Unmanned aerial vehicle and unmanned vehicle combined auxiliary wireless sensor network communication link optimization method

Abstract

The invention relates to the technical field of unmanned aerial vehicle and unmanned vehicle combined application, in particular to an unmanned aerial vehicle and unmanned vehicle combined auxiliary wireless sensor network communication link optimization method, which deduces an expectation model of data transmission link energy consumption according to random distribution characteristics of wireless sensor network nodes and unmanned vehicles. And an auxiliary link optimization model based on the expected distance and the virtual relay node is constructed. A link optimization model is analyzed and reconstructed, a link is optimized by using a convex optimization method, and an initial link is constructed by further adopting a competition-based mode. Finally, an optimal data transmission link optimization method is designed, and data transmission link optimization with optimal energy consumption is achieved. According to the invention, the calculation load in the link construction optimization process is divided into two parts which are respectively loaded to the unmanned aerial vehicle end and the information source node end, so that the calculation cost can be greatly reduced and the operation efficiency of the data transmission system can be improved 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 intelligent 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 speed 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 objectives include: improving data transmission rate, reducing communication delay, reducing wireless node transmission energy consumption and unmanned aerial vehicle / unmanned 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 vehicle-unmanned vehicle.

[0005] With the continuous expansion of the 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 drones and unmanned vehicles, and provides a method for dynamically optimizing data transmission links, which can quickly plan data transmission links according to the data transmission requirements of wireless sensor nodes and in combination with the positions of drones.

[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 drones and unmanned vehicles, the method comprising the following steps:

[0010] Step S1: Construct a data transmission system for a wireless sensor network assisted by drones and unmanned vehicles, including one drone, 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 drone is represented as , and the flight altitude of the drone is ; then:

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

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

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

[0014] The distance between the unmanned vehicle and the drone is .

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

[0016] In the above-mentioned wireless sensor network data transmission system assisted by unmanned aerial vehicles, the unmanned aerial vehicle is mainly responsible for forwarding the data of the wireless sensor nodes to the base station. The unmanned aerial vehicle flies at a constant speed in the distribution range of the sensor nodes according to the pre-set trajectory. The unmanned vehicle can forward the data of the wireless sensor nodes as a mobile relay node while performing other tasks (such as logistics delivery, etc.). The sensor nodes are randomly distributed in the corresponding task area, and the sensor nodes can act as relays to forward data to each other.

[0017] Step S2: When the wireless sensor node When there is a need for data transmission, wireless sensor nodes Send data transmission request message directly to the drone, including wireless sensor node The location information and the size of the data packet to be sent ;

[0018] After the drone receives the data transmission request message, it will , Flight speed And channel state information, estimate the algorithm runtime delay and data transmission delay , and then further calculate the drone’s own position when the data packet arrives .

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

[0020] Step S3.1: Let ,exist At time , the flying altitude of the UAV is , then the projection position of the drone to the ground is , then the wireless sensor node and the projection position of the drone to the ground The distance between ;

[0021] Step S3.2: Assume the total number of hops of the data transmission link is Jump, assuming that from the wireless sensor node To projection position There is a set of virtual relay nodes on the straight line , forming an ideal virtual relay link, the coordinates of the virtual relay node are , , is the number of virtual relay nodes;

[0022] Step S3.3: Assume that the number of jumps from 1 to 2 In the jump, the distance of each jump is ; 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 , which is used 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 and ), 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 energy consumption for data transmission and reception; 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.

[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 drone on the ground it is very unlikely that a group of relay nodes will appear simultaneously on the straight line. According to the wireless sensor nodes quantity 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: With each virtual relay node as the center and as the radius, draw a circle; 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 that can serve 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 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: Construct 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 of the data receiving end; represents the working 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, 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 positions of virtual relay nodes;

[0064] Step S8.1: Transform the integer constraint , introduce a very small constant , let , and convert 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 directly transmits data 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 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 distances between virtual relay nodes ;

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

[0071] ; Formula (23);

[0072] Wherein, , , , , , .

