A Data-Driven Heterogeneous UAV Swarm-Assisted Wireless Sensor Data Collection Method

Abstract

The present invention relates to the technical field of the intersection of unmanned aerial vehicle and wireless sensor network technologies, and specifically to a data-driven heterogeneous unmanned aerial vehicle swarm-assisted wireless sensor data collection method, including Step 1: constructing a hierarchical data collection system for heterogeneous unmanned aerial vehicle formations; Step 2: determining the communication coverage range of data collection unmanned aerial vehicles for the wireless sensor network; Step 3: performing the first round of regular hexagon coverage; Step 4: performing the second round of regular hexagon coverage; Step 5: optimizing the initial flight path of communication unmanned aerial vehicles; Step 6: determining the central coordinate information of the task area of data collection unmanned aerial vehicles; Step 7: performing dynamic task allocation for data unmanned aerial vehicle formations; Step 8: adjusting the positions of data collection unmanned aerial vehicles; Step 9: performing dynamic position adjustment for communication unmanned aerial vehicles; Step S10: determining whether the data collection of wireless sensor nodes is completed. If so, loop through Steps S7 to S10 to perform the next round of tasks; if not, end the task.

Description

Technical Field

[0001] The present invention relates to the technical field at the intersection of unmanned aerial vehicle and wireless sensor network technologies, and specifically to a data-driven heterogeneous unmanned aerial vehicle swarm-assisted wireless sensor data collection method. Background Art

[0002] Since the birth of wireless sensor networks, due to their flexible node deployment and diverse network structures, this technology has been widely applied in fields such as industry, agriculture, and environmental monitoring, and has shown development trends such as an increasing node scale, a wide distribution range, and a complex application environment. Especially in the wild environment, building a large-scale wireless sensor network faces many challenges, such as node deployment, positioning, and data transmission reliability in complex environments.

[0003] In recent years, the rapid progress of unmanned aerial vehicle technology has brought new solutions to address these challenges. Unmanned aerial vehicles, with their flexibility, mobility, and autonomy, have become important tools for data transmission and network maintenance in wireless sensor networks. However, as the scale of the sensor network expands, a single unmanned aerial vehicle has become difficult to meet the requirements of rapid data collection in large-scale networks. Therefore, designing a reasonable heterogeneous unmanned aerial vehicle formation cooperation mechanism to optimize data collection efficiency and extend network lifetime, while considering the energy consumption characteristics and data volume of different nodes, has become a key issue in current research. Summary of the Invention

[0004] In order to solve the problem that the existing single unmanned aerial vehicle has difficulty meeting the requirements of rapid data collection in large-scale networks, the present invention provides a data-driven heterogeneous unmanned aerial vehicle swarm-assisted wireless sensor data collection method.

[0005] The present invention proposes a data-driven heterogeneous unmanned aerial vehicle swarm-assisted wireless sensor data collection method, which is a method for data-driven heterogeneous unmanned aerial vehicle formation-assisted large-scale wireless sensor network data collection; this method adopts a hierarchical network architecture, and the unmanned aerial vehicle formation composed of two types works together, and through dynamic task allocation and data perception strategies, efficient collection and energy balance of large-scale sensor node data are achieved.

[0006] The core innovation of the present invention lies in introducing a heterogeneous unmanned aerial vehicle formation, dynamically adjusting the task allocation and flight path of the unmanned aerial vehicles according to the data distribution and remaining energy of the sensor nodes, so as to minimize the energy consumption of data transmission, improve the data transmission rate, and extend the operation time of the entire network; the specific technical solutions of the present invention are as follows:

[0007] A data-driven heterogeneous unmanned aerial vehicle swarm-assisted wireless sensor data collection method, the method comprising the following steps:

[0008] Step S1: Determine the distribution area and its range of the wireless sensor nodes, and construct a hierarchical data collection system for heterogeneous UAV formations, including data collection UAVs and communication UAVs ;

[0009] Step S2: Determine the communication coverage range of the data collection UAVs for the wireless sensor nodes;

[0010] Step S3: According to the communication coverage range obtained in Step S2, conduct the first-round regular hexagon coverage division of the distribution area of the wireless sensor nodes to form the first-layer honeycomb coverage structure, i.e., honeycomb coverage 1, and obtain the central coordinates of all regular hexagon areas,

[0011] which are used as the task areas of the data collection UAVs; ;

[0012] Step S4: Calculate the communication range between the communication UAVs and the data collection UAVs, and further conduct the second-round regular hexagon coverage division of the distribution area of the wireless sensor nodes to establish the second-layer honeycomb coverage structure, i.e., honeycomb coverage 2, and obtain the initial track points of the communication UAVs ; ;

[0013] Step S5: Take the initial track points of the communication UAVs as the input, optimize the initial flight path of the communication UAVs, and obtain the track matrix of the optimized communication UAVs;

[0013] Step S6: According to the order of the track points in the track matrix , successively determine the central coordinate information of the task areas of the data collection UAVs included in the communication coverage area of each communication UAV ;

[0014] Step S7: According to the track matrix of the communication UAVs, conduct dynamic task allocation for the data UAV formation under the guidance of the communication UAVs;

[0015] Step S8: The data collection UAVs enter the corresponding task areas, sense the data volume carried by the wireless sensor nodes, and conduct data-driven data collection position adjustment to reduce the energy consumption of the wireless sensor nodes;

[0016] Step S9: The communication UAVs conduct dynamic position adjustment according to the positions of the data collection UAV formation to improve the data transmission rate;

[0017] Step S10: Determine whether the data collection of all wireless sensor nodes is completed; if so, loop through Steps S7 to S10 to perform the next round of tasks; if not, end the task.

[0018] Further, in step S1: Determine the distribution area and its range of the wireless sensor nodes, and construct a hierarchical data collection system for heterogeneous UAV formations, including data collection UAVs and communication UAVs ; specifically as follows:

[0019] Step S1.1: Construct a heterogeneous UAV data collection and transmission network; this heterogeneous UAV data collection and transmission network includes two types of UAVs and wireless sensor nodes: The two types of UAVs are data collection UAVs and 1 communication UAV ; The data collection UAVs are numbered ; The number of wireless sensor nodes is , numbered ;

[0020] Step S1.2: The heterogeneous UAV data collection and transmission network is divided into three layers; among them, the first layer is the wireless sensor node layer, responsible for data acquisition; the second layer is the data collection UAV layer, responsible for collecting sensor data within the specified task area; the third layer is the communication UAV layer, responsible for UAV formation mission planning, and at the same time as a relay, using the on-board radio station to transmit the data collected by the data collection UAVs to the cloud or base station in an amplify-and-forward manner.

