Joint Optimization Method for Minimizing the Age of Information in Multi-UAV-Assisted Data Collection

Abstract

The present invention discloses a joint optimization method for minimizing the age of data collection information assisted by multiple unmanned aerial vehicles, belonging to the field of wireless communication. The optimization method includes: constructing and reformulating the initial model of the multiple unmanned aerial vehicle-assisted data collection system; initializing the iteration number variable and the hovering points corresponding to the ground sensor nodes; given the hovering positions, reformulating the initial model of the multiple unmanned aerial vehicle-assisted data collection system as a vehicle routing problem with energy constraints for optimization, obtaining the optimal task allocation and access order of the multiple unmanned aerial vehicles, and combining the hovering positions corresponding to the ground sensor nodes to optimize the initial model of the multiple unmanned aerial vehicle-assisted data collection system to obtain the optimal hovering positions corresponding to the ground sensor nodes; then substituting the task allocation, access order, and hovering positions into iterative optimization to obtain the optimal solution.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication, and specifically relates to a joint optimization method for minimizing the age of data collection information assisted by multiple unmanned aerial vehicles (UAVs). Background Art

[0002] The Internet of Things (IoT) interconnects and integrates the physical world and the information space of humans, and has been widely applied in fields such as transportation and industry. IoT devices monitor and generate data, which need to be transmitted to a base station for data analysis and processing. Since some devices are far from the base station, the data cannot be directly transmitted to the base station. The rapid development of UAV technology provides a feasible solution to solve the above problems.

[0003] According to the number of UAVs, UAV-assisted IoT can be divided into single-UAV assistance and multi-UAV assistance. Although the operation mechanism of a single-UAV-assisted IoT system is relatively simple, the data collection performance is far from satisfactory. Compared with single-UAV-assisted IoT, multi-UAV-assisted IoT has outstanding advantages in terms of task coverage and data collection efficiency, especially in sparse deployment scenarios.

[0004] However, due to the energy limitation of UAVs and the energy-saving requirements of sensor nodes, multiple UAVs need to perform comprehensive cooperative task planning. In the scenario of multi-UAV-assisted IoT data collection, traditional methods often independently optimize task allocation, trajectory planning, or energy consumption allocation, ignoring the coupling relationship between these factors, resulting in problems such as untimely information update of sensor nodes and low system energy efficiency. Therefore, a joint optimization method for minimizing the age of data collection information assisted by multiple UAVs is proposed. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a joint optimization method for minimizing the age of data collection information assisted by multiple UAVs, which solves the problems in the prior art.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A joint optimization method for minimizing the age of data collection information assisted by multiple UAVs includes the following steps:

[0008] S1. Based on a multi-UAV-assisted data collection system, an energy and data transmission model, a UAV energy consumption model, and an age of information model, construct an initial model of the multi-UAV-assisted data collection system;

[0009] S2. Determine the expression of the energy transmission time and reformulate the initial model of the multi-UAV-assisted data collection system;

[0010] S3. Set an iteration number variable , the maximum number of iterations , given the initial hovering points corresponding to all ground sensor nodes ;

[0011] S4, under the condition of the hovering positions corresponding to the given ground sensor nodes , express the initial model of the multi-UAV assisted data collection system as a vehicle routing problem with energy constraints, and optimize it using an improved genetic algorithm to obtain the optimal multi-UAV task allocation and access order ;

[0012] S5, given the optimal multi-UAV task allocation and access order and the hovering positions corresponding to the ground sensor nodes , optimize the initial model of the multi-UAV assisted data collection system based on the SCA method to obtain the optimal hovering positions corresponding to the ground sensor nodes ;

[0013] S6, substitute the optimal multi-UAV task allocation and access order and the hovering positions corresponding to the ground sensor nodes into S4, and update the iteration count variable , repeat S4 to S5 until the maximum iteration count is reached, the iteration terminates, and the optimal solution of the initial model of the multi-UAV assisted data collection system is obtained.

[0014] Furthermore, the multi-UAV assisted data collection system includes: a data center with charging facilities, M UAVs, and K ground sensor nodes SN; the M UAVs operate on orthogonal frequency channels to collect data, and the channel bandwidth size allocated to each UAV is B; the ground equipment is stationary, and the multiple UAVs start from the initial point corresponding to the data center, fly at a fixed altitude H, each UAV visits different hovering points, hovers at each hovering point, performs energy transmission to the corresponding SN and collects data, and finally returns to the initial point .