[0073] Step S9: The drone broadcasts the location information of all virtual relay nodes , and all 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 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 , calculate the distance matrix between all actual relay nodes, substitute 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 it to a typical path finding 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 location information of all sensor nodes and unmanned vehicles. It only needs to obtain the location of the source sensor nodes with data transmission requirements, avoiding a large amount of signaling interaction and channel congestion caused by the frequent update of the location 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 location 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 operating 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 side, only the distance to the corresponding location needs to be calculated according to the location of the source sensor nodes. Then, using the corresponding convex optimization algorithm to solve the reconstructed initial link optimization model, the location information of the virtual relay node 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 side, only an energy consumption cost matrix needs to be generated according to the initial link information, and the initial link optimization problem can be analogized to a path - finding problem for further optimization. The above steps can be completed by an embedded mobile platform within a limited time complexity. BRIEF 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 the virtual relay node in the present invention.

[0087] Figure 4 is the attached 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 method of the drone trajectory and the change in the number of participating relays in an embodiment of the present invention for the drone-UGV-assisted wireless sensor nodes. Detailed implementation manners

[0089] The present invention designs a method for dynamically constructing and optimizing energy consumption of a multi-hop transmission link of wireless sensor network nodes under a data transmission architecture jointly assisted by drones and UGVs. First, a method for information interaction between the source wireless sensor node and the drone 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 drone, 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 the convex optimization method 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 data transmission link optimization method is designed.

[0090] The technical solution 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 jointly assisted by drones and UGVs, the method comprising the following steps:

[0092] Step S1: Construct a data transmission system for a wireless sensor network jointly assisted by drones and UGVs, including one drone, UGVs, 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 UGV , is represented as , the position of the drone is represented as , and the flight altitude of the drone is ; then:

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

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

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

[0096] Unmanned vehicle and the distance between the unmanned aerial vehicle is ;

[0097] The number of unmanned aerial vehicles can be expanded according to the mission area, and the number of unmanned 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 of the unmanned aerial vehicle combined with the unmanned vehicle assisting the wireless sensor network, the unmanned aerial vehicle is mainly responsible for forwarding the data of the wireless sensor nodes to the base station, and the unmanned aerial vehicle hovers uniformly in a circular motion within the range of the sensor node distribution according to a preset trajectory. The unmanned vehicle can, while performing other tasks (such as logistics transportation, etc.), 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 forward data to each other as relays.

[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 unmanned aerial vehicle, specifically including the location 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 unmanned aerial vehicle estimates the algorithm operation delay , flight speed and the data transmission delay according to the current position of the unmanned aerial vehicle, and further calculates the position of the unmanned aerial vehicle itself when the data packet arrives ;

[0101] Step S3: The unmanned aerial vehicle plans the data transmission link;

[0102] Step S3.1: Let , at time, the flight altitude of the unmanned aerial vehicle is , then, the projection position of the unmanned aerial vehicle on the ground is , then the distance between the wireless sensor node and the projection position of the unmanned aerial vehicle 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 is a set of virtual relay nodes , forming an ideal virtual relay link. The coordinates corresponding to the virtual relay nodes are , , ; where \(n\) is the number of virtual relay nodes;

[0104] Step S3.3: Assume that 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 and ), represents 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:

[0109] ; Formula (3);

[0110] ; Formula (4);

[0111] ; Formula (5);

[0112] ; Formula (6);

[0113] Among them, respectively represent the energy consumption for data transmission and reception; 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 set of relay nodes appears 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 wireless sensor nodes and the distribution area range , the average area of each region where a 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 that can serve 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] ; Equation (7);

[0120] ; Equation (8);

[0121] ; Equation (9);

[0122] Among them, represents the distance from the wireless sensor node to the virtual relay node ;

[0123] Step S6.2: Jump, , 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, , , there are:

[0146] ; formula (1-4);

[0147] It can be seen that solving the analytical solution of formula (1-4) is very complicated. For this, 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 Figure 4 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 both points are not 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. According to the law of total expectation, there is:

[0150] ; formula (1-6);

[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 ones 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] Transpose the terms and let , and construct the Crofton differential equation as follows:

[0159] ; formula (1-10);

[0160] ; Formula (1-11);

[0161] In the polar coordinate system, solve for When , assume that point p1 is located 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 Part III, 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 to obtain , that is , which is equivalent to .

[0164] Second step, as shown in Appendix Figure 4 Part III, inside a circle, arbitrarily 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 obtain , and 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 Formulas (7)-(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, , All represent the energy consumption required for amplifying the signal at the transmitting end, which depends 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 the initial link optimization model is established 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 , so that the transmission energy consumption of the overall link is minimized.

[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 , then transform 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 , then directly determine 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 distances between virtual relay nodes. ;

[0187] Step S8.5: Based on the positions of the wireless sensor nodes and the distances between virtual relays , calculate the positions of the virtual relay nodes ;

[0188] ; Equation (23);

[0189] where , , , , , .