[0021] Further, in step S2: Determine the communication coverage range of the data collection UAV for the wireless sensor nodes; specifically as follows:

[0022] Step S2.1: Calculate the distance between the wireless sensor node and the data collection UAV at time t, expressed as:

[0023] ; Formula (1);

[0024] where represents the height of the data collection UAV , , is the coordinate of the wireless sensor node and the data collection UAV in the two-dimensional space;

[0025] Step S2.2: Calculate the channel power gain between the data collection UAV and the wireless sensor node , expressed as:

[0026] ; Formula (2);

[0027] Wherein, represents the channel power gain at 1 m;

[0028] Step S2.3: According to the channel power gain obtain the uplink transmission rate of the wireless sensor node , expressed as: ; Formula (3);

[0029] ; Formula (3);

[0030] Wherein, is the channel bandwidth, is the transmission power of the wireless sensor node , represents the Gaussian white noise power at the receiving end of the data collection UAV ;

[0031] Step S2.4: During the data collection process, the uplink transmission rate of the wireless sensor node must be greater than or equal to the minimum data transmission rate , that is, satisfy ;

[0032] Step S2.5: According to formula (3), convert the minimum data transmission rate constraint into a distance constraint, expressed as:

[0033] ; Formula (4);

[0034] ; Formula (5);

[0035] Wherein, is the maximum distance between the wireless sensor node and the data collection UAV under the constraint of the uplink transmission rate ;

[0036] Step S2.6: The data collection UAV uses a directional antenna with a half-beam width of for communication with the wireless sensor node , and the radiation direction of the directional antenna is vertically pointed to the ground;

[0037] Step S2.7: Calculate the communication coverage radius and the maximum flight altitude of the data collection UAV , expressed as:

[0038] ; Formula (6);

[0039] ; Formula (7).

[0040] Furthermore, in step S3: according to the communication coverage range obtained in step S2, the distribution area of the wireless sensor nodes is divided by regular hexagons in the first round to form the first-layer honeycomb coverage structure, that is, honeycomb coverage 1, and the central coordinates of all regular hexagon areas are obtained , as the mission area of the data collection drone; specifically as follows:

[0041] Step S3.1: The distribution area of the wireless sensor nodes is x km × y km. A Cartesian coordinate system is established with the origin at . According to the communication coverage radius of the data collection drone , calculate the central coordinates of each regular hexagon:

[0042] ; Formula (8);

[0043] Step S3.2: Establish a matrix to store the central coordinates of each regular hexagon , then:

[0044] ; Formula (9);

[0045] where respectively represent dividing the distribution area of the wireless sensor nodes into regular hexagon areas with a quantity of , respectively represent the row and column indices of each hexagon area;

[0046] Step S3.3: According to the central coordinates of the regular hexagon , with the communication coverage radius of the data collection drone as the side length of the regular hexagon, calculate the vertex coordinates of each regular hexagon to complete the regular hexagon coverage of the mission area. Each regular hexagon area is the mission area of the data collection drone .

[0047] Furthermore, in step S4: calculate the communication range between the communication drone and the data collection drone, further divide the distribution area of the wireless sensor nodes by regular hexagons in the second round, establish the second-layer honeycomb coverage structure, that is, honeycomb coverage 2, and obtain the initial waypoints of the communication drone ; Specifically as follows:

[0048] Step S4.1: Input the maximum transmission power of the data collection drone and the Gaussian white noise power at the receiving end of the communication drone . Referring to Step S2 and formulas (1) to (5), calculate the maximum distance between the communication drone and the data collection drone while ensuring the minimum data transmission rate. When the distance between the communication drone and the data collection drone is greater than , the communication connection is lost; When the distance between the communication drone and the data collection drone is greater than , the communication connection is lost;

[0049] Step S4.2: The communication drone and the data collection drone communicate using independent frequency bands and are equipped with independent omnidirectional antennas. The coverage radius of the communication drone is related to the maximum height difference between the communication drone and the data collection drone . When the flight altitudes of the communication drone and all data collection drones are the same, the coverage range of the communication drone reaches the maximum, that is, ; ; ;

[0050] Step S4.3: Referring to Step S3, according to the coverage radius of the communication drone , perform a second round of regular hexagon coverage on the distribution area of the wireless sensor nodes to obtain regular hexagon areas, and the corresponding regular hexagon center coordinates are , ; ;

[0051] Step S4.4: Establish a matrix to store the center coordinates of each regular hexagon obtained from the second round of regular hexagon coverage . Then:

[0052] ; Formula (10);

[0053] Step S4.5: Each center coordinate of the regular hexagon in the matrix As a communication UAV as the initial waypoint.

[0054] Further, in step S5: taking the initial waypoint of the communication UAV as the input, optimizing the initial flight path of the communication UAV to obtain the optimized waypoint matrix of the communication UAV ; specifically as follows:

[0055] Step S5.1: Flatten the matrix by columns into a matrix of size ;

[0056] Step S5.2: Optimize the flight path of the communication UAV and establish the following optimization model:

[0057] ; formula (11);

[0058] ; formula (12);

[0059] wherein, is used to mark whether the connection line between any two initial waypoints is planned into the flight path of the communication UAV ; represents the Kth initial waypoint and the Lth initial waypoint whose connection line is planned as the flight route of the communication UAV , otherwise ; formulas (11) and (12) represent sorting the centers of regular hexagons in the matrix to obtain a new matrix . When the communication UAV uses the initial waypoints in the new matrix as waypoints and flies in the order in the new matrix , the total flight distance is the shortest.