[0015] Furthermore, the energy and data transmission model is expressed as: the time when the UAV performs radio frequency energy transmission to SN at the hovering point , the time when SN transmits data to the UAV, is the amount of data to be transmitted required by SN , is the amount of data to be transmitted required by SN Data transfer rate with the UAV ; thus the total hover time .

[0016] Furthermore, the UAV energy consumption model is:

[0017]

[0018]

[0019]

[0020]

[0021] In the formula, is the energy consumption of the UAV, is the flight energy consumption, is the hover energy consumption, is the energy transfer energy consumption; among them, is the horizontal coordinate of the hover point corresponding to the th SN visited by the UAV, is the horizontal coordinate of the hover point corresponding to the th SN visited by the UAV, is the flight speed of the UAV, so the flight time between the th hover point visited by the UAV and the next hover point, ; represents the set of SNs associated with the UAV , is a permutation of the SN labels in , where represents the label of the th SN served by the UAV; represents the propulsion power consumption of the UAV; represents the hover power consumption of the UAV, represents the power of the UAV for energy transfer.

[0022] Furthermore, the information age model is:

[0023]

[0024] In the formula, is the average information age of all data collected by the UAV from SNs.

[0025] Furthermore, the initial model of the multi-UAV assisted data acquisition system is:

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] In the formula, is the label of M UAVs, is the label of K ground sensor nodes, is the association variable, indicating whether the UAV is associated with the ground sensor node ; represents the SN set associated with the UAV , is a permutation of the SN labels in where represents the label of the th SN served by the UAV ; is the horizontal coordinate of the hovering position where the UAV collects data from its associated SN is the transmission power for the SN to upload data; is the maximum energy consumption of the UAV itself; is the energy transmission efficiency, is the energy transmission constant, which is affected by actual environmental factors, is the horizontal coordinate of the SN , represents the channel gain when the reference distance is 1m, and H is the flight altitude of the UAV.

[0035] Furthermore, the re-expressed initial model of the UAV assisted data acquisition system is:

[0036]

[0037] .

[0038] A joint optimization system for minimizing the age of data acquisition information assisted by multiple unmanned aerial vehicles, comprising:

[0039] Initial model construction module: Based on the multi-UAV assisted data acquisition system, energy and data transmission model, UAV energy consumption model, and age of information model, construct an initial model of the multi-UAV assisted data acquisition system;

[0040] Model restatement module: Determine the expression of the energy transmission time and restate the initial model of the multi-UAV assisted data acquisition system;

[0041] Initial parameter setting module: Set the iteration number variable , the maximum number of iterations , and given the initial hovering points corresponding to all sensor nodes ;

[0042] First optimization module: Under the condition of given hovering positions corresponding to ground sensor nodes , express the initial model of the multi-UAV assisted data acquisition system as a vehicle routing problem with energy constraints, and use an improved genetic algorithm for optimization to obtain the optimal multi-UAV task allocation and access order ;

[0043] Second optimization module: Given the optimal multi-UAV task allocation and access order and the hovering positions corresponding to ground sensor nodes , optimize the initial model of the multi-UAV assisted data acquisition system based on the SCA method to obtain the optimal hovering positions corresponding to ground sensor nodes ;

[0044] And, iterative optimization module: Substitute the optimal multi-UAV task allocation and access order and the hovering positions corresponding to ground sensor nodes into the first optimization module, and update the iteration number variable , repeat from the first optimization module to the second optimization module until the maximum number of iterations is reached, the iteration terminates, and the optimal solution of the initial model of the multi-UAV assisted data acquisition system is obtained.

[0045] A computer storage medium stores a readable program, which can execute the above-mentioned joint optimization method for minimizing the age of data acquisition information assisted by multiple unmanned aerial vehicles when the program runs.

[0046] An electronic device, comprising: a processor, a memory, a communication interface, and a communication bus, where the processor, the memory, and the communication interface complete communication with each other through the communication bus;

[0047] The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform operations corresponding to the above-mentioned data collection information age minimization joint optimization method assisted by multiple unmanned aerial vehicles.

