An Optimization Method for Communication Links of Unmanned Aerial Vehicle Combined with Unmanned Ground Vehicle Assisted Wireless Sensor Networks

Abstract

The present invention relates to the technical field of the combined application of unmanned aerial vehicles and unmanned vehicles, and specifically to a method for optimizing the communication link of an unmanned aerial vehicle combined with an unmanned vehicle to assist a wireless sensor network. According to the random distribution characteristics of wireless sensor network nodes and unmanned vehicles, an expected model of the energy consumption of the data transmission link is deduced, and a link optimization model based on the expected distance and virtual relay node assistance is constructed. By analyzing and reconstructing the link optimization model, and using the convex optimization method to optimize the link, an initial link is further constructed in a competition-based manner. Finally, an optimal data transmission link optimization method is designed to achieve the optimization of the data transmission link with the optimal energy consumption; in the present invention, the computational load in the link construction and optimization process is divided into two parts and loaded onto the unmanned aerial vehicle side and the source node side respectively, which can significantly reduce the computational cost and improve the operation efficiency of the data transmission system on the basis of ensuring the optimal energy consumption of the data transmission link.

Description

Technical Field

[0001] The present invention relates to the technical field of the combined application of unmanned aerial vehicles and unmanned vehicles, and specifically to a method for optimizing the communication link of an unmanned aerial vehicle combined with an unmanned vehicle to assist a wireless sensor network. Background Art

[0002] With the rapid development of the Internet of Things and smart city technologies, wireless sensors have been widely used in fields such as environmental monitoring, equipment monitoring, disaster warning, and agricultural monitoring. However, with the expansion of application scenarios and the increase in network scale, traditional wireless node data transmission methods face problems such as limited coverage, high energy consumption, and lack of flexibility. Especially in some complex environments, wireless nodes are severely interfered and cannot directly transmit relevant information to the base station.

[0003] In the context of the high speeds of unmanned aerial vehicles and driverless vehicles, by utilizing the flexibility of unmanned aerial vehicle flight and the mobility of unmanned vehicles, they can be used as relay nodes for wireless sensor node data transmission to assist wireless sensor nodes in data transmission, thereby increasing the coverage of the wireless monitoring network and improving the reliability of data transmission.

[0004] Existing research mainly focuses on aspects such as unmanned aerial vehicle trajectory optimization, communication coverage optimization, communication scheduling optimization, and data transmission link optimization. The main optimization goals include: improving data transmission rate, reducing communication latency, reducing wireless node transmission energy consumption and unmanned aerial vehicle / driverless vehicle energy consumption, etc. Although existing research has made important progress in unmanned aerial vehicle-assisted wireless sensor network data transmission, most research mainly focuses on single-hop data transmission link optimization and fails to fully consider the multi-hop data transmission link optimization problem under the assistance of unmanned aerial vehicles and unmanned vehicles.

[0005] With the continuous expansion of network scale, the optimization of multi-hop data transmission links becomes particularly important for reducing the energy consumption of wireless sensor nodes. In addition, in the combined assistance of unmanned aerial vehicles and unmanned vehicles for wireless node data transmission, since the positions of unmanned aerial vehicles and unmanned vehicles are constantly changing, traditional link planning algorithms based on static position information can no longer meet the actual needs. And in the above scenarios, wireless sensor nodes are not homogeneous and the data transmission cycles are inconsistent, so the clustering-based strategy is not applicable. Therefore, how to effectively plan efficient multi-hop data transmission links in a dynamically changing network environment has become an urgent problem to be solved. Summary of the Invention

[0006] In order to solve the problem of how to effectively plan efficient multi-hop data transmission links in a dynamically changing network environment, the present invention provides a method for optimizing the communication link of an unmanned aerial vehicle combined with an unmanned vehicle to assist a wireless sensor network.

[0007] The present invention aims at optimizing the energy consumption of data transmission of wireless sensor network nodes assisted by unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), and provides a method for dynamically optimizing the data transmission link, which can quickly plan the data transmission link according to the data transmission requirements of wireless sensor nodes and the position of the UAV.

[0008] The present invention is implemented by the following technical solutions:

[0009] A method for optimizing the communication link of a wireless sensor network assisted by UAVs and UGVs, the method comprising the following steps:

[0010] Step S1: Construct a data transmission system of a wireless sensor network assisted by UAVs and UGVs, including one UAV, UGVs and wireless sensor nodes; in the Cartesian coordinate system, the position of the wireless sensor node , is expressed as , at time t, the position of the UGV , is expressed as , the position of the UAV is expressed as , and the flight altitude of the UAV is ; then:

[0011] The distance between any two wireless sensor nodes and , is , and ;

[0012] The distance between the wireless sensor node and the UAV is ;

[0013] The distance between the wireless sensor node and the UGV is ;

[0014] The distance between the UGV and the UAV is .