[0060] Further, in step S6: according to the order of the waypoints in the waypoint matrix , successively determine the central coordinate information of the task area of the data collection UAV included in the communication coverage area of each communication UAV ; specifically as follows:

[0061] Step S6.1: The data transmission power of the UAV is usually several times or even dozens of times that of the wireless sensor node. Therefore, the coverage radius of the communication UAV is greater than the communication coverage radius of the data collection UAV , then ; Therefore, within the regular hexagon centered at the center coordinates of the communication UAV corresponding to the center coordinates of the regular hexagon as the center, there are multiple regular hexagons centered at the center coordinates of the regular hexagon covered by the data collection UAVs divided by the coverage range; as the center ;

[0062] Step S6.2: According to the flight order of the flight path points of the communication UAV , that is, the track matrix , determine the th task area within the communication UAV covered by the data collection UAV divided by the coverage range; as the center of the regular hexagon, and establish a matrix , store the corresponding center coordinates of the regular hexagon .

[0063] Furthermore, the step S7: According to the track matrix of the communication UAV , perform dynamic task allocation for the data UAV formation under the guidance of the communication UAV; specifically as follows:

[0064] Step S7.1: Introduce the concept of task rounds and initialize the task rounds ; The data collection UAV formation completes data collection of the wireless sensor nodes within the specified area once, ;

[0065] Step S7.2: Establish a task library matrix , which is used to save the center coordinates of the task areas that the data collection UAV can select in the th round of tasks, and initialize the task library matrix as an empty set; among them, the task library matrix is dynamically changing. In each round of tasks, the number of task points in the task library matrix needs to be greater than or equal to the number of data collection UAVs ; If the dimension of the task library matrix is , then it needs to satisfy ; After the corresponding task point is selected, it is deleted from the task library matrix ; When , it indicates that new task points need to be added, that is ;

[0066] Step S7.3: Establish the task allocation model for the data collection UAV formation in the th round; that is, in the task library matrix , according to the current position ( ) of the data collection UAV , find a task point for each data collection UAV , and the flight altitude of the data collection UAV

[0067] ; formula (13);

[0068] ; formula (14);

[0069] where , is a penalty variable, which ensures that a task point is only selected once. When a certain task point is selected for the first time , and when it is selected again ;

[0070] Step S7.4: After obtaining the initial position of the data collection UAV , the data collection UAV formation adjusts its flight speed and collaboratively arrives at the corresponding task area.

[0071] Furthermore, in step S8: The data collection UAV enters the corresponding task area, senses the data volume carried by the wireless sensor nodes, and adjusts the data collection position driven by data to reduce the energy consumption of the wireless sensor nodes; specifically as follows:

[0072] Step S8.1: Data perception by the data collection UAV: After obtaining the task area information of each data collection UAV , the data collection UAV flies to the center of the designated task area ; broadcasts its own position information. After receiving the broadcast, the wireless sensor node packs and sends its position information, remaining energy and the data volume to be sent to the data collection UAV . The data collection UAV obtains the number of wireless sensor nodes in this area, denoted as ;

[0073] Step S8.2: When the distance between the data collection drone and the wireless sensor node is the communication energy consumption is calculated as follows:

[0074] ; Formula (15);

[0075] wherein respectively represent the data transmission energy consumption and the data reception energy consumption; represents the data volume of the wireless sensor node ; represents the operating energy consumption of the communication module, is the data aggregation energy consumption; respectively represent the energy consumption required to amplify the signal, which depends on the transceiver distance and the bit error rate;

[0076] Step S8.3: Normalize the remaining energy and the communication energy consumption of the wireless sensor node to obtain the normalized remaining energy and the normalized communication energy consumption values:

[0077] ; Formula (16);

[0078] wherein ; , , , respectively represent the highest remaining energy value, the lowest remaining energy value, the highest communication energy consumption, and the lowest communication energy consumption among the wireless sensor network nodes;

[0079] Step S8.4: Establish that in the th round of tasks, when the data collection drone completes the data collection of the wireless sensor nodes with the number of within its communication coverage area, the total cost is:

[0080] ; Formula (17);

[0081] Optimally adjusting the data collection position of the data collection drone is attributed to finding an optimal data collection position according to the data volume carried by the wireless sensor node , maximizing the sum of the remaining energy values of all wireless sensor nodes in the area, the objective function and constraints are as follows:

[0082] ; Equation (18);

[0083] ; Equation (19);

[0084] Among them, Equation (19) ensures that, under the guarantee of the coverage range, the data collection UAV can still ensure the coverage of all wireless sensor nodes in the area after the position adjustment ; Equations (18) and (19) are linear programming problems, and it can be set to convert them into minimization problems;

[0085] Step S8.5: Obtain the optimal data collection position After that, the data collection UAV performs position adjustment and starts data collection.

[0086] Furthermore, in Step S9: The communication UAV performs dynamic position adjustment according to the position of the data collection UAV formation to improve the data transmission rate;

[0087] Step S9.1: After the data collection UAV calculates the optimal data collection position , it uploads the corresponding coordinates to the communication UAV ;

[0088] Step S9.2: Assume that the communication UAV has the coordinates in the -th round of tasks. At this coordinate, the data transmission rate from the data collection UAV formation to the communication UAV reaches the maximum; Combining Equations (1)-(3) to obtain the total system data transmission achievable rate is:

[0089] ; Equation (20);

[0090] Among them, , , , respectively represent the maximum transmission power of the data collection UAV , the communication channel bandwidth of the communication UAV and the data collection UAV , and the Gaussian white noise power at the receiving end of the communication UAV ; Indicates the reference channel power gain at 1 m.

[0091] Step S9.3: Communication UAV The optimal position optimization model is:

[0092] ; Equation (21);

[0093] ; Equation (22);

[0094] Equations (21) and (22) represent finding the optimal position of the communication UAV such that the overall data throughput from the data collection UAV formation to the communication UAV reaches the maximum; the constraint conditions ensure the communication connection between the communication UAV and the data collection UAV ; and the data collection UAV ;

[0095] Step S9.4: Solve Equations (21) and (22), convert the problem of maximizing the overall data throughput into a minimization problem, i.e.: The upper and lower bounds of the solution range and the constraint conditions remain unchanged.

[0096] Compared with the prior art, the present invention proposes a data-driven heterogeneous UAV swarm-assisted wireless sensor data collection method, which has the following advantages:

[0097] 1. Hierarchical network architecture: A hierarchical network is constructed using heterogeneous UAV formations, including data collection UAVs and communication UAVs. The hierarchical design improves data collection efficiency and network coverage, and through the relay and amplify-and-forward mechanisms, ensures stable and efficient data transmission in large-scale wireless sensor networks.

