Unmanned aerial vehicle assisted data collection and distribution optimization method based on information age

Abstract

The application discloses an unmanned aerial vehicle (UAV) assisted data collection and distribution optimization method based on information age, and belongs to the technical field of wireless communication. First, the UAV goes to the sensor node area to collect data, then other UAVs share data with the UAV with optimal energy, and the UAV with optimal energy distributes the data to users while flying. The optimization method comprises the following steps: under the constraints of the communication distance of the sensor node, the number of UAVs and the signal-to-noise ratio threshold, a minimum average age of information (AoI) problem is established; the minimum average AoI problem is decomposed into three optimization sub-problems, namely, the number of sensor node clusters, area division and UAV trajectory; the improved K-means clustering method is used to solve the number of sensor node cluster optimization sub-problems, and the sensor nodes in the area are clustered; the area division method of geometric centroid distance is used to solve the area division optimization sub-problem, so as to divide the whole area into multiple sub-areas, and each UAV is responsible for collecting data in a sub-area; and the 2-opt method with fixed starting point and ending point is used to solve the UAV trajectory optimization sub-problem, so that the average AoI is minimized.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to an optimized method for drone-assisted data collection and distribution based on information age. Background Technology

[0002] In recent years, the Internet of Things (IoT) technology has developed rapidly, enabling intelligent interaction between various devices and systems, demonstrating enormous application value and development potential. In the IoT environment, information collection is a crucial foundation for maintaining stable system operation and achieving intelligent decision-making. Unmanned Aerial Vehicles (UAVs), with their high efficiency, flexibility, and precision, have become key tools for data collection and distribution. They can acquire geographic, environmental, and monitoring data in real time, providing timely and reliable information support for relevant decision-making, and playing an increasingly important role in many fields such as disaster monitoring, environmental protection, and intelligent transportation. Therefore, UAV-assisted data collection and distribution has become one of the current research hotspots. In UAV-assisted data collection systems, data collection efficiency is directly affected by the UAV's flight trajectory and resource allocation. Although there has been in-depth research on UAV resource allocation and path planning, it has not fully considered the factor of data freshness, while many IoT applications (such as environmental monitoring and intelligent transportation) have strict requirements for data freshness. Information age (AoI) is a metric used to measure data freshness. It is defined as the time interval from the moment the data is generated to the moment the data is successfully received by the receiver. It is often used to analyze the timeliness of information updates in queue models. Most inventions generally use information age as a key indicator for evaluating data timeliness. However, the proposed methods have neglected how drones can distribute the acquired data to users more quickly. A drone-assisted data collection and distribution optimization method based on information age is designed to improve the efficiency of drone data distribution to users, reduce drone flight time, and increase data freshness. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide an optimized method for drone-assisted data collection and distribution based on information age, thereby solving the problems in existing technologies.

[0004] The objective of this invention can be achieved through the following technical solutions: An optimized method for drone-assisted data collection and distribution based on information age is applied in a system comprising drones, sensor nodes, and users. The drones first collect data in the sensor node area, then other drones share data with the drone with the best energy level. Finally, the drone with the best energy level distributes the data to users while in flight. The optimization method includes: Under the constraints of communication distance of sensor nodes, number of drones and signal-to-noise ratio threshold, a problem is established to minimize the average AoI; The problem of minimizing the average AoI is decomposed into three optimization sub-problems: the number of sensor node clusters, region partitioning, and UAV trajectory. An improved K-means clustering method is used to solve the subproblem of optimizing the number of sensor node clusters, and the sensor nodes in the region are clustered. The region partitioning method based on geometric centroid distance is used to solve the subproblem of region partitioning, so that the entire region is divided into multiple sub-regions, and each UAV is responsible for collecting data in one sub-region. The UAV trajectory optimization subproblem is solved using the 2-opt method with fixed start and end points, so as to minimize the average AoI.

[0005] Furthermore, the other drones use frequency division multiple access (FDMA) to share the collected data with the drone with the best energy.