[0015] The number of UAVs can be expanded according to the mission area, and the number of UGVs is not fixed and can be added at any time to participate in the data transmission network.

[0016] In the above UAV-unmanned vehicle assisted wireless sensor network data transmission system, the UAV is mainly responsible for forwarding the data of wireless sensor nodes to the base station, and the UAV circles uniformly within the range where the sensor nodes are distributed according to a pre-set trajectory. The unmanned vehicle can, while performing other tasks (such as logistics transportation, etc.), act as a mobile relay node to forward the data of wireless sensor nodes. The sensor nodes are randomly distributed in the corresponding task area, and the sensor nodes can act as relays for each other to forward data.

[0017] Step S2: When a wireless sensor node has a data transmission requirement, the wireless sensor node directly sends a data transmission request message to the UAV, specifically including the location information of the wireless sensor node and the size of the data packet to be sent ;

[0018] After receiving the data transmission request message, the UAV estimates the algorithm operation delay , flight speed and data transmission delay based on the current position of the UAV, and further calculates the position of the UAV itself when the data packet arrives .

[0019] Step S3: The UAV plans the data transmission link;

[0020] Step S3.1: Let , at time, the flight altitude of the UAV is , then the projection position of the UAV on the ground is , then the distance between the wireless sensor node and the projection position of the UAV on the ground is ;

[0021] Step S3.2: Let the total number of hops of the data transmission link be hops. Assume that on the straight line from the wireless sensor node to the projection position , there is a set of virtual relay nodes , forming an ideal virtual relay link. The coordinates corresponding to the virtual relay nodes are , , , where

[0022] Step S3.3: Let the distances of each hop from the 1st hop to the th hop be respectively ; The distance of the th jump is:

[0023] ; Formula (1);

[0024] Step S3.4: Then, the distance from the wireless sensor node to the projection position of the UAV on the ground can be decomposed into:

[0025] ; Formula (2);

[0026] Step S4: Consider a multi-hop information transmission link from a sensor node to the UAV, and establish a matrix to record the information of each hop (for example, the source node of the first hop is , and the destination node is , then the elements of the first row of the matrix are i and j), represents the total number of hops, and the communication energy consumption for transmitting 1 bit of data from the source node to the destination node is:

[0027] ; Formula (3);

[0028] ; Formula (4);

[0029] ; Formula (5);

[0030] ; Formula (6);

[0031] Among them, respectively represent the data transmission energy consumption and the reception energy consumption; I represents the data volume; represents the working energy consumption of the communication module, is the data aggregation energy consumption; represents the distance from the source node to the destination node , ; respectively represent the energy consumption required to amplify the signal, which depends on the transceiver distance and the bit error rate.

[0032] Step S5: Construct a link energy consumption cost calculation model based on the expected distance;

[0033] Step S5.1: Due to the randomness of the distribution of wireless sensor nodes and the movement of the unmanned vehicle, among the wireless sensor nodes to the projection position of the UAV on the ground The probability that a group of relay nodes simultaneously appear on a straight line is very small. According to the number of wireless sensor nodes and the distribution area range , the average area of the region where each wireless sensor node is located can be calculated as ; assuming that the region is circular, the corresponding radius can be calculated as ; ;

[0034] Step S5.2: Draw a circle with each virtual relay node as the center and as the radius; according to the law of large numbers, when the sample size is large enough and with the addition of the unmanned vehicle as a relay node, within each circle, there must be an actual wireless sensor node or an unmanned vehicle that can be used as an actual relay node, and the position of the actual relay node within the circle follows a random distribution.

[0035] Step S6: Calculate the expectation of the distance corresponding to each hop;

[0036] Step S6.1: For the first hop, find the expectation of the distance, the expectation of the square of the distance, and the expectation of the fourth power of the distance from the wireless sensor node to any point within the circle with the first virtual relay node as the center and as the radius, which are respectively expressed as:

[0037] ; Formula (7);

[0038] ; Formula (8);

[0039] ; Formula (9);

[0040] where represents the distance from the wireless sensor node to the virtual relay node ;

[0041] Step S6.2: For the th hop, , find the expectation of the distance, the expectation of the square of the distance, and the expectation of the fourth power of the distance from any point within the circle with the virtual relay node as the center and as the radius to any point within the circle with the virtual relay node as the center and as the radius, which are respectively expressed as:

[0042] ; Formula (10);

[0043] ; Formula (11);

[0044] ; Formula (12);

[0045] Step S6.3: For the th jump, find the expectations of the distance, the square of the distance, and the fourth power of the distance from any point inside the circle with as the center and as the radius to the UAV, which are respectively expressed as:

[0046] ; Formula (13);

[0047] ; Formula (14);

[0048] ; Formula (15);

[0049] Step S7: Build an optimization model for the data transmission link;

[0050] Step S7.1: , let , and obtain the total energy consumption of the data transmission link as:

[0051] ; Formula (16);