[0098] 2. Flexible data collection strategy: Intelligent data collection strategies are formulated for the differences in the positions, data volumes, and energies of wireless sensor nodes, reducing the energy consumption of node information transmission, balancing the energies of network nodes, and extending the service life of the sensor network.

[0099] 3. Dynamic multi-UAV formation cooperative operation allocation strategy: Through the collaborative work and reasonable scheduling among UAVs, the problem that a single UAV cannot handle large-scale wireless sensor networks is solved, improving the timeliness and energy efficiency of data collection tasks. Brief Description of the Drawings

[0100] Figure 1 is a schematic diagram of the system framework of the present invention.

[0101] Figure 2 is the implementation flowchart of the present invention.

[0102] Figure 3 It is a schematic diagram of the two-round task area division based on regular hexagon coverage in the present invention.

[0103] Figure 4 It is a schematic diagram of the initial trajectory and waypoint sequence planning of the communication UAV in the present invention.

[0104] Figure 5 It is the flight route of the data collection UAV formation and the dynamic adjustment of the UAV positions in the present invention. Detailed implementation manners

[0105] The present invention designs a data-driven heterogeneous UAV swarm-assisted wireless sensor data collection method, which uses a heterogeneous UAV formation to implement an efficient hierarchical data collection system. This method optimizes the data acquisition process of a large-scale wireless sensor network through the hierarchical structure of the UAV formation, and solves the problem that a single UAV cannot meet the requirements of large-scale and efficient data collection.

[0106] A data-driven heterogeneous UAV swarm-assisted wireless sensor data collection method, as shown in the attached Figure 2 figures, the method includes the following steps:

[0107] Step S1: Determine the distribution area and its scope of the wireless sensor nodes, and construct a hierarchical data collection system for the heterogeneous UAV formation, including data collection UAVs and communication UAVs ; specifically as follows:

[0108] Step S1.1: Construct a heterogeneous UAV data collection and transmission network; this heterogeneous UAV data collection and transmission network includes two types of UAVs and wireless sensor nodes: the two types of UAVs are data collection UAVs and 1 communication UAV ; the data collection UAVs are numbered ; the number of wireless sensor nodes is and they are numbered ;

[0109] Step S1.2: The heterogeneous UAV data collection and transmission network is divided into three layers; among them, the first layer is the wireless sensor node layer, which is responsible for data acquisition; the second layer is the data collection UAV layer, which is responsible for collecting sensor data in the specified task area; the third layer is the communication UAV layer, which is responsible for UAV formation task planning and, at the same time, as a relay, uses the on-board radio station to transmit the data collected by the data collection UAVs to the cloud or base station in an amplify-and-forward manner.

[0110] Step S2: Determine the data collection UAV For the communication coverage of wireless sensor nodes, specifically as follows:

[0111] Step S2.1: Calculate the distance between the wireless sensor node and the data collection drone at time t, denoted as: :

[0112] ; Formula (1);

[0113] where, represents the height of the data collection drone , , is the coordinate of the wireless sensor node and the data collection drone in the two-dimensional space;

[0114] Step S2.2: Calculate the channel power gain between the data collection drone and the wireless sensor node , denoted as:

[0115] ; Formula (2);

[0116] where, represents the channel power gain at 1m;

[0117] Step S2.3: Obtain the uplink transmission rate of the wireless sensor node from the channel power gain , denoted as:

[0118] ; Formula (3);

[0119] where, is the channel bandwidth, is the transmission power of the wireless sensor node , represents the Gaussian white noise power at the receiving end of the data collection drone ;

[0120] Step S2.4: During the data collection process, the uplink transmission rate of the wireless sensor node must be greater than or equal to the minimum data transmission rate , that is, satisfy ;

[0121] Step S2.5: According to Formula (3), the minimum data transmission rate The constraint is transformed into a distance constraint, expressed as:

[0122] ; Formula (4);

[0123] ; Formula (5);

[0124] where is the maximum distance between the wireless sensor node and the data collection UAV under the uplink transmission rate constraint;

[0125] Step S2.6: The data collection UAV uses a directional antenna with a half-beam width of for communication with the wireless sensor node , and the radiation direction of the directional antenna is vertically pointed to the ground;

[0126] Step S2.7: Calculate the communication coverage radius and the maximum flight altitude of the data collection UAV , expressed as:

[0127] ; Formula (6);

[0128] ; Formula (7).

[0129] Step S3: According to the communication coverage range obtained in Step S2, perform the first-round regular hexagon coverage division on the distribution area of the wireless sensor nodes to form the first-layer cellular coverage structure, i.e., cellular coverage 1, and obtain the central coordinates of all regular hexagon areas as the task area of the data collection UAV; In the UAV-assisted wireless data transmission network, the cellular coverage range affects the communication quality and transmission efficiency, as follows:

[0130] Step S3.1: The distribution area of the wireless sensor node is x km × y km. Establish a Cartesian coordinate system with the origin at . According to the communication coverage radius of the data collection UAV , calculate the central coordinates of each regular hexagon:

[0131] ; Formula (8);

[0132] Step S3.2: Establish a matrix to store the central coordinates of each regular hexagon, then:

[0133] ; Formula (9);

[0134] Among them, respectively represent dividing the distribution area of the wireless sensor nodes into regular hexagon areas with the number of ; respectively represent the row and column indexes of each hexagon area;

[0135] Step S3.3: According to the center coordinates of the regular hexagon, taking the communication coverage radius of the data collection drone as the side length of the regular hexagon, calculate the vertex coordinates of each regular hexagon to complete the regular hexagon coverage of the mission area. Each regular hexagon area is the mission area of the data collection drone .