[0048] Advantages of the present invention:

[0049] 1. The method proposed by the present invention can more flexibly and conveniently adapt to special application scenarios, realizes more efficient collaborative task planning, and effectively improves information freshness and the overall network performance; the present invention considers multiple unmanned aerial vehicles to collect data based on a joint optimization method, with the goal of minimizing the maximum value of the average age of information of the sensor nodes collected by each unmanned aerial vehicle among multiple unmanned aerial vehicles under the constraints of the minimum energy obtained by the ground sensor nodes and the limited energy consumption of the unmanned aerial vehicles, and jointly optimizes the task allocation and access order of multiple unmanned aerial vehicles, the position of the hovering point corresponding to each sensor node, and the hovering time.

[0050] 2. Since the proposed optimization problem is non-convex and difficult to solve, the present invention decomposes the problem into two sub-problems: task allocation and access order optimization, and hovering point position optimization. Given the hovering point position, an improved genetic algorithm is proposed to optimize the task allocation and the access order of the hovering points associated with each unmanned aerial vehicle. Given the task allocation and the access order of the hovering points associated with each unmanned aerial vehicle, the SCA is used to optimize the hovering point position, alternately optimizing the optimization variables of these two sub-problems, and the solution obtained in each iteration will be used as the input for the next iteration.

[0051] 3. The legality of the solution obtained by the improved genetic algorithm proposed by the present invention can be guaranteed. The improved chromosome coding method ensures the legality of task allocation, each SN is uniquely allocated, and at the same time, the task allocation is made as balanced as possible; the improved fitness function ensures that the obtained solutions all satisfy the energy constraints; for the chromosome coding method proposed in this patent, the legality of the chromosome after crossover cannot be ensured under the existing crossover methods. This patent proposes an improved non-zero partial mapping crossover (NZPMX) method to exchange the genes of the parent chromosomes, ensuring that the chromosome after crossover can still contain all the sensor nodes and all the unmanned aerial vehicles have tasks.

[0052] 4. The simulation results of the present invention show the optimized trajectories of unmanned aerial vehicles in different scenarios, and compared with the existing separate optimization schemes, such as the one-time iteration scheme combining K-means clustering and ant colony algorithm, etc., the proposed algorithm realizes more efficient collaborative task planning, effectively improving information freshness and the overall network performance. Brief Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0054] Figure 1 It is a schematic structural diagram of a multi-UAV assisted data acquisition system of the present invention;

[0055] Figure 2 It is a time series framework structure diagram of a single UAV of the present invention;

[0056] Figure 3 It is a flowchart of a joint optimization method for minimizing the average age of information of data acquisition nodes under the assistance of multiple UAVs of the present invention;

[0057] Figure 4 It is a curve graph showing the relationship between the proposed scheme, comparative scheme 1, comparative scheme 2, comparative scheme 3 of the present invention and the amount of data required to be uploaded by the sensor nodes of the present invention. Detailed Embodiments

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0059] Embodiment 1

[0060] As Figure 3 shown, the joint optimization method for minimizing the age of information of data acquisition assisted by multiple UAVs includes the following steps:

[0061] S1, based on the multi-UAV assisted data acquisition system, energy and data transmission model, UAV energy consumption model, and age of information model, construct an initial model of the multi-UAV assisted data acquisition system;

[0062] As Figure 1 shown, the multi-UAV assisted data acquisition system includes: a data center (DC) with charging facilities, M UAVs with a maximum energy of and K ground sensor nodes (SN); all SNs are divided into M non-overlapping groups, each group is served by one UAV, and is used to represent the set of SNs associated with the UAV associated with the UAV Fly to hovering points to perform energy transfer to ground sensor nodes and collect data from ground sensor nodes. Use the correlation variable to represent whether the UAV is correlated with the ground sensor node , where the sensor node is correlated with the UAV when , otherwise . Yes is the permutation of the SN tags in , where represents the th SN tag served by the UAV. is the horizontal coordinate of the hovering position where the UAV collects data from its associated SN . For convenience of representation, define and , that is, all UAVs start from the initial point corresponding to the data center and return to the data center after the task is completed; the speed magnitude of each UAV during flight is constant at .

[0063] M UAVs operate on orthogonal frequency channels to collect data, and the channel bandwidth size allocated to each UAV is B; the ground equipment is stationary, and multiple UAVs start from the initial point , fly at a fixed height H, each UAV (UAV) visits different hovering points, hovers at each hovering point, performs energy transfer to the corresponding SN and collects data, and finally returns to the initial point . Among them ; the set of ground sensor nodes is represented as , the height of the ground equipment is 0 meters and the position is fixed, and the horizontal coordinate is .