[0052] Among them, is the energy consumption for transmitting 1 bit of data from the source node to the destination node; is the energy consumption at the data receiving end; represents the operating energy consumption of the communication module, is the data aggregation energy consumption;

[0053] The corresponding data transmission energy consumption is:

[0054] ; Formula (17);

[0055] Among them, , both represent the energy consumption required to amplify the signal at the transmitter, which depends on the transceiver distance and the bit error rate;

[0056] Step S7.2: If the wireless sensor node transmits data directly to the UAV, , the corresponding communication energy consumption is:

[0057] ; Formula (18);

[0058] Step S7.3: Establish an optimization model for the data transmission link;

[0059] Let , and establish the initial link optimization model as:

[0060] ; Equation (19);

[0061] ; Equation (20);

[0062] Equations (15) and (16) indicate that the multi-hop or direct transmission mode is determined, and the optimal number of hops is calculated , and the distance corresponding to each hop between virtual relay nodes is determined , so that the transmission energy consumption of the overall link is minimized.

[0063] Step S8: Calculate the optimal number and optimal position of virtual relay nodes;

[0064] Step S8.1: Transform the integer constraint , introduce a very small constant , and let , then transform the integer constraint into an inequality constraint;

[0065] Step S8.2: For , after obtaining the solution of the objective function , by comparing the value of with , if , then directly determine that the wireless sensor node transmits data directly to the UAV;

[0066] Step S8.3: Reconstruct the data transmission link optimization model as:

[0067] ; Equation (21);

[0068] ; Equation (22);

[0069] Step S8.4: Use the convex optimization algorithm (such as the interior point method) to solve Equations (21) and (22) to obtain the number of virtual relay nodes and the distance between virtual relay nodes ;

[0070] Step S8.5: According to the position of the wireless sensor node and the distance between virtual relays , calculate the position of the virtual relay node ;

[0071] ; Equation (23);

[0072] Among them, , , , , , .

[0073] Step S9: The drone broadcasts the location information of all virtual relay nodes . All the wireless sensor nodes within the area corresponding to each virtual relay node calculate the distance to the virtual relay node. At the same time, all the unmanned vehicles calculate the distance to the virtual relay node, and in a competitive manner, select the wireless sensor node or unmanned vehicle closest to the virtual relay node to join the data transmission link, and finally form an initial data transmission link from the wireless sensor node to the drone ; The wireless sensor node or unmanned vehicle closest to the virtual relay node joins the data transmission link as the actual relay node.

[0074] Step S10: The wireless sensor node According to the information in the initial data transmission link of the drone , calculates the distance matrix between all actual relay nodes, and substitutes it into the communication energy consumption model, that is, formula (3), to obtain the energy consumption matrix .

[0075] Step S11: Optimize the initial data transmission link of the drone ;

[0076] Reduce to a typical pathfinding problem, set the starting point as the wireless sensor node , and the end point as the position of the drone, run the Dijkstra algorithm, and obtain a data transmission link with the lowest overall energy consumption from the energy consumption matrix ;

[0077] Step S12: The wireless sensor node Reports the information of the current data transmission link to the drone;

[0078] Step S13: The drone sends a transmission instruction to start data transmission.

[0079] The present invention has the following beneficial effects:

[0080] 1. The present invention proposes an optimization method for the communication link of an unmanned aerial vehicle (UAV) - combined with unmanned vehicle - assisted wireless sensor network. According to this method, the UAV does not need to obtain and update the position information of all sensor nodes and unmanned vehicles, but only needs to obtain the position of the source sensor nodes with data transmission requirements, avoiding a large amount of signaling interaction and channel congestion caused by the need for the UAV to frequently update the position information of unmanned vehicles and sensor nodes when multiple wireless sensor nodes initiate data transmission requests to the UAV.

[0081] 2. The present invention proposes an optimization method for the communication link of an unmanned aerial vehicle (UAV) - combined with unmanned vehicle - assisted wireless sensor network. In the prior art, the UAV mostly calculates the optimal data transmission link for each node. When the number of wireless nodes increases, all the position information of wireless nodes needs to be traversed during the calculation process, which consumes a large amount of computing resources of the UAV. The present invention divides the computing load into two parts and loads them onto the UAV and wireless sensor nodes respectively, thereby improving the operation efficiency of the system.

[0082] 3. The present invention proposes an optimization method for the communication link of an unmanned aerial vehicle (UAV) - combined with unmanned vehicle - assisted wireless sensor network. At the UAV end, only the distance to the corresponding source sensor nodes needs to be calculated according to their positions. Then, using the corresponding convex optimization algorithm to solve the reconstructed initial link optimization model, the position information of the virtual relay nodes can be calculated within a limited time complexity, and further the initial link information can be obtained, ensuring the application requirements of large - scale networks.