[0136] Step S4: Calculate the communication range between the communication drone and the data collection drone, further perform a second round of regular hexagon coverage division on the distribution area of the wireless sensor nodes, establish a second-layer cellular coverage structure, that is, cellular coverage 2, and obtain the initial waypoints of the communication drone; specifically as follows:

[0137] Step S4.1: Input the maximum transmission power of the data collection drone , the Gaussian white noise power at the receiving end of the communication drone , refer to Steps S2, Formula (1) to Formula (5), and calculate to obtain the maximum distance between the communication drone and the data collection drone on the basis of ensuring the minimum data transmission rate . When the distance between the communication drone and the data collection drone is greater than , the communication connection is lost;

[0138] Step S4.2: The communication drone and the data collection drone communicate using independent frequency bands and are equipped with independent omnidirectional antennas. The coverage radius of the communication drone is related to the maximum height difference between the communication drone and the data collection drone . When the communication drone and all data collection drones When the flight altitudes are the same, the communication UAV coverage range reaches the maximum, that is, ;

[0139] Step S4.3: Referring to Step S3, according to the communication UAV coverage radius perform a second round of regular hexagon coverage on the distribution area of the wireless sensor nodes to obtain regular hexagon areas, and the corresponding regular hexagon center coordinates are , ;

[0140] Step S4.4: Establish a matrix to save the center coordinates of each regular hexagon obtained from the second round of regular hexagon coverage , then:

[0141] ; Formula (10);

[0142] Step S4.5: Take each regular hexagon center coordinate in the matrix as the initial waypoint of the communication UAV .

[0143] Step S5: Take the initial waypoints of the communication UAV as the input, optimize the initial flight path of the communication UAV, and obtain the optimized waypoint matrix of the communication UAV; specifically as follows:

[0144] Step S5.1: Flatten the matrix column by column into a matrix of size ;

[0145] Step S5.2: Optimize the flight path of the communication UAV and establish the following optimization model:

[0146] ; Formula (11);

[0147] ; Formula (12);

[0148] where is used to mark whether the connection line between any two initial waypoints is planned into the flight path of the communication UAV ; represents the Kth initial waypoint and the Lth initial waypoint Link is planned as a communication drone flight path, otherwise ; Formula (11) and Formula (12) represent the matrix Sort the centers of the inner regular hexagons to get a new matrix , communication drone The new matrix The initial track point in the path is taken as the path point, and the new matrix When flying in sequence, the total flight distance is the shortest.

[0149] Step S6: According to the track matrix The sequence of internal track points determines each communicating drone one by one The center coordinate information of the data collection drone mission area contained in the communication coverage area is as follows:

[0150] Step S6.1: The data transmission power of the UAV is usually several times or even dozens of times that of the wireless sensor node, so the communication UAV Coverage radius Bigger than a data-collecting drone Communication coverage radius ,but Therefore, in communication drones The corresponding coordinates of the center of the regular hexagon A regular hexagon centered It includes multiple data collection drones Coverage area divided by regular hexagon center coordinates A regular hexagon centered ;

[0151] Step S6.2: Follow the communication drone The flight sequence of the flight path points, i.e. the track matrix , determine the communication drone No. mission area Data collection drones included Coverage area divided by regular hexagon center coordinates A regular hexagon centered , and create the matrix ,storage The corresponding regular hexagon center coordinates .

[0152] Step S7: According to the track matrix of the communication drone , to carry out dynamic task allocation of data drone formation under the guidance of communication drone; the details are as follows:

[0153] Step S7.1: Introduce the concept of task rounds and initialize the task rounds. ; The data collection UAV formation completes data collection for the wireless sensor nodes within the specified area once. ;

[0154] Step S7.2: Establish a task library matrix , which is used to save the central coordinates of the optional task areas for the data collection UAVs in the round of tasks, and initialize the task library matrix as an empty set; among them, the task library matrix is dynamically changing. In each round of tasks, the number of task points in the task library matrix needs to be greater than or equal to the number of data collection UAVs ; If the dimension of the task library matrix is , then it needs to satisfy ; After the corresponding task point is selected, it is deleted from the task library matrix ; When , it indicates that new task points need to be added, that is ;

[0155] Step S7.3: Establish the task allocation model for the data collection UAV formation in the round; that is, within the task library matrix , according to the current position ( ) of the data collection UAV , find a task point for each data collection UAV , where is the flight altitude of the data collection UAV

[0156] ; Formula (13);

[0157] ; Formula (14);

[0158] Among them, , is a penalty variable, which ensures that the task point is only selected once. When a certain task point is selected for the first time , when it is selected again ;

[0159] Step S7.4: Obtain the data collection UAV After the initial position, the data collection UAV formation arrives at the corresponding mission area collaboratively by adjusting the flight speed.

[0160] Step S8: The data collection UAV enters the corresponding mission area, senses the data volume carried by the wireless sensor nodes, and adjusts the data collection position driven by data to reduce the energy consumption of the wireless sensor nodes; specifically as follows:

[0161] Step S8.1: Data perception by the data collection UAV: After obtaining the mission area information of each data collection UAV, the data collection UAV flies to the center of the designated mission area ; broadcasts its own position information, and after the wireless sensor nodes receive the broadcast, they package and send their position information, remaining energy and the data volume to be sent to the data collection UAV , and the data collection UAV obtains the number of wireless sensor nodes in this area, denoted as ;

[0162] Step S8.2: When the distance between the data collection UAV and the wireless sensor node is , the communication energy consumption is calculated as follows:

[0163] ; Formula (15);

[0164] Among them, respectively represent the data transmission energy consumption and the data reception energy consumption; represents the data volume of the wireless sensor node ; represents the working energy consumption of the communication module, is the data aggregation energy consumption; respectively represent the energy consumption required to amplify the signal, which depends on the transceiver distance and the bit error rate;

[0165] Step S8.3: Normalize the remaining energy and communication energy consumption of the wireless sensor node to obtain the values of the normalized remaining energy and the normalized communication energy consumption :

[0166] ; Formula (16);

[0167] Among them, ; , , , Respectively Wireless sensor network nodes The highest remaining energy value, the lowest remaining energy value, the highest communication energy consumption, and the lowest communication energy consumption;

[0168] Step S8.4: Establish During the mission, data collection drone In the communication coverage area, the number of When collecting data from wireless sensor nodes, the total cost for:

[0169] ;Formula (17);

[0170] Data collection drones The optimization adjustment of data collection location is attributed to the wireless sensor node The amount of data carried and its distribution to find an optimal data collection location , so that the sum of the residual energy values ​​of all wireless sensor nodes in the area is maximized. The objective function and constraints are as follows:

[0171] ;Formula (18);

[0172] ;Formula (19);

[0173] Among them, formula (19) ensures that the data collection drone After the position adjustment, it can still ensure that all wireless sensor nodes in the area Coverage; Formula (18) and Formula (19) are linear programming problems, which can be Convert it into a minimization problem;

[0174] Step S8.5: Obtaining the optimal data collection position After that, the data collection drone Make position adjustments and start data collection.