[0006] Furthermore, the problem of minimizing the average AoI is defined as P1: in, Represents the number of clusters. Represents the size of the region. and This represents the trajectory of the drone during data collection and distribution. For the number of users, This represents the number of sensor nodes. For the number of drones, The total amount of data required by all users. For users Is it necessary? Type of data, For sensors Whether it produces Type of data, For the data type of the sensor node, The cluster head can only communicate with one drone. For users, drones with all the data are starting to be used. The observation time for distributing data, The AoI for the drone to begin distributing data to the user. The signal-to-noise ratio of the transmission link between the cluster head and the drone. The signal-to-noise ratio threshold for the transmission link. The total energy consumed by the drone, For the maximum energy of the drone, This represents the distance between a regular node and the cluster head. Cluster radius; constraint The signal-to-noise ratio threshold for communication between the UAV and sensor nodes is constrained. The total energy consumed by the drone must be less than its maximum usable energy, constraining... Each cluster head node communicates with only one drone, constraining... The constraint is that the distance between sensor member nodes and cluster head nodes cannot exceed the maximum communication distance of the nodes. The type of sensor required by the user. Constraints. Each sensor detects only one type of data.

[0007] Furthermore, the subproblem of optimizing the number of sensor node clusters is defined as P2: The region partitioning optimization subproblem is defined as P3: The UAV trajectory optimization sub-problem is defined as P4: .

[0008] Furthermore, the process of solving the subproblem of optimizing the number of sensor node clusters using the improved K-means clustering method is as follows: based on the Euclidean distance between ground sensor nodes, the cluster head node is determined by dynamically optimizing the number and position of clusters.

[0009] Furthermore, the process of solving the sub-problem of region partitioning optimization using the geometric centroid distance method is as follows: determine the number of sub-regions based on the number of UAVs, and randomly select... Each cluster head is used as the initial sub-region center. Based on the distance of each other cluster head node to the sub-region center, the remaining cluster head nodes are assigned to the nearest sub-region.

[0010] 7 Further, the process of solving the UAV trajectory optimization subproblem using the 2-opt method with fixed start and end points is as follows: Based on the clusters in the region, find the cluster farthest from the center of the region and the nearest cluster in each region. The farthest cluster is the start point and the nearest cluster is the end point. After determining the start and end points, generate the initial path according to the nearest neighbor algorithm, and then optimize the initial path using the 2-opt optimization algorithm.

[0011] The drone-assisted data collection and distribution optimization device based on information age performs the above method, including: Problem building module: Under the constraints of communication distance of sensor nodes, number of drones and signal-to-noise ratio threshold, establish the problem of minimizing average AoI; Problem decomposition module: The problem of minimizing the average AoI is decomposed into three optimization sub-problems: the number of sensor node clusters, region partitioning, and UAV trajectory; Problem-solving module: The improved K-means clustering method is used to solve the subproblem of optimizing the number of sensor node clusters, and the sensor nodes in the region are clustered. The geometric centroid distance method is used to solve the subproblem of region partitioning, so that the entire region is divided into multiple sub-regions, and each UAV is responsible for collecting data in one sub-region. The 2-opt method with fixed start and end points is used to solve the subproblem of UAV trajectory optimization, so as to minimize the average AoI.

[0012] A computer storage medium storing a readable program that, when executed, instructs a computing device to perform the aforementioned information age-based UAV-assisted data collection and distribution optimization method.

[0013] An electronic device includes: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform operations corresponding to the above-described information age-based UAV-assisted data collection and distribution optimization method.

[0014] The beneficial effects of this invention are: 1. This invention employs an improved K-means clustering algorithm to cluster sensor nodes, and then uses a geometric centroid distance algorithm based on the cluster head position to divide the entire area into multiple sub-regions, which are then distributed to multiple drones for data collection. Through clustering, data from member nodes is concentrated at the cluster head, avoiding the need for drones to fly over a massive number of lower-level nodes one by one. By dividing the area, load balancing and parallel operation among multiple drones are achieved. This mechanism significantly reduces the total time required for drone data collection and effectively lowers the average AoI compared to traditional non-clustering schemes.