[0083] 4. The present invention proposes an optimization method for the communication link of an unmanned aerial vehicle (UAV) - combined with unmanned vehicle - assisted wireless sensor network. At the source sensor node end, only an energy consumption cost matrix needs to be generated according to the initial link information, and the initial link optimization problem can be further optimized by analogy with a path - finding problem. The above steps can be completed by an embedded mobile platform within a limited time complexity. Description of the Drawings

[0084] Figure 1 is a schematic diagram of the data transmission system of the unmanned aerial vehicle (UAV) - combined with unmanned vehicle - assisted wireless sensor network in the present invention.

[0085] Figure 2 is the implementation flowchart of the present invention.

[0086] Figure 3 is a schematic diagram of constructing the initial data transmission link based on virtual relay nodes in the present invention.

[0087] Figure 4 is the drawing for the derivation and proof of the calculation model based on the expected distance in the present invention.

[0088] Figure 5It is the optimal data transmission link formed by the UAV trajectory and the method of UAV - unmanned vehicle assisted wireless sensor nodes under the change of the number of participating relays in an embodiment of the present invention. Detailed implementation manners

[0089] The present invention designs a method for dynamically constructing and optimizing energy consumption of multi - hop transmission links of wireless sensor network nodes under the joint assistance of UAVs and unmanned vehicles in a wireless sensor network node data transmission architecture. First, a method for information interaction between the source wireless sensor node and the UAV is determined. Secondly, aiming at the problems of high computational complexity and large amount of calculation in the optimization of the data transmission link between the source wireless sensor and the UAV, a virtual relay node - assisted link optimization model based on the expected distance is constructed. Then, by analyzing and reconstructing the link optimization model and using convex optimization methods to optimize the link, the optimal number of hops of the initial data transmission link and the positions of the virtual nodes are determined, and the initial link is constructed in a competitive manner. Finally, an optimal energy - consumption - based data transmission link optimization method is designed.

[0090] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings:

[0091] A method for optimizing the communication link of a wireless sensor network assisted by a UAV and an unmanned vehicle jointly, the method comprising the following steps:

[0092] Step S1: Construct a data transmission system for a wireless sensor network assisted by a UAV and an unmanned vehicle jointly, including one UAV, unmanned vehicles, and wireless sensor nodes; in the Cartesian coordinate system, the position of the wireless sensor node , is represented as , at time t, the position of the unmanned vehicle , is represented as , the position of the UAV is represented as , and the flight altitude of the UAV is ; then:

[0093] The distance between any two wireless sensor nodes and , is , and ;

[0094] The distance between the wireless sensor node and the UAV is ;

[0095] The distance between the wireless sensor node and the unmanned vehicle is ;

[0096] Driverless vehicle and the distance between the driverless vehicle and the drone is .

[0097] The number of drones can be expanded according to the mission area, and the number of driverless vehicles is not fixed and can be added at any time to participate in the data transmission network.

[0098] In the above-mentioned data transmission system for assisting a wireless sensor network by drones and driverless vehicles, the drones are mainly responsible for forwarding the data of the wireless sensor nodes to the base station, and the drones fly uniformly in circles within the range where the sensor nodes are distributed according to a pre-set trajectory. While performing other tasks (such as logistics transportation, etc.), the driverless vehicle can forward the data of the wireless sensor nodes as a mobile relay node. The sensor nodes are randomly distributed in the corresponding mission area, and the sensor nodes can act as relays for each other to forward data.

[0099] Step S2: When the wireless sensor node has a data transmission requirement, the wireless sensor node directly sends a data transmission application message to the drone, specifically including the position information of the wireless sensor node and the size of the data packet to be sent ;

[0100] After receiving the data transmission application message, the drone estimates the algorithm operation delay and the data transmission delay according to the current position of the drone, the flight speed , and the channel state information, and then further calculates the position of the drone itself when the data packet arrives .

[0101] Step S3: The drone plans the data transmission link;

[0102] Step S3.1: Let , at time, the flight altitude of the drone is , then, the projection position of the drone on the ground is , then the distance between the wireless sensor node and the projection position of the drone on the ground is ;

[0103] Step S3.2: Let the total number of hops of the data transmission link be hops, assuming from the wireless sensor node to the projection position On a straight line, there exists a set of virtual relay nodes , forming an ideal virtual relay link. The coordinates corresponding to the virtual relay nodes are , , where is the number of virtual relay nodes;

[0104] Step S3.3: Assume that in the hops from the 1st hop to the -th hop, the distances of each hop are respectively ; The distance of the -th hop is:

[0105] ; Formula (1);

[0106] Step S3.4: Then, the distance from the wireless sensor node to the projection position of the UAV on the ground can be decomposed into:

[0107] ; Formula (2);

[0108] Step S4: Consider a multi-hop information transmission link from a sensor node to the UAV, and establish a matrix to record the information of each hop (for example, if the source node of the first hop is and the destination node is , then the elements of the first row of the matrix are i and j), represents the total number of hops, and the communication energy consumption for transmitting 1 bit of data from the source node to the destination node is:

[0109] ; Formula (3);

[0110] ; Formula (4);

[0111] ; Formula (5);

[0112] ; Formula (6);

[0113] where respectively represent the energy consumption for data transmission and reception; I represents the data volume; represents the operating energy consumption of the communication module, is the data aggregation energy consumption; represents the distance from the source node to the destination node , ; respectively represent the energy consumption required to amplify the signal, which depends on the transceiver distance and the bit error rate.