[0190] Step S9: The UAV broadcasts the location information of all virtual relay nodes . All wireless sensor nodes within the area corresponding to each virtual relay node calculate the distances to the virtual relay nodes, and at the same time all unmanned vehicles calculate the distances to the virtual relay nodes. 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 nodes 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 nodes calculate the distance matrix between all actual relay nodes according to the information in the initial data transmission link of the UAV , substitute it into the communication energy consumption model, i.e., Equation (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 ending 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 nodes report the current data transmission link to the UAV information;

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

[0196] The following is a simulation verification description of the present invention;

[0197] The simulation of an optimization method for a communication link of a UAV combined with an unmanned vehicle assisting 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 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 UAVs and the positions of wireless sensors within a range of 3 km × 3 km, and randomly generate the positions of unmanned vehicles (since both wireless sensor nodes and 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 source sensor nodes to initiate data transmission requests according to the process shown in appendix Figure 2 .

[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 algorithm inputs to obtain the positions and quantities of virtual relay nodes.

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

[0203] Step 6: The remaining nodes compete, and the node closest to the virtual relay node becomes the actual relay node to form 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 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, with 100 unmanned vehicles and 500 wireless sensor nodes involved, the changes in the data transmission link under the change of the UAV's position are shown in the appendix. Figure 5 as shown.

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

[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 therefore cannot be understood as a limitation of the present invention.

[0209] 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 principle and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing communication links of a wireless sensor network assisted by a UAV and an unmanned vehicle, characterized in that: The method comprises the following steps: Step S1: Construct a UAV-assisted wireless sensor network data transmission system, including a UAV, Unmanned vehicles and wireless sensor nodes; in the Cartesian coordinate system, the wireless sensor nodes , The position is represented by , at time t, the driverless car , The position is represented by , the position of the UAV is expressed as , the flight altitude of the drone is ;So: Any two wireless sensor nodes and , The distance between ,and ; Wireless sensor nodes The distance between the drone and ; Wireless sensor nodes and driverless cars The distance between ; Self-driving cars The distance between the drone and ; Step S2: When the wireless sensor node When there is a need for data transmission, wireless sensor nodes Send data transmission request message directly to the drone, including wireless sensor node The location information and the size of the data packet to be sent ; After the drone receives the data transmission request message, it will , Flight speed And channel state information, estimate the algorithm runtime delay and data transmission delay , and then further calculate the drone’s own position when the data packet arrives ; Step S3: The UAV plans the data transmission link; Step S4: Consider a sensor node Multi-hop information transmission link to the drone, establishing a matrix , used to record the information of each hop; Step S5: constructing a link energy consumption cost calculation model based on the expected distance; Step S6: Calculate the expected distance corresponding to each hop; Step S7: constructing a data transmission link optimization model; Step S8: Calculate the optimal number and optimal position of virtual relay nodes; Step S9: The drone broadcasts the location information of all virtual relay nodes , each virtual relay node corresponds to an area where all wireless sensor nodes calculate the distance to the virtual relay node. At the same time, all unmanned vehicles calculate the distance to the virtual relay node and use a competitive method to select the wireless sensor node or unmanned vehicle closest to the virtual relay node to join the data transmission link. Finally, a wireless sensor node to the drone initial data transmission link is formed. ; 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 nodes According to the initial data transmission link of the UAV The information in the calculation is used to calculate the distance matrix between all actual relay nodes, and then the energy consumption matrix is ​​obtained by introducing the communication energy consumption model. ; Step S11: Initial data transmission link to the drone Optimize Step S12: Wireless sensor nodes Report the current data transmission link to the drone information; Step S13: The drone sends a transmission instruction to start data transmission.

2. The method for optimizing communication links of a wireless sensor network assisted by a UAV and an unmanned vehicle according to claim 1, characterized in that: The step S3: the UAV plans the data transmission link, including the following steps: Step S3.1: Let ,exist At time , the flying altitude of the UAV is , then the projection position of the drone to the ground is , then the wireless sensor node and the projection position of the drone to the ground The distance between ; Step S3.2: Assume the total number of hops of the data transmission link is Jump, assuming that from the wireless sensor node To projection position There is a set of virtual relay nodes on the straight line , forming an ideal virtual relay link, the coordinates of the virtual relay node are , , is the number of virtual relay nodes; Step S3.3: Assume that In the jump, the distance of each jump is ;No. The jump distance is: Formula (1) Step S3.4: Then, from the wireless sensor node To the projection position of the drone to the ground The distance can be decomposed into: ;Formula (2).