[0064] The energy and data transfer model is expressed as: the time when the UAV performs radio frequency energy transfer to the SN at the hovering point , the time when the SN transfers data to the UAV , is the amount of data to be transferred required by the SN , is the transmission rate between the SN and the UAV .

[0065] The UAV energy consumption model is expressed as the UAV 's energy consumption includes: flight energy consumption , hovering energy consumption and energy transmission energy consumption . The UAV 's energy consumption (i.e., the UAV energy consumption model) is expressed as:

[0066]

[0067]

[0068]

[0069]

[0070] where, is the UAV's energy consumption, is the flight energy consumption, is the hovering energy consumption, is the energy transmission energy consumption; among them, is the horizontal coordinate of the hovering point corresponding to the th SN visited by the UAV, is the horizontal coordinate of the hovering point corresponding to the th SN visited by the UAV, is the flight speed of the UAV. Therefore, the flight time between the th hovering point and the next hovering point visited by the UAV, ; represents the set of SNs associated with the UAV , is the permutation of the SN labels in , where represents the label of the th SN served by the UAV, represents the propulsion power consumption of the UAV; represents the hovering power consumption of the UAV, represents the power of the UAV for energy transmission.

[0071] The information age model is expressed as:

[0072]

[0073] where, is the UAV from The average information age of all data collected by one SN, single unmanned aerial vehicle UAV The time series framework structure diagram of Figure 2 is shown as follows.

[0074] The initial model of the multi-UAV assisted data collection system is:

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083] In the formula, is the correlation variable, indicating whether the UAV is correlated with the ground sensor node ; represents the set of SNs associated with the UAV , is the permutation of the SN tags in , where represents the tag of the th SN served by the UAV ; is the horizontal coordinate of the hovering position where the UAV collects data from its associated SN ; are the tags of M UAVs, are the tags of K ground sensor nodes, is the transmission power of the data uploaded by the SN, is the maximum energy consumption of the UAV itself, is the energy transfer efficiency, is the energy transfer constant, which is affected by actual environmental factors, is the horizontal coordinate of the SN , represents the channel gain when the reference distance is 1m, and H is the flight altitude of the UAV.

[0084] Formula C1 restricts each SN to be associated with only one UAV;

[0085] Formula C2 restricts that all SNs need to have data collected;

[0086] Formula C3 represents the set of SNs associated with the UAV ;

[0087] Formula C4 restricts that the value of the associated variable can only be 0 or 1;

[0088] Formula C5 restricts that the energy consumed by each SN for uploading data cannot be greater than the energy it obtains;

[0089] Formula C6 restricts that the energy consumption required for the UAV to complete the task cannot be greater than its own maximum energy consumption, which is the maximum energy consumption of the UAV itself;

[0090] Formula C7 ensures that the starting point and the ending point of each UAV are the data center .

[0091] S2. Determine the expression of the energy transfer time and reformulate the initial model of the multi-UAV assisted data collection system;

[0092] It can be proved that in the optimal solution of the initial model of the multi-UAV assisted data collection system, the equality relationship in constraint C5 holds. Otherwise, with other variables fixed, it can be reduced, then the average age of information is reduced, and other constraint conditions are still satisfied, resulting in a reduction of the objective value. Therefore , the radio frequency energy transfer time can be expressed as:

[0093]

[0094] where represents the received signal-to-noise ratio when the UAV is 1 m away from the ground sensor node, represents the channel gain when the reference distance is 1 m, represents the noise at the UAV, and B is the channel bandwidth size allocated to the UAV.