[0114] Step S5: Construct a link energy consumption cost calculation model based on the expected distance;

[0115] Step S5.1: Due to the randomness of the distribution of wireless sensor nodes and the movement of the unmanned vehicle, the probability that a group of relay nodes appear simultaneously on the straight line from the wireless sensor node to the projection position of the UAV on the ground is very small. According to the number of the wireless sensor nodes and the distribution area range , the average area of the region where each wireless sensor node is located can be calculated as ; assuming that the region is circular, the corresponding radius can be calculated as ;

[0116] Step S5.2: Draw a circle with each virtual relay node as the center and as the radius; According to the law of large numbers, when the sample size is large enough, and with the addition of the unmanned vehicle as a relay node, within each circle, there must be an actual wireless sensor node or the unmanned vehicle, which can be used as an actual relay node, and the position of the actual relay node within the circle follows a random distribution. The above process is shown in Appendix Figure 3 .

[0117] Step S6: Calculate the expectation of the distance corresponding to each hop;

[0118] Step S6.1: For the first hop, find the expectation of the distance, the expectation of the square of the distance, and the expectation of the fourth power of the distance from the wireless sensor node to any point within the circle with the first virtual relay node as the center and as the radius, which are respectively expressed as:

[0119] ; Formula (7);

[0120] ; Formula (8);

[0121] ; Formula (9);

[0122] where represents the distance from the wireless sensor node to the virtual relay node ;

[0123] Step S6.2: For the th hop, , find the virtual relay node is the center of the circle, Any point in the circle with radius 1 to the virtual relay node is the center of the circle, The expectation of the distance, the expectation of the square of the distance, and the expectation of the fourth power of the distance to any point in the circle with radius are expressed as:

[0124] ;Formula (10);

[0125] ;Formula (11);

[0126] ;Formula (12);

[0127] Step S6.3: Jump, seek is the center of the circle, The expected distance from any point in the circle with a radius of to the UAV, the expected square of the distance, and the expected fourth power of the distance are expressed as:

[0128] ;Formula (13);

[0129] ;Formula (14);

[0130] ;Formula (15);

[0131] The derivation and proof process of formula (7) to formula (15) are as follows:

[0132] for The calculation is derived as follows:

[0133] Establish a polar coordinate system, let ,in , , is the probability density function, and the corresponding expectation is:

[0134] ;Formula (1-1);

[0135] for The calculation derivation is to convert the integrand of formula (1-1) into Change to That's it.

[0136] Similarly, for and Reference for calculation derivation and The calculation derivation method of .

[0137] for The calculation is derived as follows:

[0138] Establish a polar coordinate system, let , , , ,but:

[0139] ;Formula (1-2);

[0140] Likewise, for To derive the calculation of Replace with That's it.

[0141] for , The calculation of the integrated term Change to , but for Integrals usually do not have analytical solutions. For this reason, we first assume that and The positions of coincide, and the expected distance from any point in the circle to the center of the circle is , then solve Equivalent to asking To is the center of the circle, The expected distance of any point on the arc of radius is shown in the following figure. Figure 4 As shown in the first part.

[0142] ;Formula (1-3);

[0143] In formula (1-3), due to The value of is much smaller than the previous two values, so we choose to omit it. , to obtain The subsequent numerical simulation results verify that the above derivation has a good similarity.

[0144] same, The calculation of can refer to the above derivation.

[0145] for The calculation of can be divided into two steps. In the first step, assume that in a radius of Take any two points in the circle and let the expected distance between the two points be .make , ,in, , ,have:

[0146] ; Formula (1-4);

[0147] It can be seen that solving the analytical solution of Formula (1-4) is very complicated. In this regard, we can simplify the solution of the model by constructing the Crofton differential equation and the Jacobian determinant. First, assume is a function of the circle radius . When the radius of the circle is 1, . According to the properties of derivatives:

[0148] ; Formula (1-5);

[0149] As shown in the second part of the appendix, a circle with a radius of R is divided into two parts. Arbitrarily take two points, and there are three cases for the distribution of the two points: both points are in the dark area, one point is in the dark area, and neither point is in the dark area. According to different cases, we use different functions and weight factors to represent the corresponding expected distance and the corresponding weight value. Figure 4

[0150] According to the law of total expectation, there is:

[0151] ; Formula (1-6);

[0152] According to the total probability formula, , where:

[0153] ; Formula (1-7);

[0154] ; Formula (1-8);

[0155] Among them, the omitted terms are high-order terms. In addition are all high-order terms, so we can set .