3. The method for optimizing the communication link of a UAV-assisted wireless sensor network according to claim 2, characterized in that: The step S4 also includes: Step S4: Consider a sensor node Multi-hop information transmission link to the drone, establishing a matrix , used to record the information of each hop, represents the total number of hops, then the communication energy consumption of transmitting 1 bit of data from the source node to the destination node is for: Formula (3) Formula (4) Formula (5) Formula (6) in, They represent the energy consumption of data sending and receiving respectively; Indicates the amount of data; Indicates the working energy consumption of the communication module, Aggregate energy consumption for data; Indicates the distance from the source node to the destination node , ; They represent the energy consumption required to amplify the signal, which depends on the transmission and reception distance and the bit error rate.

4. The method for optimizing communication links of a wireless sensor network assisted by a UAV and an unmanned vehicle according to claim 3 is characterized by: The step S5: constructing a link energy consumption cost calculation model based on the expected distance includes the following steps: Step S5.1: Based on the wireless sensor node Number of And the distribution area , we can calculate the average area of ​​each wireless sensor node: ; 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 radius ; according to the law of large numbers, when the sample size is large enough and with the addition of unmanned vehicles as relay nodes, there must be an actual wireless sensor node in each circle Or an unmanned vehicle can be used as an actual relay node, and the position of the actual relay node in the circle follows a random distribution.

5. The method for optimizing communication links of a wireless sensor network assisted by a UAV and an unmanned vehicle according to claim 4 is characterized in that: The step S6: calculating the expected distance corresponding to each hop includes the following steps: Step S6.1: First hop, find the wireless sensor node To the first 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: Formula (7) Formula (8) Formula (9) in, Represents a wireless sensor node Virtual relay node distance; Step S6.2: Jump, , 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: Formula (10) ;Formula (11); ;Formula (12); 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: ;Formula (13); ;Formula (14) ; ;Official(15).

6. The method for optimizing the communication link of a wireless sensor network assisted by a UAV and an unmanned vehicle according to claim 5, characterized in that: The step S7: constructing a data transmission link optimization model comprises the following steps: Step S7.1: , make , the total energy consumption of the data transmission link is obtained as: ;Formula (16); in, is the energy consumption of transmitting 1 bit of data from the source node to the sink node; is the energy consumption of the data receiving end; Indicates the working energy consumption of the communication module, Aggregate energy consumption for data; The corresponding data transmission energy consumption is: ;Formula (17); in, , Both represent the energy consumption required to amplify the signal at the transmitter, which depends on the transmission and reception distance and the bit error rate; Step S7.2: If the wireless sensor node When transmitting data directly to the drone, , the corresponding communication energy consumption is: ;Formula (18); Step S7.3: Establishing a data transmission link optimization model; make , the initial link optimization model is established as: ;Formula (19); ;Formula (20); Formula (15) and Formula (16) show that determining the multi-hop or direct transmission method and calculating the optimal number of hops , and determine the distance corresponding to each hop between virtual relay nodes , making the transmission energy consumption of the overall link minimum.

7. The method for optimizing communication links of a wireless sensor network assisted by a UAV and an unmanned vehicle according to claim 6 is characterized by: The step S8: calculating the optimal number and optimal position of virtual relay nodes comprises 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 , in order to obtain the objective function After the solution, by comparing The value of Size, if , then directly determine the wireless sensor node Transmit data directly to the drone; Step S8.3: Reconstruct the data transmission link optimization model as follows: ;Formula (21); ;Formula (22); Step S8.4: Using the convex optimization algorithm, solve formula (21) and formula (22) to obtain the number of virtual relay nodes and the distance between virtual relay nodes. ; Step S8.5: Based on the wireless sensor node The location and distance between virtual relays , calculate the location of the virtual relay node ; ;Formula (23); in, , , , , , .

8. The method for optimizing communication links of a wireless sensor network assisted by a UAV and an unmanned vehicle according to claim 7, characterized in that: Step S11: Initial data transmission link for the drone The optimization process involves the following steps: Set the starting point to the wireless sensor node , the end point is the position of the drone, run the Dijkstra algorithm, and get the energy consumption matrix A data transmission link with the lowest overall energy consumption is obtained .

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