[0095] The reformulated initial model of the UAV assisted data collection system is:

[0096]

[0097]

[0098] S3. Set the iteration number variable , the maximum number of iterations , and given the initial hovering points corresponding to all sensor nodes ;

[0099] S4. Under the condition of the hovering position corresponding to the given sensor node , express the initial model of the multi-UAV assisted data collection system in S2 as a vehicle routing problem with energy constraints (CVRP); use an improved genetic algorithm for optimization to obtain the optimal multi-UAV task allocation and access order ;

[0100] Obtain the optimal multi-UAV task allocation and access order The steps are as follows:

[0101] S41. Under the condition of the hovering position corresponding to the given sensor node , the initial model of the UAV-assisted data collection system to be optimized is:

[0102]

[0103]

[0104] S42. Initialize the chromosome: The chromosome uses an integer sequence to represent the access order of SNs and divides the allocation of sensor nodes by inserting zeros. This coding method can comprehensively describe the task execution process of the UAV and take into account the legality of the solution. For example, there are 15 ground sensor nodes and 3 UAVs, and a certain chromosome coding is:

[0105]

[0106] It means that the ground sensor node is assigned to the UAV , and at the same time, the access order of the UAV is ; is assigned to the UAV , and at the same time, the access order of the UAV is ; is assigned to the UAV , and at the same time, the access order of the UAV is .

[0107] S43. Calculate the fitness function: The fitness function is designed as a combination of the optimization objective and the constraint penalty term. The optimization objective is to minimize the maximum average age of information of the task , and the constraint penalty term is . The expression of the final fitness function is: , where is the fitness function of the genetic algorithm, is the maximum value among the average age of information of M UAVs. is the value by which the energy consumed by the UAV exceeds its maximum energy consumption. If it does not exceed, it is 0. is the penalty coefficient, which is used to balance the optimization objective and the constraint conditions between the weights.

[0108] S44, Select: Adopt the tournament selection method to select the chromosomes with higher fitness from the current population as elites to be retained and enter the next generation.

[0109] S45, Crossover: Under the existing crossover methods, it is impossible to ensure the legality of the chromosomes after crossover. In order to ensure that the chromosomes after crossover can still contain all the sensor nodes and each sensor node appears only once, the present invention proposes an improved zero-removed partial mapping crossover (NZPMX) method to exchange the genes of the parent chromosomes. The specific steps are as follows: First, record the positions of the zeros in the chromosome and remove the zeros in the chromosome to make it a new chromosome without zeros; Second, perform partial mapping crossover on the chromosome without zeros; Finally, insert zeros into the chromosome without zeros so that the positions of the zeros in the expanded chromosome are the same as those before removing the zeros.

[0110] S46, Mutation: Randomly exchange the positions of two genes in the chromosome to increase the diversity of the solutions.

[0111] S47, The optimization iteration of the entire algorithm continues until the preset maximum number of iteration generations is reached, and the optimal task allocation of multiple UAVs and the access order of each UAV are obtained.

[0112] S5, Given the optimal task allocation of multiple UAVs and the access order and the hovering positions corresponding to the ground sensor nodes , optimize the initial model of the multi-UAV assisted data acquisition system in S2 based on the SCA method to obtain the optimal hovering positions corresponding to the ground sensor nodes , and use it as the input for the next iteration;

[0113] The steps to obtain the optimal hovering positions corresponding to the sensor nodes are as follows:

[0114] S51, Under the condition of the given task allocation of multiple UAVs and the access order , the initial model of the UAV assisted data acquisition system to be optimized is:

[0115]

[0116]

[0117] S52. For each UAV, optimize the hovering point position separately. The goal is to minimize the average age of information of the sensor nodes served by a single UAV, and substitute the specific expression formulas of time and energy into this problem. The model can be reformulated as:

[0118]

[0119]

[0120] S53. Introduce a set of slack variables and into the constraint conditions, where:

[0121]

[0122] By replacing the corresponding terms with the slack variables, we get:

[0123]

[0124]

[0125] S54. Under the condition of the given task assignment and access order of the multi-UAVs, the initial model of the UAV-assisted data collection system to be optimized is updated as:

[0126]

[0127]

[0128]

[0129] S55. Use the successive convex approximation (SCA) technique to obtain the first-order Taylor expansion expression of the data transmission rate for the squared norm at the given iteration point . is the lower bound function expression after the first-order Taylor expansion of

[0130]

[0131] That is, is at the given iteration point with respect to the squared norm ​The first derivative, specifically expressed as:

[0132]

[0133] S56, at the th iteration, given the task assignment of multiple UAVs and the access order of each UAV as well as the hover point positions under the conditions, the initial model of the UAV-assisted data acquisition system to be optimized is updated to:

[0134]

[0135]

[0136]

[0137] In the formula, is the lower bound function expression after the first-order Taylor expansion of

[0138] S6, based on the joint optimization method of alternating optimization, the optimal task assignment of multiple UAVs and the access order and the hover positions corresponding to the ground sensor nodes are substituted into S4, and the iteration number variable is updated. Repeat S4 to S5 until the initial model of the UAV-assisted data acquisition system reaches the maximum number of iterations and the iteration terminates, and the optimal solution of the initial model of the UAV-assisted data acquisition system is obtained.

