in the n-th time slot; otherwise, it is not activated to transmit data.

[0055] The algorithm flow of this invention is as follows:

[0056] (1) Given an initial value ξ for the energy efficiency parameter ξ (0) j = 0;

[0057] (2) Repeat j:=j+1;

[0058] (3) Given an initial value for the optimization variable (U,V,Φ,Θ).

[0059] (4) Repeat i:=i+1;

[0060] (5) For a given The optimal solution in step S32 is obtained by solving the problem using convex optimization.

[0061] (6) Continue with step S32. The increment of the objective function is less than a given precision value.

[0062] (7) Used Calculate the objective function value in step S2;

[0063] (8) Continue in step S2, the increment of the objective function is less than a given precision value;

[0064] (9) Sensors that meet the confidential transmission rate requirements are activated to send data;

[0065] (10) Return the optimal trajectory and speed, as well as the energy efficiency of secure transmission.

[0066] To verify the performance of this invention, the proposed algorithm was compared with the shortest path algorithm. Data acquisition from randomly deployed sensors within a one-square-kilometer area was considered. Simulation parameters are as follows: Figure 2 The flight path simulation results are shown in the table. Figure 3 As shown. By Figure 3 As can be seen, the shortest path for a drone is a straight line from the starting point to the final point. Although the shortest path consumes less total energy, it also results in fewer selected sensors within the drone's receiving range, potentially leading to lower energy efficiency in data acquisition. To improve data acquisition energy efficiency, drone path optimization should cover as many sensors as possible, thereby selecting more sensors that meet the secure transmission rate requirements to be activated and transmit data, thus maximizing the secure energy efficiency of data acquisition.

[0067] Simulation results of the secure transmission energy efficiency curve for data acquisition are as follows: Figure 4 As shown. The energy efficiency of secure transmission achieved by the algorithm proposed in this invention is far higher than that achieved by the shortest flight path. Furthermore, Figure 4 As the number of sensors in the area increases, the energy efficiency of secure data transmission increases. This is because drones can select more sensors that meet the requirements for secure transmission to send data, allowing the data acquisition mission to collect more data and thus improve energy utilization efficiency.

[0068] This invention addresses the security and energy constraints of using drones for wireless sensor data acquisition in the Internet of Things (IoT). Based on physical layer security theory and technology, it optimizes the drone's flight path and effectively selects the time for transmitting ground sensor data. This maximizes the energy efficiency of secure data transmission while ensuring information security, achieving optimal energy-saving data acquisition. Specifically, this invention considers factors such as the drone's mission duration, maximum power constraints, maximum speed, and acceleration constraints. With the goal of maximizing the energy efficiency of secure data transmission, it establishes a drone trajectory and speed optimization problem model. By solving for the optimal trajectory and flight speed, it achieves optimal energy-saving and secure data acquisition.

[0069] The present invention also discloses a computer-readable storage medium containing a computer program, which, when run, executes the aforementioned high-efficiency sensor data security acquisition algorithm based on unmanned aerial vehicles.

[0070] The present invention also discloses a computer system, including a processor and a storage medium, wherein a computer program is stored on the storage medium, and the processor reads from the storage medium and runs the computer program to execute the above-mentioned high-efficiency sensor data security acquisition algorithm based on UAV.

[0071] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the invention.

[0072] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein can be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0073] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0074] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or through a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0075] The prior description of this disclosure is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not intended to be limited to the examples and designs described herein, but should be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0076] 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; and these 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 high energy-efficient sensor data security collection based on a UAV, characterized in that, The method comprises the following steps: S1, acquiring three-dimensional coordinates of K ground sensors and three-dimensional coordinates of an eavesdropper in a region collected by a UAV in T time; wherein the three-dimensional position of the th sensor is represented as wherein is the horizontal coordinate of the sensor; the three-dimensional position of the eavesdropper is represented as wherein is the horizontal coordinate of the eavesdropper; the time that the UAV performs the data collection task is divided into equal time slots of length , which is small enough so that the position of the UAV is considered constant within a time slot, and thus the trajectory of the UAV can be represented as a series of three-dimensional points wherein represents the horizontal coordinate of the UAV in the th time slot, and the velocity of the UAV in the th time slot is represented as , the UAV is in cruise mode while collecting data, and the flight height is fixed at , and the initial and final positions of the UAV are denoted as and ; S2, establishing a UAV track and rate optimization problem model according to the information acquired in step S1; The UAV track and rate optimization problem model is established as follows: ； wherein, , denotes the secrecy transmission energy efficiency of the UAV data collection, is a function of the flight path and the speed , , , denotes the distance from the sensor to the eavesdropper, is the propulsion power of the UAV, and are parameters related to the weight of the UAV, the wing area, and the air density, , , are the maximum power, speed, and acceleration of the UAV, respectively, is the channel bandwidth, is the reference signal-to-noise ratio of the received signal of the UAV; S3, solving the UAV track and rate optimization problem model established in step S2; specifically, S31, parameter programming: introducing a slack variable, taking the energy efficiency as a parameter, giving an initial value, solving the following parameter programming problem by iteration; specifically, introducing a slack variable and ; taking the energy efficiency as a parameter, giving an initial value of , the optimization problem model is solved by solving the following parameter programming problem by iteration: ； wherein, represents the energy efficiency obtained from the previous iteration, which is used in the current iteration and updated after the end of the current iteration for the next iteration; S32, first order Taylor approximation: first order Taylor expansion approximation is performed on the optimization variables, and the transformed optimization problem is solved by iteration; specifically, for the solution of step S31, further transformation is needed, for a given value of the optimization variable in step S31 , and the first order Taylor expansion approximation is performed on and in step S31:​ ； ； Using the above two Taylor approximations, further transformations, given an initial value of the optimization variable , the optimal solution is obtained by iteratively solving the following problem: ;​ S4, setting a secure transmission rate threshold condition according to the UAV track obtained in step S3, selecting a suitable sensor to be woken up and activated, and sending data to the UAV.

2. The method of claim 1, wherein, The step S4 selects appropriate sensors to send data for the UAV to receive. Specifically, in each time slot when the UAV performs a task, some sensors can not actually send data effectively because their secret transmission rate is too low. Therefore, in each time slot, sensors with a secret transmission rate greater than a given threshold value are selected to be activated to send data, thereby improving the energy utilization efficiency of the sensors. The secret transmission rate is represented as The given threshold is The sensor selection rule is which is equivalent to: if the first sensor is selected to be activated to send data in the first time slot, otherwise it is not activated to send data.

3. A computer-readable storage medium, characterized in that: The medium has a computer program stored thereon, and the computer program runs to execute the high-energy-efficient sensor data security acquisition method based on a UAV as claimed in any one of claims 1 to 2.

4. A computer system, characterized by: The device comprises a processor and a storage medium, the storage medium has a computer program stored thereon, and the processor reads and runs the computer program from the storage medium to execute the high-energy-efficient sensor data security acquisition method based on a UAV as claimed in any one of claims 1 to 2.

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