[0175] Step S9: the communication UAV dynamically adjusts its position according to the position of the data collection UAV formation to improve the data transmission rate;

[0176] Step S9.1: Data Collection Drone Calculate the optimal data collection location Afterwards, the communication drone Upload the corresponding coordinates;

[0177] Step S9.2: Assume that the communication UAV has coordinates in the th round of tasks. At this coordinate, the data transmission rate of the data collection UAV formation to the communication UAV reaches the maximum. Combining formulas (1)-(3), the total data transmission reachable rate of the system is obtained as:

[0178] ; Formula (20);

[0179] where , , , respectively represent the maximum transmission power of the data collection UAV , the communication channel bandwidth of the communication UAV and the data collection UAV , and the Gaussian white noise power at the receiving end of the communication UAV ; represents the reference channel power gain at 1 m.

[0180] Step S9.3: The optimal position optimization model of the communication UAV is:

[0181] ; Formula (21);

[0182] ; Formula (22);

[0183] Formulas (21) and (22) represent finding the optimal position of the communication UAV such that the overall data throughput of the data collection UAV formation to the communication UAV reaches the maximum; the constraint conditions ensure the communication connection between the communication UAV and the data collection UAV ;

[0184] Step S9.4: Solve Formulas (21) and (22), and convert the problem of maximizing the overall data throughput into a minimization problem, that is: , and the upper and lower bounds of the solution range and the constraint conditions remain unchanged.

[0185] Step S10: Determine whether the data collection of all wireless sensor nodes is completed. If so, loop through Steps S7-S10 to perform the next round of tasks; if not, end the task.

[0186] Embodiment

[0187] The simulation verification of the present invention is described as follows:

[0188] The simulation of a data-driven heterogeneous UAV swarm-assisted wireless sensor data collection method includes the following steps:

[0189] Step S1: Input relevant parameters, determine the distribution area and its range of wireless sensor nodes, and construct a hierarchical data collection system for heterogeneous UAV formations. Specifically, 400 wireless sensor nodes are randomly generated within a range of 1 km × 1 km, the size of the data packets of each wireless sensor node is randomly generated, the maximum transmission powers of the wireless sensor nodes and the UAV communication modules are set to 30 dBm and 33 dBm respectively, the noise power at the receiving end is -110 dBm, the flight altitude range of the UAVs is [10, 300] / m, the communication bandwidth is 1 MHz, the energy consumption of the communication module , the energy consumption of data aggregation . The energy consumption required to amplify the signal is respectively , , the convergence parameter of the interior point method algorithm , and the number of data collection UAVs is 3;

[0190] Step S2: Determine the communication coverage range of the data collection UAVs for the wireless sensor nodes;

[0191] Step S3: According to the communication coverage range obtained in Step S2, perform the first-round regular hexagon coverage on the distribution area of the wireless sensor nodes to obtain the central coordinates of all regular hexagon areas,

[0192] which are used as the task areas of the data collection UAVs; ;

[0193] Step S4: Calculate the communication range between the communication UAVs and the data collection UAVs, and further perform the second-round regular hexagon coverage on the distribution area of the wireless sensor nodes to obtain the initial waypoint of the communication UAVs; Step S5: Use the initial waypoint of the communication UAVs as the input to optimize the initial flight path of the communication UAVs to obtain the trajectory matrix of the optimized communication UAVs;

[0194] Step S6: According to the order of the waypoints within the trajectory matrix, successively determine the central coordinate information of the task areas of the data collection UAVs included in the communication coverage area of each communication UAV ;

[0195] Step S7: According to the flight path matrix of the communication UAV , perform dynamic task allocation for the data UAV formation under the guidance of the communication UAV;

[0196] Step S8: The data collection UAV enters the corresponding task area, senses the data volume carried by the wireless sensor nodes, and adjusts the data collection position driven by the data to reduce the energy consumption of the wireless sensor nodes;

[0197] Step S9: The communication UAV makes dynamic position adjustments according to the positions of the data collection UAV formation to improve the data transmission rate;

[0198] Step S10: Determine whether the data collection of all wireless sensor nodes is completed. If so, loop through Steps S7 to S10 to perform the next round of tasks; if not, end the task.

[0199] According to the above steps, the flight trajectories of the communication UAV and the data collection UAV formation, as well as the changes in the data collection positions, are obtained as shown in the appendix Figure 5 as shown.

[0200] It can be seen from the above results that the method of the present invention can realize the task allocation of heterogeneous UAV formations and the optimization of data collection positions, which is of great significance for large-scale sensor network data collection, reducing the energy consumption of wireless sensor nodes, and extending the lifespan of wireless sensor nodes.

[0201] In the description of the present invention, it should be understood that the indicated orientation or positional relationship is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation of the present invention.