[0015] 2. This invention not only adopts the 2-opt optimization algorithm, but also explicitly stipulates that the initial path is generated by taking the cluster head farthest from the center in the sub-region as the starting point and the nearest cluster head as the ending point. This anchor point setting with clear physical boundary awareness makes the trajectory of the UAV from entering the working area to leaving the working area coherent, greatly reducing the invalid flight time between various cluster heads, and further compressing the information retention delay in the collection stage.

[0016] 3. After data collection is completed, the system does not allow all drones to fly over the user area independently. Instead, it selects the drone with the best energy output as the master node, and other drones share data with it via Frequency Division Multiple Access (FDMA). This mechanism effectively overcomes the risk of energy depletion that a single drone may face during large-scale downlink data distribution. Simultaneously, the introduction of FDMA avoids channel interference during concurrent transmission by multiple drones, ensuring that all network data can be safely and efficiently concentrated on the master drone with the strongest endurance, laying the physical foundation for subsequent rapid distribution.

[0017] 4. When the main UAV of this invention flies towards a user, it predicts the amount of data that can be transmitted during the halfway from the current target to the next user. Upon reaching the current user, if the remaining amount of data to be transmitted is greater than the predicted amount, it hovers to make up the difference; if it is less than or equal to the predicted amount, it flies directly over and completes the transmission en route. This breaks the rigid limitation of traditional UAVs that must "hover and transmit" or "fly blindly." By converting the physical time spent during flight into an effective communication transmission window, this strategy significantly reduces the dwell time of the UAV during the distribution phase and the total flight time, forming the core closed loop for minimizing the overall average AoI of the system. Attached Figure Description

[0018] 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, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the system model corresponding to the UAV-assisted data collection and distribution optimization method based on information age of this invention; Figure 2 This is a schematic diagram of the flight trajectory of the clustering, regionalization, and drone data collection stages corresponding to the drone-assisted data collection and distribution optimization method based on information age of the present invention. Figure 3 This is a schematic diagram of the flight trajectory of the drone data distribution stage corresponding to the drone-assisted data collection and distribution optimization method based on information age of the present invention. Figure 4 This is a simulation diagram illustrating the variation of average AoI with the number of drones under different comparison algorithms for the drone-assisted data collection and distribution optimization method based on information age of this invention; Figure 5 This is a simulation diagram illustrating the variation of average AoI with the number of drones under different clustering radii for the drone-assisted data collection and distribution optimization method based on information age according to the present invention; Figure 6This is a simulation diagram illustrating the variation of average AoI with the number of drones at different drone flight speeds, based on the drone-assisted data collection and distribution optimization method of the present invention, which is based on information age. Figure 7 This is a simulation diagram illustrating the variation of average AoI with the number of drones under different sensor node numbers for the drone-assisted data collection and distribution optimization method based on information age according to the present invention; Figure 8 This is a simulation diagram illustrating the variation of average AoI with the number of drones under different sensor node classification types, based on the drone-assisted data collection and distribution optimization method of the present invention, which is based on information age. Detailed Implementation

[0020] 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.