[0156] It is obtained that . Substituting it into Formula (1-6), there is:

[0157] ; Formula (1-9);

[0158] Moving the terms and letting , the Crofton differential equation is constructed as follows:

[0159] ; Formula (1-10);

[0160] ; Formula (1-11);

[0161] In the polar coordinate system, for​ Solve. When , assume that point p1 is on the outer arc of the circle. is the Jacobian determinant. Establish a polar coordinate system with point p1 as the origin. As shown in Figure 4 the third part, the upper and lower limits of the integral can be obtained by geometric methods, and there are:

[0162] ; formula (1-12);

[0163] Since , further substitute and obtain , that is , which is equivalent to .

[0164] The second step, as shown in Appendix Figure 4 the third part, in a circle, randomly take two points a and b and connect them. Rotate it to make it perpendicular to the horizontal direction, and horizontally move point b by length to get . There is . According to the properties of expectation, if the above process is used as a sample, when the number of samplings tends to infinity, there is .

[0165] So far, the derivation and proof of formula (7) - formula (15) are completed.

[0166] Step S7: Build an optimization model for the data transmission link;

[0167] Step S7.1: , let , and obtain the total energy consumption of the data transmission link as:

[0168] ; formula (16);

[0169] Among them, is the energy consumption for transmitting 1 bit of data from the source node to the destination node; is the energy consumption at the data receiving end; represents the operating energy consumption of the communication module, is the data aggregation energy consumption;

[0170] The corresponding data transmission energy consumption is:

[0171] ; formula (17);

[0172] Among them, , both represent the energy consumption required to amplify the signal at the transmitter end, depending on the transceiver distance and the bit error rate;

[0173] Step S7.2: If the wireless sensor node transmits data directly to the UAV, , the corresponding communication energy consumption is:

[0174] ; Equation (18);

[0175] Step S7.3: Establish an optimization model for the data transmission link;

[0176] Let , and establish the initial link optimization model as:

[0177] ; Equation (19);

[0178] ; Equation (20);

[0179] Equations (15) and (16) indicate that the multi-hop or direct transmission mode is determined, the optimal number of hops is calculated , and the distance corresponding to each hop between virtual relay nodes is determined to minimize the transmission energy consumption of the overall link.

[0180] Step S8: Calculate the optimal number and optimal location of virtual relay nodes;

[0181] Step S8.1: Transform the integer constraint , introduce a very small constant , and let to convert the integer constraint into an inequality constraint;

[0182] Step S8.2: For , after obtaining the solution of the objective function , by comparing the value of with , if , it is directly determined that the wireless sensor node transmits data directly to the UAV;

[0183] Step S8.3: Reconstruct the data transmission link optimization model as:

[0184] ; Equation (21);

[0185] ; Equation (22);

[0186] Step S8.4: Use a convex optimization algorithm (such as the interior point method) to solve Equations (21) and (22) to obtain the number of virtual relay nodes and the distance between virtual relay nodes ;

[0187] Step S8.5: According to the position of the wireless sensor node and the distance between virtual relays , calculate the position of the virtual relay node ;

[0188] ; Formula (23);

[0189] Among them, , , , , , .

[0190] Step S9: The UAV broadcasts the position information of all virtual relay nodes . All wireless sensor nodes within the area corresponding to each virtual relay node calculate the distance to the virtual relay node, and at the same time all unmanned vehicles calculate the distance to the virtual relay node. Then, in a competitive manner, select the wireless sensor node or unmanned vehicle closest to the virtual relay node to join the data transmission link, and finally form an initial data transmission link from the wireless sensor node to the UAV ; The wireless sensor node or unmanned vehicle closest to the virtual relay node joins the data transmission link as the actual relay node

[0191] Step S10: The wireless sensor node calculates the distance matrix between all actual relay nodes according to the information in the initial data transmission link of the UAV , and substitutes it into the communication energy consumption model, that is, Formula (3), to obtain the energy consumption matrix .

[0192] Step S11: Optimize the initial data transmission link of the UAV ;

[0193] Reduce it to a typical pathfinding problem, set the starting point as the wireless sensor node , the end point as the position of the UAV, run the Dijkstra algorithm, and obtain a data transmission link with the lowest overall energy consumption from the energy consumption matrix ; ;

[0194] Step S12: The wireless sensor node reports the information of the current data transmission link to the UAV;

[0195] Step S13: The UAV sends a transmission instruction to start data transmission

[0196] The simulation verification of the present invention will be described below;

[0197] The simulation of an optimization method for the communication link of an unmanned aerial vehicle (UAV) combined with an unmanned vehicle to assist a wireless sensor network includes the following steps:

[0198] Step 1: Set the working energy consumption of the communication module , and the data aggregation energy consumption . The energy consumptions required to amplify the signals are respectively , , the size of the data packet is 100 kbit, and the algorithm convergence parameter of the convex optimization algorithm (interior point method) is .