[0139] Compared with the traditional method that often independently optimizes or separately optimizes task assignment, trajectory planning, or energy consumption allocation, ignoring the coupling relationship between these factors, resulting in problems such as untimely update of sensor node information and low system energy efficiency, the framework proposed by the present invention can more flexibly and conveniently adapt to special application scenarios, realizes more efficient collaborative task planning, and effectively improves the information freshness and overall network performance.

[0140] Based on a similar inventive concept, an embodiment of the present invention further provides a computer storage medium storing a readable program, which can execute the above-mentioned joint optimization method for minimizing the age of data acquisition information assisted by multiple UAVs when the program runs.

[0141] Based on a similar inventive concept, an embodiment of the present invention provides an electronic device, including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete communication with each other through the communication bus;

[0142] The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform operations corresponding to the above-mentioned joint optimization method for minimizing the age of data acquisition information assisted by multiple UAVs.

[0143] Based on a similar inventive concept, an embodiment of the present invention also provides a computer program product, including computer instructions, and the computer instructions instruct a computing device to perform operations corresponding to the above-mentioned joint optimization method for minimizing the age of data acquisition information assisted by multiple UAVs.

[0144] The method of the present invention can be implemented in hardware, firmware, or be implemented as software or computer code that can be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk, or magneto-optical disk), or be implemented as computer code originally stored in a remote recording medium or a non-transitory machine-readable medium and downloaded through a network and will be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component (such as a RAM, a ROM, a flash memory, etc.) that can store or receive software or computer code. When the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0145] Embodiment 2

[0146] In this embodiment, the solution of the present invention is described in detail in combination with a specific embodiment. MATLAB software is used to simulate this embodiment. The specific parameters are set as shown in Table 1;

[0147] Table 1 Simulation Parameter Table

[0148]

[0149] In addition to the above table, the coordinates of the four ground devices are set as =(150, -200), =(-300, 400), =(250, -350), =(-450, -100), =(400, 250), =(-120, -450), =(350, 300), =(-200, -300), =(100, 150), =(-400, 200), =(275, -275), =(-375, -125), =(425, 375), =(-275, 350), =(50, -400).

[0150] In this embodiment, a comparative experiment is conducted on the joint optimization scheme of the present invention with Comparative Scheme 1, Comparative Scheme 2, and Comparative Scheme 3. Among them, Comparative Scheme 1 is an improved genetic algorithm combined with the successive convex approximation method, Comparative Scheme 2 is the K-means clustering algorithm and the ant colony algorithm combined with the successive convex approximation method, and Comparative Scheme 3 is the K-means clustering algorithm combined with the ant colony algorithm.

[0151] From Figure 4 it can be seen that through the comparison of the data upload amounts required by different ground sensor nodes, it can be observed that the larger the data upload amount required by the ground sensor nodes, the larger the minimized maximum average age of information. This is because the UAV needs to spend more hovering time at the hovering points for energy transfer and data collection, increasing the overall age of information of the system. In addition, the joint optimization scheme proposed in the present invention minimizes the maximum average age of information under different data upload amounts required by ground sensor nodes and is less than Comparative Schemes 1 - 3 because the joint optimization scheme proposed in the present invention can better consider the coupling relationship of various factors.

[0152] Embodiment 3

[0153] Based on the joint optimization method for minimizing the age of information in data collection assisted by multiple UAVs proposed in Embodiment 1, a joint optimization system for minimizing the age of information in data collection assisted by multiple UAVs is proposed in this embodiment, specifically including:

[0154] Initial model construction module: Based on the multi-UAV assisted data collection system, energy and data transmission model, UAV energy consumption model, and age of information model, construct an initial model of the multi-UAV assisted data collection system.

[0155] Model restatement module: Determine the expression of the energy transfer time and restate the initial model of the multi-UAV assisted data collection system.

[0156] Initial parameter setting module: Set the iteration number variable , the maximum number of iterations , and give the initial hovering points corresponding to all sensor nodes .