[0202] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data-driven heterogeneous drone swarm assisted wireless sensor data collection method, characterized by: The method comprises the following steps: Step S1: Determine the distribution area and range of wireless sensor nodes, and build a hierarchical data collection system for heterogeneous UAV formations, including data collection UAVs and communication drones Step S2: Identify the data collection drone Communication coverage of wireless sensor nodes; Step S3: According to the communication coverage obtained in step S2, the distribution area of ​​the wireless sensor nodes is divided into the first round of regular hexagonal coverage to form the first layer of cellular coverage structure, i.e., cellular coverage 1, and the center coordinates of all regular hexagonal areas are obtained. as a mission area for data-collecting drones; Step S4: Calculate the communication range between the communication UAV and the data collection UAV, further divide the distribution area of ​​the wireless sensor nodes into a second round of regular hexagonal coverage, establish the second layer of cellular coverage structure, i.e., cellular coverage 2, and obtain the initial track point of the communication UAV Step S5: Set the initial track point of the communication drone As input, the initial flight path of the communication UAV is optimized to obtain the optimized communication UAV track matrix Step S6: According to the track matrix The sequence of internal track points determines each communicating drone one by one The center coordinate information of the data collection UAV mission area contained in the communication coverage area; Step S7: According to the track matrix of the communication drone Carry out dynamic task allocation of data drone formation under the guidance of communication drone; Step S8: The data collection drone enters the corresponding mission area, senses the amount of data carried by the wireless sensor nodes, and performs data-driven data collection position adjustment to reduce the energy consumption of the wireless sensor nodes; Step S9: the communication UAV dynamically adjusts its position according to the position of the data collection UAV formation to improve the data transmission rate; Step S10: Determine whether data collection of all wireless sensor nodes is completed; if so, loop through steps S7 to S10 to perform the next round of tasks; if not, end the task; Step S5: Set the initial track point of the communication drone As input, the initial flight path of the communication UAV is optimized to obtain the optimized communication UAV track matrix The details are as follows: Step S5.1: Transform the matrix Flatten by columns Matrix of size Step S5.2: Communication UAV The flight path is optimized and the following optimization model is established: Among them, ξ KL Used to mark whether the line between any two initial track points is planned to the communication drone ξ KL =1 indicates the Kth initial track point and the Lth initial track point Link is planned as a communication drone flight path, otherwise ξ KL =0; Formula (11) and Formula (12) represent the matrix Sort the centers of the inner regular hexagons to get a new matrix Communication drone The new matrix The initial track point in the path is taken as the path point, and the new matrix When flying in sequence, the total flight distance is the shortest; Step S7: according to the track matrix of the communication drone Carry out dynamic task allocation of data drone formation under the guidance of communication drone; the details are as follows: Step S7.1: Introduce the concept of task rounds and initialize the task round Ω = 1; the data collection UAV formation completes the wireless sensor nodes S in the specified area. l After collecting data once, the value of Ω increases by 1, that is, the task round increases automatically; Step S7.2: Create a task library matrix X Ω ={X Ω ,T t }, used to save the data collection drone in the Ω round of missions Optional task area center coordinates, initialize the task library matrix X Ω ={} is an empty set; where the task library matrix X Ω It changes dynamically. In each round of tasks, the task library matrix X Ω The number of internal mission points needs to be greater than or equal to the number of data collection drones The number of tasks; if the task library matrix X Ω The dimension is Need to meet After the corresponding task point is selected, the task library matrix X Ω Delete in; when When , it indicates that a new task point needs to be added, that is, X Ω ={X Ω ,T t+1 }; Step S7.3: Establish the task allocation model for the first round of data collection UAV formation; that is, in the task library matrix X Ω Inside, based on data collection drones Current location For every data collection drone Find a mission point Drones for data collection The flight altitude is set to minimize the flight distance of the data collection UAV formation in the next round of missions. The model is as follows: Among them, U′ Ω = {U′ Ω,1 ,U′ Ω,2 ,...,U′ Ω,k }, δ k is the penalty variable, δ k It ensures that the task point is only selected once. When the first selection is k =1 / n u , when choosing againδ k =∞; Step S7.4: Get the data collection drone After the initial position, the data collection drone formation coordinated to reach the corresponding mission area by adjusting the flight speed; The step S8: the data collection drone enters the corresponding task area, senses the amount of data carried by the wireless sensor node, and adjusts the data collection position under data drive to reduce the energy consumption of the wireless sensor node; the details are as follows: Step S8.1: Data collection drone data perception: Get each data collection drone After the mission area information, data collection drone Arrive at designated mission area center U′ Ω,k ; Broadcast its own location information, wireless sensor node S l After receiving the broadcast, its location information, remaining energy And the amount of data to be sent l Packed and sent to data collection drone Data Collection Drones The number of wireless sensor nodes in the area is recorded as N Ωk ; Step S8.2: When the data collection drone and wireless sensor nodes S l The distance between them is d lk When the communication energy consumption is E lk The calculation is as follows: Among them, E tx ,E rx Respectively represent the energy consumption of data sending and data receiving; I l Represents the wireless sensor node S l The amount of data E e Indicates the working energy consumption of the communication module, E DA Energy consumption for data aggregation; E f ,E a They represent the energy consumption required to amplify the signal, which depends on the transmission and reception distance and the bit error rate; Step S8.3: For wireless sensor node S l The remaining energy and communication energy consumption are normalized to obtain the normalized remaining energy And normalized communication energy consumption Values: in, max(E) and min(E) represent N Ωk A wireless sensor network node S l The highest remaining energy value, the lowest remaining energy value, the highest communication energy consumption, and the lowest communication energy consumption; Step S8.4: Establish the data collection drone in the Ωth round mission In the communication coverage area, the number of completed Ωk When collecting data from wireless sensor nodes, the total cost for: Data collection drones The optimization adjustment of the data collection location is summarized as follows: l The amount of data carried and its distribution to find an optimal data collection location U″ Ω,k , so that the sum of the residual energy values ​​of all wireless sensor nodes in the area is maximized. The objective function and constraints are as follows: Among them, formula (19) ensures that the data collection drone is After the position adjustment, it can still ensure that all wireless sensor nodes S in the area l Coverage; Formula (18) and Formula (19) are linear programming problems, which can be Convert it into a minimization problem; Step S8.5: Obtain the optimal data collection position U″ Ω,k After that, the data collection drone Make position adjustments and start data collection; Step S9: the communication drone dynamically adjusts its position according to the position of the data collection drone formation to increase the data transmission rate; Step S9.1: Data Collection Drone Calculate the optimal data collection position U″ Ω,k Afterwards, the communication drone Upload the corresponding coordinates; Step S9.2: Set up a communication drone In the Ωth round of tasks, the coordinates are At this coordinate, the data collection drones are deployed to the communication drones The data transmission rate reaches the maximum; further combined with Shannon's theorem, the total data transmission reachability rate of the system can be obtained for: in, is the channel power gain; P k , Represents data collection drones Maximum transmit power, communication drone and data collection drones Communication channel bandwidth and communication drones Gaussian white noise power at the receiving end; β u represents the reference channel power gain at 1m; Step S9.3: Communicate with the drone The optimal location optimization model is: Formula (21) and Formula (22) represent the search for communication drones. The best location Enables data collection drones to form fleets of communications drones The overall data throughput is maximized; the constraints ensure that the communication drone and data collection drones Communication connection; Step S9.4: Solve formula (21) and formula (22) to convert the maximized overall data throughput problem into a minimization problem, namely: The upper and lower bounds of the solution range and the constraints do not change.