[0021] Example 1 The system applied to the drone-assisted data collection and distribution optimization method based on information age is as follows: Figure 1 As shown, the system consists of ground sensor nodes, drones and It consists of 1 user group, where the ground sensor node is represented as Drones indicate The user represents The sensor nodes and users are randomly distributed in two different areas, with their locations being respectively... and Assume the total time for drone data collection and distribution is... ,Will Divided into equidistant time slots, represented as drones With a fixed height Flight, then drone At any moment The position is ,in The projected coordinates of the drone on the horizontal plane; Sensor nodes are divided into Different types, each user can make random requests There are several data types, with each sensor sensing only one type of data, and each data type has... Each sensor node generates different amounts of data depending on the type of sensor. Sensors of the same type can sense the same amount of data. To improve the transmission rate, ground sensor nodes are divided into multiple clusters. Each cluster consists of multiple member nodes and a cluster head (CH) node. Each cluster head collects data from the member nodes and sends the collected data to the UAV. Each cluster head corresponds to a UAV hovering point, and the set of cluster heads is represented as... The coordinates of the hovering point and the cluster head can be represented as follows: and Then, the entire area was divided according to the location of the cluster leader and the number of drones. Each drone is responsible for collecting data from a different sub-region. After collection, the drone with the best energy is selected as the master drone, and the other drones share data with it using FDMA. Then, the drones distribute the data to the user according to the required data type. To improve efficiency during data distribution, the drones use a fly-and-distribute-data mode. When flying towards the first user, the drone only begins transmitting data when it reaches the user's area boundary. The coordinates of the point where the drone reaches the user boundary are... When the drone arrives above a user, if the remaining data volume is greater than the amount of data that can be transmitted over half the distance to the next user, it will hover. After transmitting the difference while hovering, the remaining data will be transmitted on the way to the next user. Otherwise, it will not hover and will fly directly to the next user, transmitting all data on the way.

[0022] Communication between member nodes and the cluster head node follows a ground-based point-to-point communication model, with a transmission rate of: in, For the system bandwidth, The transmit power of the cluster member nodes. For noise power, For channel gain, For reference distance The corresponding channel gain at that time, The distance between a member node and the cluster head node. This is an environment-dependent constant; the total time for member nodes to transmit data to the cluster head node is: in, For clusters The number of internal member nodes, For data types The amount of data sensed by the sensors is assumed to be the same for nodes of the same type.

[0023] The UAV-sensor communication model adopts a probabilistic line-of-sight (LOS) / non-line-of-sight (LOS) channel model, and the probability of the LOS channel is: in, and It is a constant determined by the propagation environment. It is the altitude at which the drone flies. The elevation angle formed by the cluster head and the drone is such that, since the drone hovers directly above the cluster head to receive data, The probability of a non-line-of-sight link is... The path loss can be expressed as: in, It is a drone To the head of the cluster distance, and These represent the additional losses for line-of-sight communication and non-line-of-sight communication, respectively, based on free-space path loss. Represents the speed of light. Representing the carrier frequency, the probabilistic average path loss under LOS and NLOS conditions is: The signal-to-noise ratio of the transmission link is denoted as: in, Let be the transmit power of the cluster members. Then the data upload rate between the cluster head node and the drone hovering above can be expressed as: Therefore, the time it takes for the cluster head node to send data to the drone is: The hovering time of the drone at each hovering point mainly consists of two parts: the time it takes for member nodes to send data to the cluster head, and the time it takes for the cluster head to send the collected data to the drone. Therefore, the hovering time of the drone at each hovering point is: The time each drone spends collecting data within its respective area is: in, This indicates the number of cluster heads in each region. This refers to the flight time of the drone.

[0024] Data transmission between drones is via line-of-sight (Line-of-Sight) links. After drones have collected data within their respective areas, one drone with optimal energy performance is selected as the data distribution drone. Other drones then send their data to this selected drone using FDMA (Fiber-of-the-Line) technology. Therefore, the data rate between drones is: in For the drone's transmission power, this article assumes the drones are at the same altitude, so... .

[0025] The drone sharing service will begin at: The time for drones to share data is: The sharing ends at: After the drone finishes sharing data, the drone with all the data flies to the user's location and distributes the data to the user. The channel between the drone and the ground user is the same as the channel between the drone and the ground sensor, and the defined path loss is... , Time Drones and Users The distance is: Drones and Users The transmission rate is: Then the drone will reach the user The data transmission time is: in, For users Is it necessary? Data of type, if user If this type of data is needed, then ,on the contrary, . For sensors Whether it produces Data of this type, if sensor Generate type The data, Conversely, it is 0. When the drone flies towards the first user, it only begins transmitting data to the first user after reaching the user's area boundary. The time it takes for the drone to reach the user boundary is: in, The distance the drone flies to the boundary of the user area. This refers to the flight speed of the drone.