[0157] The first optimization module: Under the condition of the hovering position corresponding to the given ground sensor node The initial model of the multi-UAV assisted data acquisition system is formulated as a vehicle routing problem with energy constraints, and an improved genetic algorithm is used for optimization to obtain the optimal multi-UAV task allocation and access order ;

[0158] The second optimization module: Given the optimal multi-UAV task allocation and access order and the hovering position corresponding to the ground sensor node , based on the SCA method, the initial model of the multi-UAV assisted data acquisition system is optimized to obtain the optimal hovering position corresponding to the ground sensor node ;

[0159] In addition, the iterative optimization module: Substitute the optimal multi-UAV task allocation and access order and the hovering position corresponding to the ground sensor node into the first optimization module, and update the iteration number variable , repeat from the first optimization module to the second optimization module until the maximum iteration number is reached, the iteration terminates, and the optimal solution of the initial model of the multi-UAV assisted data acquisition system is obtained.

[0160] The method of the present invention can be implemented in hardware, firmware, or be implemented as software or computer code that can be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk, or magneto-optical disk), or be implemented as computer code that is originally stored in a remote recording medium or a non-transitory machine-readable medium and will be stored in a local recording medium and downloaded through a network, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component (such as RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0161] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed.

Claims

1. A joint optimization method for minimizing the age of data collection information assisted by multiple drones, characterized in that: The following steps are involved: S1, construct the initial model of multi-UAV assisted data collection system based on multi-UAV assisted data collection system, energy and data transmission model, UAV energy consumption model and information age model; S2, determine the expression of energy transfer time and reformulate the initial model of multi-UAV assisted data acquisition system; S3, set the number of iterations variable , maximum number of iterations , given the initial hovering points corresponding to all ground sensor nodes ; S4, the hovering position corresponding to a given ground sensor node Under the condition of , the initial model of multi-UAV assisted data acquisition system is expressed as a vehicle routing problem with energy constraints, and the improved genetic algorithm is used to optimize it to obtain the optimal multi-UAV task allocation Access Order ; S5, given the optimal multi-UAV task allocation Access Order The hovering position corresponding to the ground sensor node , based on the SCA method, the initial model of the multi-UAV assisted data acquisition system is optimized to obtain the optimal hovering position corresponding to the ground sensor node ; S6, optimal multi-drone task allocation Access Order The hovering position corresponding to the ground sensor node Substitute into S4 and update the iteration count variable , repeat S4 to S5 until the maximum number of iterations is reached , the iteration is terminated, and the optimal solution of the initial model of the multi-UAV assisted data acquisition system is obtained; The energy and data transmission model is represented as: At the hover point Ground sensor nodes Time of RF energy transmission , the time it takes for the ground sensor node to transmit data to the UAV , Ground sensor nodes The amount of data to be transferred, Ground sensor nodes With drones The data transfer rate between ; The energy consumption model of the UAV is: In the formula, The energy consumption of the drone, is the flight energy consumption, is the hovering energy consumption, is the energy transmission energy consumption; For drones Visited Ground sensor nodes The horizontal coordinate of the corresponding hover point, For drones Visited Ground sensor nodes The horizontal coordinate of the corresponding hover point, is the flight speed of the drone, so Drones Visited The flight time between the first hovering point and the next hovering point, ; Indicates drone The associated set of ground sensor nodes, yes The permutation of ground sensor node labels in Indicates drone Service Tags of ground sensor nodes; Indicates the propulsion power consumption of the UAV; Indicates the hovering power consumption of the drone. Indicates the power used by the drone for energy transmission; The information age model is: In the formula, For drones from The average information age of all data collected by ground sensor nodes; The initial model of the multi-UAV assisted data acquisition system is: In the formula, is the label of M drones, are the labels of K ground sensor nodes, is an associated variable, indicating that the drone Whether it is connected to the ground sensor node association; Indicates drone The associated set of ground sensor nodes, yes The permutation of ground sensor node labels in Indicates drone Service Tags of ground sensor nodes; For drones From its associated ground sensor node The horizontal coordinates of the hovering position where data was collected; Transmission power for uploading data from ground sensor nodes; is the maximum energy consumption of the drone itself; is the energy transfer efficiency, is the energy transfer constant, which is affected by actual environmental factors. Ground sensor nodes The horizontal coordinate of It represents the channel gain when the reference distance is 1m, and H is the flight altitude of the UAV.