2. According to claim 1, a data-driven heterogeneous drone swarm assisted wireless sensor data collection method is characterized by: Step S1: Determine the distribution area and range of wireless sensor nodes, and construct a hierarchical data collection system for heterogeneous UAV formations, including data collection UAVs and communication drones The details are as follows: Step S1.1: Construct a heterogeneous UAV data collection and transmission network; the heterogeneous UAV data collection and transmission network includes two types of UAVs and wireless sensor nodes: the two types of UAVs are n u Data collection drone And 1 communication drone The data collection drone is numbered The number of wireless sensor nodes is n s , numbered S l ,l∈{1,2,...,n s }; Step S1.2: The heterogeneous UAV data collection and transmission network is divided into three layers; the first layer is the wireless sensor node layer, which is responsible for data collection; the second layer is the data collection UAV layer, which is responsible for the collection of sensor data in the specified mission area; the third layer is the communication UAV layer, which is responsible for the UAV formation mission planning and acts as a relay, using the airborne radio to transmit the data collected by the data collection UAV to the cloud or base station by amplifying and forwarding.

3. The data-driven heterogeneous drone swarm-assisted wireless sensor data collection method according to claim 2 is characterized by: Step S2: Determine the data collection drone The communication coverage of wireless sensor nodes is as follows: Step S2.1: Calculate the time t for the wireless sensor node S l With data collection drones The distance d lk (t), expressed as: in, Represents a data collection drone Height, For wireless sensor node S l With data collection drones The coordinates of in two-dimensional space; Step S2.2: Calculate data collection drone With wireless sensor node S l The channel power gain between It is expressed as: Among them, β k represents the channel power gain at 1m; Step S2.3: Based on the channel power gain Find the wireless sensor node S l Uplink transmission rate It is expressed as: in, is the channel bandwidth, P l For wireless sensor node S l The transmission power, Represents a data collection drone Gaussian white noise power at the receiving end; Step S2.4: During the data collection process, the wireless sensor node S l Uplink transmission rate Must be greater than or equal to the minimum data transfer rate Satisfaction Step S2.5: According to formula (3), the minimum data transmission rate The constraint is transformed into a distance constraint, expressed as: in, To meet the uplink transmission rate The wireless sensor node S under the constraint l With data collection drones The maximum distance between Step S2.6: Data Collection Drone Using a half beam width of θ H o Directional antennas are used to communicate with wireless sensor nodes S l For communication, the directional antenna radiates vertically toward the ground; Step S2.7: Calculate data collection drone Communication coverage radius and maximum flight altitude It is expressed as:

4. The data-driven heterogeneous drone swarm assisted wireless sensor data collection method according to claim 3 is characterized by: Step S3: According to the communication coverage obtained in step S2, the distribution area of ​​the wireless sensor nodes is divided into the first round of regular hexagonal coverage to form the first layer of cellular coverage structure, i.e., cellular coverage 1, and the center coordinates of all regular hexagonal areas are obtained. The mission area for the data collection drone is as follows: Step S3.1: Wireless sensor node S l The distribution area size is x km×y km. A Cartesian coordinate system is established with the origin at O(0,0). Communication coverage radius Calculate the coordinates of the center of each regular hexagon: Step S3.2: Create a matrix Used to save the center coordinates of each regular hexagon but: in, They represent the wireless sensor nodes S l The distribution area is divided into A regular hexagonal area, Represent the row index and column index of each hexagonal area respectively; Step S3.3: According to the center coordinates of the regular hexagon Data collection drones Communication coverage radius The length of the regular hexagon is calculated, and the vertex coordinates of each regular hexagon are calculated to complete the regular hexagon coverage of the mission area. Each regular hexagon area is a data collection drone. mission area.

5. The data-driven heterogeneous drone swarm assisted wireless sensor data collection method according to claim 4 is characterized by: Step S4: Calculate the communication range between the communication UAV and the data collection UAV, further divide the distribution area of ​​the wireless sensor nodes into a second round of regular hexagonal coverage, establish a second layer of cellular coverage structure, i.e., cellular coverage 2, and obtain the initial track point of the communication UAV The details are as follows: Step S4.1: Input data collection drone The maximum transmission power P k , communication drones Gaussian white noise power at the receiving end Referring to step S2, formula (1) to formula (5), the minimum data transmission rate is calculated to obtain Based on the communication drone With data collection drones Maximum distance When communication drones and data collection drones The distance is greater than When the communication connection is lost; Step S4.2: Communicate with the drone and data collection drones Independent frequency bands are used between them, and independent omnidirectional antennas are equipped for communication. Coverage radius With communication drone and data collection drones The maximum height difference between the two is related to the communication between the UAVs. and all data collection drones When the flight altitude is consistent, the communication drone Coverage reaches the maximum, that is, Step S4.3: Referring to step S3, according to the communication drone Coverage radius For wireless sensor nodes S l The distribution area of ​​​​the second round of regular hexagonal coverage is obtained, and the number is A regular hexagonal area, corresponding to the regular hexagon center coordinates Step S4.4: Create a matrix Used to save the center coordinates of each regular hexagon obtained from the second round of regular hexagon coverage but: Step S4.5: Transform the matrix The center coordinates of each regular hexagon in As a communication drone The initial track point.

6. The data-driven heterogeneous drone swarm assisted wireless sensor data collection method according to claim 5, characterized in that: Step S6: according to the track matrix The sequence of internal track points determines each communicating drone one by one The center coordinate information of the data collection drone mission area contained in the communication coverage area is as follows: Step S6.1: The data transmission power of the UAV is usually several times or even dozens of times that of the wireless sensor node, so the communication UAV Coverage radius Bigger than a data-collecting drone Communication coverage radius but Therefore, in communication drones The corresponding coordinates of the center of the regular hexagon A regular hexagon centered It includes multiple data collection drones Coverage area divided by regular hexagon center coordinates A regular hexagon centered Step S6.2: Follow the communication drone The flight sequence of the flight path points, i.e. the track matrix Identify communication drones In the tth task area Data collection drones included Coverage area divided by regular hexagon center coordinates A regular hexagon centered And create the matrix storage The corresponding regular hexagon center coordinates

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