[0199] Step 2: Randomly generate the trajectories of the UAVs and the positions of the wireless sensors within a range of 3 km × 3 km, and randomly generate the positions of the unmanned vehicles (since both the wireless sensor nodes and the unmanned vehicles are responsible for data forwarding in the present invention, their positions are represented by black solid dots in the appendix Figure 5 ).

[0200] Step 3: Randomly generate the source sensor nodes. According to the process shown in the appendix Figure 2 , initiate a data transmission request.

[0201] Step 4: The UAV performs position prediction, calculates the distance to the source sensor node and the corresponding projection distance. And use the corresponding parameters as the algorithm input to obtain the position and quantity of the virtual relay nodes.

[0202] Step 5: The UAV broadcasts the positions of the virtual relay nodes within the initial link.

[0203] Step 6: The remaining nodes compete, and the node closest to the virtual relay node becomes the actual relay node, forming an initial data transmission link.

[0204] Step 7: After obtaining the initial data transmission link information, the source sensor node calculates the distance matrix between the actual relay nodes in the initial data transmission link, further calculates the energy consumption cost matrix, and uses the Dijkstra algorithm to obtain the data transmission link with the lowest energy consumption, and further uploads the initial data transmission link information to the UAV.

[0205] Step 8: The UAV schedules the actual relay nodes to start data transmission.

[0206] According to the above steps, the changes in the data transmission link under the change of the UAV position with 100 and 500 unmanned vehicles and wireless sensor nodes participating are shown in the appendix Figure 5 .

[0207] As can be seen from the above results, the method of the present invention can rely on the unmanned aerial vehicle-unmanned vehicle to achieve effective and rapid dynamic planning of the data link of the wireless sensor node, which is of great significance for reducing the energy consumption of the wireless sensor node and extending the lifespan of the wireless sensor node.

[0208] 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, and 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 should not be construed as a limitation of the present invention.