[0026] When the drone reaches above a user, if the remaining data volume is greater than the amount of data that can be transmitted over half the distance to the next user, it will hover. The distance between the two users is... The time it takes for this user to fly to the point where the distance to the next user is halfway is: The amount of data that can be transmitted during this route is: in, For the first A specific point in time.

[0027] The amount of data remaining upon reaching the user is Therefore, the amount of data that needs to be transmitted while hovering is The drone's hovering time above the user is: at this time ,like The drone will not hover above the user and will fly directly to the next user, transmitting all data en route. Indicates user The information age at which data is first received is represented as: In the formula, the first term is the unmanned aerial vehicle (UAV). The hovering time above all cluster heads within its sub-region; the second term is from the first... The third term is the flight time from the first cluster to the last cluster; Within each cluster The fourth item is the time for data transmission by each sensor node; the fifth item is the time for the data sharing to end; the sixth item is the time for the drone. The sixth item is the time taken to collect data within the area; the seventh item is the time it took for the drone to fly to the user area. Hover time per user Whether the drone needs to hover above the user, and if so, then... Conversely, it is 0. The eighth term is the first term. The time it takes for data to be distributed to each user. Therefore, the average AoI of all data collected from sensor nodes is defined as: In the formula, The total amount of data required by all users.

[0028] Example 2 This embodiment proposes an optimized method for drone-assisted data collection and distribution based on information age. The applied system includes: drones, sensor nodes, and users. The drone first collects data in the sensor node area, then other drones share data with the drone with the best energy level. Finally, the drone with the best energy level distributes the data to the user while flying. The optimization method includes the following steps: S1. Under the constraints of communication distance of sensor nodes, number of drones and signal-to-noise ratio threshold, establish the problem of minimizing average AoI; By optimizing the number of sensor clusters Regional division and drone trajectories Under constraints of communication distance between sensor nodes, number of drones, and signal-to-noise ratio threshold, minimize the average AoI of users. The problem of minimizing the information age of users acquiring data is defined as P1: Among them, constraints The signal-to-noise ratio threshold for communication between the UAV and sensor nodes is constrained. The total energy consumed by the drone must be less than its maximum usable energy, constraining... Each cluster head node communicates with only one drone, constraining... The constraint is that the distance between sensor member nodes and cluster head nodes cannot exceed the maximum communication distance of the nodes. Constraints for the type of sensor required by the user Each sensor detects only one type of data.

[0029] S2 decomposes the problem of minimizing the average AoI into three optimization sub-problems: the number of sensor node clusters, region partitioning, and UAV trajectory. The subproblem of optimizing the number of sensor node clusters is defined as P2: The region partitioning optimization subproblem is defined as P3: The UAV trajectory optimization sub-problem is defined as P4: S3 uses an improved K-means clustering method to solve the subproblem of optimizing the number of sensor node clusters, and clusters the sensor nodes in the region; it uses a geometric centroid distance region partitioning method to solve the subproblem of region partitioning, and divides the entire region into multiple sub-regions, with each UAV responsible for collecting data in one sub-region; it uses the 2-opt method with fixed start and end points to solve the UAV trajectory optimization subproblem, so as to minimize the average AoI.

[0030] S31, the process of solving the subproblem of optimizing the number of sensor node clusters using the improved K-means clustering method is as follows: based on the Euclidean distance between ground sensor nodes, the cluster head node is determined by dynamically optimizing the number and position of clusters. First, randomly select... Each node is designated as a cluster head, and each sensor node is assigned to the nearest cluster head, thus forming the initial cluster. Subsequently, for each cluster... Calculate the distance between the cluster head and its member nodes. If the maximum distance exceeds the preset clustering radius... If the above steps are not met, a new cluster head node needs to be added. The above steps are iteratively executed until the distance between all member nodes and their corresponding cluster heads is less than the cluster radius, and the node allocation results no longer change, thus finally determining the positions of all cluster heads.