2. The multi-UAV-assisted data collection information age minimization joint optimization method according to claim 1 is characterized in that: The multi-UAV assisted data collection system includes: a data center with charging facilities, M UAVs and K ground sensor nodes; the M UAVs operate on orthogonal frequency channels to collect data, and the channel bandwidth size allocated to each UAV is B; the ground equipment is stationary, and multiple UAVs start from the corresponding initial points in the data center. Starting from the fixed altitude H, each UAV visits different hovering points and hovers at each hovering point, transmitting energy to the corresponding ground sensor nodes and collecting data, and finally returns to the starting point .

3. The multi-UAV-assisted data collection information age minimization joint optimization method according to claim 1 is characterized in that: The reformulated initial model of the UAV-assisted data acquisition system is: 。 4. A joint optimization system for minimizing the age of data collection information assisted by multiple drones, characterized in that: include: Initial model building module: Based on the multi-UAV assisted data collection system, energy and data transmission model, UAV energy consumption model and information age model, the initial model of the multi-UAV assisted data collection system is built; Model restatement module: Determine the expression of energy transfer time and restate the initial model of multi-UAV assisted data acquisition system; Initial parameter setting module: set the number of iterations variable , maximum number of iterations , given the initial hovering points corresponding to all sensor nodes ; First optimization module: hovering position corresponding to a given ground sensor node Under the condition of , the initial model of multi-UAV assisted data acquisition system is expressed as a vehicle routing problem with energy constraints, and the improved genetic algorithm is used to optimize it to obtain the optimal multi-UAV task allocation Access Order ; Second optimization module: Given the optimal multi-UAV task allocation Access Order The hovering position corresponding to the ground sensor node , based on the SCA method, the initial model of the multi-UAV assisted data acquisition system is optimized to obtain the optimal hovering position corresponding to the ground sensor node ; And, iterative optimization module: optimal multi-UAV task allocation Access Order The hovering position corresponding to the ground sensor node Substitute into the first optimization module and update the number of iterations variable , repeat the first optimization module to the second optimization module until the maximum number of iterations is reached , the iteration is terminated, and the optimal solution of the initial model of the multi-UAV assisted data acquisition system is obtained; The energy and data transmission model is represented as: At the hover point Ground sensor nodes Time of RF energy transmission , the time it takes for the ground sensor node to transmit data to the UAV , Ground sensor nodes The amount of data to be transferred, Ground sensor nodes With drones The data transfer rate between ; The energy consumption model of the UAV is: In the formula, The energy consumption of the drone, is the flight energy consumption, is the hovering energy consumption, is the energy transmission energy consumption; For drones Visited Ground sensor nodes The horizontal coordinate of the corresponding hover point, For drones Visited Ground sensor nodes The horizontal coordinate of the corresponding hover point, is the flight speed of the drone, so Drones Visited The flight time between the first hovering point and the next hovering point, ; Indicates drone The associated set of ground sensor nodes, yes The permutation of ground sensor node labels in Indicates drone Service Tags of ground sensor nodes; Indicates the propulsion power consumption of the UAV; Indicates the hovering power consumption of the drone. Indicates the power used by the drone for energy transmission; The information age model is: In the formula, For drones from The average information age of all data collected by ground sensor nodes; The initial model of the multi-UAV assisted data acquisition system is: In the formula, is the label of M drones, are the labels of K ground sensor nodes, is an associated variable, indicating that the drone Whether it is connected to the ground sensor node association; Indicates drone The associated set of ground sensor nodes, yes The permutation of ground sensor node labels in Indicates drone Service Tags of ground sensor nodes; For drones From its associated ground sensor node The horizontal coordinates of the hovering position where data was collected; Transmission power for uploading data from ground sensor nodes; is the maximum energy consumption of the drone itself; is the energy transfer efficiency, is the energy transfer constant, which is affected by actual environmental factors. Ground sensor nodes The horizontal coordinate of It represents the channel gain when the reference distance is 1m, and H is the flight altitude of the UAV.

5. A computer storage medium storing a readable program, characterized in that: When the program is executed by the processor, it can execute the joint optimization method for minimizing the age of data collection information assisted by multiple drones as described in any one of claims 1 to 3.

6. An electronic device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the joint optimization method for minimizing the age of data collection information assisted by multiple drones as described in any one of claims 1-3.

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