[0209] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An optimization method for the communication link of an unmanned aerial vehicle (UAV) combined with an unmanned vehicle to assist a wireless sensor network, characterized in that: The method includes the following steps: Step S1: Construct a data transmission system for an unmanned aerial vehicle (UAV) combined with an unmanned vehicle to assist a wireless sensor network, including one UAV, unmanned vehicles, and wireless sensor nodes; in the Cartesian coordinate system, the positions of the wireless sensor nodes , are represented as , at time t, the positions of the unmanned vehicles , are represented as , the position of the UAV is represented as , and the flight altitude of the UAV is ; then: Any two wireless sensor nodes and , the distance between them is , and ; Wireless sensor node The distance between and the drone is Wireless sensor node and the unmanned vehicle The distance between them is ; Driverless vehicle The distance between and the drone is Step S2: When the wireless sensor node has a data transmission requirement, the wireless sensor node directly sends a data transmission request message to the UAV, specifically including the location information of the wireless sensor node and the size of the data packet to be sent ; After receiving the data transmission request message, the drone estimates the algorithm running delay and flight speed as well as the channel status information, and estimates the algorithm running delay and the data transmission delay . Then, it further calculates the position of the drone when the data packet arrives ; Step S3: The UAV plans the data transmission link; Step S4: Consider a multi-hop information transmission link from a sensor node to the UAV, and establish a matrix for recording the information of each hop. Step S5: Construct a link energy consumption cost calculation model based on the expected distance; Step S6: Calculate the expectation of the distance corresponding to each hop; Step S7: Construct a data transmission link optimization model; Step S8: Calculate the optimal number and optimal location of virtual relay nodes; Step S9: The UAV broadcasts the location information of all virtual relay nodes , and all the wireless sensor nodes within the area corresponding to each virtual relay node calculate the distance to the virtual relay node. At the same time, all the unmanned vehicles calculate the distance to the virtual relay node, and in a competitive manner, select the wireless sensor node or unmanned vehicle closest to the virtual relay node to join the data transmission link, and finally form an initial data transmission link from the wireless sensor node to the UAV ; The wireless sensor node or unmanned vehicle closest to the virtual relay node joins the data transmission link as the actual relay node; Step S10: Wireless sensor node According to the information in the initial data transmission link of the UAV calculate the distance matrix between all actual relay nodes, substitute it into the communication energy consumption model, and obtain the energy consumption matrix ; Step S11: Optimize the initial data transmission link of the drone ; Step S12: Wireless sensor node Report the information of the current data transmission link to the UAV ; Step S13: The UAV sends a transmission instruction to start data transmission; The said Step S3: The UAV plans the data transmission link includes the following steps: Step S3.1: Let At moment, the flight altitude of the UAV is , then the projection position of the UAV on the ground is , so the distance between the wireless sensor node and the projection position of the UAV on the ground is ; Step S3.2: Let the total number of hops of the data transmission link be hops. Assume that on the straight line from the wireless sensor node to the projection position there is a set of virtual relay nodes , forming an ideal virtual relay link. The coordinates corresponding to the virtual relay nodes are , , is the number of virtual relay nodes; Step S3.3: Assume that from the 1st jump to the th jump, the distances of each jump are respectively ; the distance of the th jump is: ; Formula (1); Step S3.4: Then, the distance from the wireless sensor node to the projection position of the UAV on the ground can be decomposed into: ; Formula (2); The said Step S4 further includes: Step S4: Consider a multi-hop information transmission link from a sensor node to the UAV, and establish a matrix to record the information of each hop. Let \(h\) represent the total number of hops. Then the communication energy consumption for transmitting 1 bit of data from the source node to the destination node is: ; formula (3); ; Formula (4); ; Formula (5); ; formula (6); Among them, respectively represent the energy consumption of data transmission and reception; I represents the amount of data; represents the operating energy consumption of the communication module, is the data aggregation energy consumption; represents the distance from the source node to the destination node , ; respectively represent the energy consumption required to amplify the signal, which depends on the transceiver distance and the bit error rate; The said Step S5: Construct a link energy consumption cost calculation model based on the expected distance includes the following steps: Step S5.1: According to the number of wireless sensor nodes and the distribution area range , the average area of each area where the wireless sensor node is located can be calculated as ; assuming that the area is circular, the corresponding radius can be calculated as ; ; Step S5.2: Taking each virtual relay node as the center, draw a circle with a radius of ; According to the law of large numbers, when the sample size is large enough, and with the addition of the driverless vehicle as a relay node, within each circle, there must be an actual wireless sensor node or a driverless vehicle that can serve as an actual relay node, and the position of the actual relay node within the circle follows a random distribution; The said Step S6: Calculate the expectation of the distance corresponding to each hop includes the following steps: Step S6.1: First hop, calculate the expectations of the distance, the square of the distance, and the fourth power of the distance from the wireless sensor node to any point within the circle centered at the first virtual relay node with a radius of , which are respectively expressed as: ; Formula (7); ; Formula (8); ; Formula (9); Among them, represents the distance of the wireless sensor node to the virtual relay node ; Step S6.2: The jump, , find the expectations of the distance, the square of the distance, and the fourth power of the distance from any point inside the circle with the virtual relay node as the center and as the radius to any point inside the circle with the virtual relay node as the center and as the radius, which are respectively expressed as: ; Formula (10); ; formula (11); ; formula (12); Step S6.3: The th jump, find the expectations of the distance, the square of the distance, and the fourth power of the distance from any point inside the circle with as the center and as the radius to the drone, which are respectively expressed as: ; Formula (13); ; formula (14); ; Formula (15); The said Step S7: Construct a data transmission link optimization model includes the following steps: Step S7.1: , let , and obtain the total energy consumption of the data transmission link as: ; Formula (16); Among them, is the energy consumption for transmitting 1 bit of data from the source node to the destination node; is the energy consumption at the data receiving end; represents the operating energy consumption of the communication module, is the data aggregation energy consumption; The corresponding data transmission energy consumption is: ; formula (17); Among them, , both represent the energy consumption required for amplifying the signal at the transmitting end, which depends on the transceiver distance and the bit error rate; Step S7.2: If the wireless sensor node transmits data directly to the drone, , the corresponding communication energy consumption is: ; formula (18); Step S7.3: Establish a data transmission link optimization model; Let , and establish the initial link optimization model as follows: ; Formula (19); ; Formula (20); Formulas (15) and (16) indicate that the multi-hop or direct transmission mode is determined, and the optimal number of hops is calculated and the distance corresponding to each hop between virtual relay nodes is determined so that the transmission energy consumption of the overall link is minimized; The said Step S8: Calculate the optimal number and optimal location of virtual relay nodes includes the following steps: Step S8.1: Constrain the integer Transform and introduce a tiny constant ,make , convert the integer constraint into an inequality constraint; Step S8.2: For , after obtaining the solution of the objective function , by comparing the value of with in size, if , then directly determine that the wireless sensor node directly transmits data to the UAV; Step S8.3: Reconstruct the data transmission link optimization model into: ; formula (21); ; Formula (22); Step S8.4: Using the convex optimization algorithm, solve equations (21) and (22) to obtain the number of virtual relay nodes and the distances between virtual relay nodes ; Step S8.5: According to the position of the wireless sensor node and the distance between virtual relays , calculate the position of the virtual relay node ; ; Formula (23); Among them, , , , , , .

2. The method for optimizing the communication link of the unmanned aerial vehicle combined with the unmanned vehicle assisted wireless sensor network according to claim 1, wherein: The said step S11: Optimizing the initial data transmission link of the drone includes the following steps: Set the starting point as the wireless sensor node , the end point as the position of the UAV, run the Dijkstra algorithm, and obtain a data transmission link with the lowest overall energy consumption from the energy consumption matrix . .

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