[0031] S32, the process of solving the sub-problem of region partitioning optimization using the geometric centroid distance method is as follows: First, determine the number of sub-regions based on the number of drones, and then randomly select... Each cluster head serves as the initial sub-region center, based on the position coordinates of the cluster head nodes. For any sub-region obtained by partitioning Then the centroid of this region is In the formula For the first The number of cluster heads in each subregion, partitioning function Can be defined as Based on the partitioning function To determine the final region, The smaller the size, the more compact the area will be.

[0032] S33, the process of solving the UAV trajectory optimization subproblem using the 2-opt method with a fixed start and end point is as follows: First, based on the clusters within the region, find the cluster furthest from the region center and the nearest cluster in each region. The furthest cluster is the start point, and the nearest cluster is the end point. After determining the start and end points, an initial path is generated using the nearest neighbor algorithm, and then the 2-opt optimization algorithm is used to optimize the initial path. 2-opt is a heuristic algorithm for improving local search in path problems. It involves deleting two edges from the existing path and then reconnecting them in another way; if the new path is shorter, this change is accepted. The original path contains two edges. and The new path is then replaced with and The condition for exchanging to shorten the path is: Figure 2A schematic diagram of the flight trajectory during the sensor region clustering, region division, and UAV data acquisition stages is provided. Figure 2 Figure (a) shows a schematic diagram of sensor region clustering. Figure 2 Figures (b) and (c) show the sensor area segmentation and the UAV's flight trajectory during the data acquisition phase, respectively. It can be seen that the clustering and segmentation optimization strategy significantly reduces the total time required for UAV data acquisition, thereby effectively reducing the average AoI.

[0033] Figure 3 A schematic diagram of the drone's flight trajectory during the distribution phase is provided; it can be seen that optimizing the drone's data distribution trajectory greatly reduces the time it takes for the drone to distribute data to users, thereby effectively reducing the average AoI.

[0034] Based on a similar inventive concept, embodiments of the present invention also provide a computer storage medium storing a readable program that, when run by a processor, can execute the aforementioned drone-assisted data collection and distribution optimization method based on information age.

[0035] Based on a similar inventive concept, this invention provides an electronic device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described information age-based UAV-assisted data collection and distribution optimization method.

[0036] Based on a similar inventive concept, embodiments of the present invention also provide a computer program product, including computer instructions, which instruct a computing device to perform the operations corresponding to the above-described information age-based UAV-assisted data collection and distribution optimization method.

[0037] Example 3 To verify the performance of the method proposed in this invention, this invention considers comparing it with the following two algorithms: Algorithm 1: The drone trajectory is not optimized during data collection and distribution. During the collection phase, it flies randomly to the cluster head for collection, and during the data distribution phase, it randomly selects users to fly and distribute data.

[0038] Algorithm 2: The classic k-means algorithm is used for clustering during the data acquisition phase.

[0039] Compared to Algorithm 3, Algorithm 3 did not cluster the sensor nodes or divide the entire area into several sub-regions during the data distribution phase, nor did it classify the sensor nodes into different types. Furthermore, it did not consider the possibility of distributing data to users while the device is in flight.

[0040] In this embodiment, the relevant parameters of the system are shown in Table 1 below: Table 1 Truth Table Figure 4 The average AoI of the proposed method is compared with that of Comparison Algorithm 1, Comparison Algorithm 2, and Comparison Algorithm 3. As shown in the figure, the average AoI of all algorithms decreases with the increase in the number of drones. The algorithm proposed in this invention outperforms the other three algorithms in terms of average AoI. This is because in Comparison Algorithm 1, the drone randomly selects cluster heads and users during data collection and distribution, increasing the drone's flight time. In Comparison Algorithm 2, the traditional k-means algorithm is used for clustering, resulting in a larger number of clusters than the proposed algorithm, thus increasing the drone's flight time. In Comparison Algorithm 3, the drone needs to travel to each node to collect data during the data collection phase, leading to increased drone flight time. During the data distribution phase, Comparison Algorithm 3 still uses hovering distribution above users, without considering data transmission during flight, significantly increasing the data distribution time and thus increasing the average AoI. Therefore, it has the worst security performance among the three algorithms.

[0041] Figure 5 The figure shows how the average AoI changes with the number of drones when the number of drones increases, with different sensor cluster radii. As can be seen from the figure, when the number of drones is the same, the average AoI decreases as the cluster radius increases. This is because a larger cluster radius results in fewer clusters, thus reducing the flight time of the drones during data collection and consequently decreasing the total time spent on data acquisition. Therefore, the average AoI decreases with increasing cluster radius.

[0042] Figure 6 The trend of average AoI with the number of drones is presented under different drone flight speeds. It can be seen that, given a certain number of drones, the average AoI decreases as the flight speed increases. This is because, during data acquisition, higher flight speeds help reduce the flight time of drones from one cluster head to another, reducing the total time spent by drones collecting data, thus effectively lowering the average AoI.

[0043] Figure 7The figure shows how the average AoI changes with the number of drones when the number of sensor nodes increases. As can be seen from the figure, when the number of drones is the same, the average AoI increases with the increase of sensor nodes. This is because, under otherwise constant conditions, an increase in the number of sensor nodes leads to a corresponding increase in the number of clusters, increasing the hovering and flight times of drones during data collection, and also increasing the time spent distributing data. Therefore, the average AoI increases with the increase of the number of sensor nodes.

[0044] Figure 8 The figure shows how the average AoI changes with the number of drones as the number of sensor node classes increases. The data size of different sensor node classes increases with the number of classes. For four classes, the data size per sensor of different types is 60KB, 65KB, 70KB, and 75KB, respectively. The data size increases sequentially for five and six classes, resulting in an increase in the total data size of sensor nodes. As can be seen from the figure, when the number of drones is the same, the average AoI increases with the number of sensor node classes. This is because, all other things being equal, as the number of sensor node classes increases, the hovering time of the drones during data acquisition increases accordingly, and the time for drones to distribute data also increases accordingly. Therefore, the average AoI increases with the number of sensor node classes.

[0045] Example 4 This embodiment proposes a drone-assisted data collection and distribution optimization device based on information age, specifically including: Problem building module: Under the constraints of communication distance of sensor nodes, number of drones and signal-to-noise ratio threshold, establish the problem of minimizing average AoI; Problem decomposition module: The problem of minimizing the average AoI is decomposed into three optimization sub-problems: the number of sensor node clusters, region partitioning, and UAV trajectory; Problem-solving module: The improved K-means clustering method is used to solve the subproblem of optimizing the number of sensor node clusters, and the sensor nodes in the region are clustered. The geometric centroid distance method is used to solve the subproblem of region partitioning, so that the entire region is divided into multiple sub-regions, and each UAV is responsible for collecting data in one sub-region. The 2-opt method with fixed start and end points is used to solve the subproblem of UAV trajectory optimization, so as to minimize the average AoI.

[0046] The methods of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded via a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses the code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for performing the methods shown herein.

[0047] 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 to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. An optimized method for drone-assisted data collection and distribution based on information age, characterized in that, The applied system includes: drones, sensor nodes, and users; the drones first collect data in the sensor node area, then other drones share data with the drone with the best energy, and finally, the drone with the best energy distributes the data to the users while flying; the optimization method includes: Under the constraints of communication distance of sensor nodes, number of drones and signal-to-noise ratio threshold, a problem is established to minimize the average AoI; The problem of minimizing the average AoI is decomposed into three optimization sub-problems: the number of sensor node clusters, region partitioning, and UAV trajectory. An improved K-means clustering method is used to solve the subproblem of optimizing the number of sensor node clusters, and the sensor nodes in the region are clustered. The region partitioning method based on geometric centroid distance is used to solve the subproblem of region partitioning, so that the entire region is divided into multiple sub-regions, and each UAV is responsible for collecting data in one sub-region. The UAV trajectory optimization subproblem is solved using the 2-opt method with fixed start and end points, so as to minimize the average AoI.

2. The UAV-assisted data collection and distribution optimization method based on information age as described in claim 1, characterized in that, The other drones use frequency division multiple access (FDMA) to share the collected data with the drone with the best energy.

3. The UAV-assisted data collection and distribution optimization method based on information age according to claim 1, characterized in that, The problem of minimizing the average AoI is defined as P1: in, Represents the number of clusters, Represents the size of the region. and This represents the trajectory of the drone during data collection and distribution. For the number of users, This represents the number of sensor nodes. For the number of drones, The total amount of data required by all users. For users Is it necessary? Type of data, For sensors Whether it produces Type of data, For the data type of the sensor node, The cluster head can only communicate with one drone. For users, drones with all the data are starting to be used. The observation time for distributing data, The AoI for the drone to begin distributing data to the user. The signal-to-noise ratio of the transmission link between the cluster head and the drone. The signal-to-noise ratio threshold for the transmission link. The total energy consumed by the drone, For the maximum energy of the drone, This represents the distance between a regular node and the cluster head. Cluster radius; constraint The signal-to-noise ratio threshold for communication between the UAV and sensor nodes is constrained. The total energy consumed by the drone must be less than its maximum usable energy, constraining... Each cluster head node communicates with only one drone, constraining... The constraint is that the distance between sensor member nodes and cluster head nodes cannot exceed the maximum communication distance of the nodes. The type of sensor required by the user. Constraints. Each sensor only senses one type of data.

4. The UAV-assisted data collection and distribution optimization method based on information age according to claim 3, characterized in that, The subproblem of optimizing the number of sensor node clusters is defined as P2: The region partitioning optimization subproblem is defined as P3: The UAV trajectory optimization sub-problem is defined as P4: 。 5. The UAV-assisted data collection and distribution optimization method based on information age according to claim 1, characterized in that, The process of solving the subproblem of optimizing the number of sensor node clusters using the improved K-means clustering method is as follows: based on the Euclidean distance between ground sensor nodes, the cluster head node is determined by dynamically optimizing the number and position of clusters.

6. The UAV-assisted data collection and distribution optimization method based on information age according to claim 5, characterized in that, The process of solving the subproblem of region partitioning optimization using the geometric centroid distance method is as follows: determine the number of subregions based on the number of UAVs, and randomly select... Each cluster head is used as the initial sub-region center. Based on the distance of each other cluster head node to the sub-region center, the remaining cluster head nodes are assigned to the nearest sub-region.

7. The UAV-assisted data collection and distribution optimization method based on information age according to claim 6, characterized in that, The process of solving the UAV trajectory optimization subproblem using the 2-opt method with a fixed start and end point is as follows: Based on the clusters in the region, find the cluster farthest from the center of the region and the nearest cluster in each region. The farthest cluster is the start point, and the nearest cluster is the end point. After determining the start and end points, generate the initial path according to the nearest neighbor algorithm, and then optimize the initial path using the 2-opt optimization algorithm.

8. A drone-assisted data collection and distribution optimization device based on information age, comprising the method described in any one of claims 1-7, characterized in that, include: Problem building module: Under the constraints of communication distance of sensor nodes, number of drones and signal-to-noise ratio threshold, establish the problem of minimizing average AoI; Problem decomposition module: The problem of minimizing the average AoI is decomposed into three optimization sub-problems: the number of sensor node clusters, region partitioning, and UAV trajectory; Problem-solving module: The improved K-means clustering method is used to solve the sub-problem of optimizing the number of sensor node clusters, and the sensor nodes in the region are clustered. The geometric centroid distance region partitioning method is used to solve the sub-problem of region partitioning, so that the entire region is divided into multiple sub-regions, and each UAV is responsible for collecting data in one sub-region. The UAV trajectory optimization subproblem is solved using the 2-opt method with fixed start and end points, minimizing the average AoI.

9. A computer storage medium storing a readable program, characterized in that, When the program runs, it can instruct the computing device to execute the information age-based UAV-assisted data collection and distribution optimization method as described in any one of claims 1-7.

10. An electronic device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the UAV-assisted data collection and distribution optimization method based on information age as described in any one of claims 1-7.

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