A flexible variable-speed fixed-wing unmanned aerial vehicle energy-saving data acquisition method and system

Abstract

The application discloses a kind of flexible variable-speed fixed-wing unmanned aerial vehicle energy-saving data acquisition method and system, method includes the following steps: obtaining the position coordinates of sensor node and data center, initialization is carried out to access order, flight speed and collection speed;According to given access path, the optimal solution of flight speed and collection speed is obtained by using convex optimization method;According to all node access order all possible order, obtain new access order;Judge whether the new path corresponding to new access order satisfies the node AOI less than maximum AOI value;Under new path, the total energy consumed by UAV is calculated;Judge whether the total energy of UAV energy consumption meets the set condition, output optimal path and speed;Fully consider the different needs of fixed-wing UAV in different modes: in flight mode, to maintain the real-time of node data, UAV needs to fly at high speed;And in data receiving mode, speed needs to be reduced to ensure complete data reception.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication, and particularly relates to a flexible variable-speed fixed-wing unmanned aerial vehicle energy-saving data collection method and system. BACKGROUND

[0002] The advent of the 5th Generation Mobile Communication Technology (5G) marks the arrival of a new era, providing strong support for the Internet of Things (IoT) with high transmission speed, low latency, and extensive connectivity. From smart homes to smart cities, from industrial automation to smart agriculture, various devices and sensors can be interconnected in real time. According to statistics, the proportion of IoT devices in wireless connections will increase from 33% in 2018 to 50% in 2023, with a total of 14.7 billion connections. This undoubtedly presents unprecedented challenges for network operators. As a key component of the IoT, sensors are responsible for sensing data in the environment and uploading these data to remote centers for analysis and decision-making through data collectors. Traditional data collectors are statically deployed, which is simple and low-cost, but when sensors are located in remote areas with complex terrain, it is often difficult to ensure effective data collection. In addition, with the rapid development of real-time sensor monitoring applications, the timeliness of monitoring data is increasingly demanding. Once the data is not updated in time, it may cause a series of serious problems, and even catastrophic consequences.

[0003] To address these challenges, unmanned aerial vehicles (UAVs) have become a promising solution due to their low cost, high mobility, and ease of deployment. By carrying various sensors and cameras, UAVs can monitor and collect data from ground targets in real time or periodically from the air, and are widely used in industries, agriculture, environmental monitoring, and other fields, providing efficient and safe services to society. At the same time, as the application market of UAVs continues to expand, their classification standards have become increasingly diverse. According to the configuration of the wings, UAVs are mainly divided into two types: fixed-wing UAVs and rotary-wing UAVs. Rotary-wing UAVs can freely ascend and descend, and are flexible in hovering, but have small load capacity and limited battery capacity. Compared with rotary-wing UAVs, fixed-wing UAVs are more energy-efficient, have strong load capacity, fast flight speed, and strong endurance, but they cannot hover at a fixed position. These two types of UAVs each have unique advantages, so when choosing a UAV, the appropriate type needs to be determined based on the needs of different scenarios.

[0004] Compared with traditional IoT communication networks, IoT networks with UAVs have the following significant advantages: 1. Improving the life of IoT devices. UAVs can approach IoT devices to reduce signal attenuation during propagation, thereby reducing the energy consumption of IoT devices during data transmission. 2. Improve channel quality. By adjusting the flight position, UAVs can establish stable and reliable line-of-sight (LoS) connections with ground devices, optimizing the quality of communication links. 3. Extend coverage. Due to the high flight altitude of UAVs, the probability of establishing LoS connections between them and ground devices is greatly increased, thereby effectively expanding the coverage area of the network. These advantages make UAVs a very promising technology in IoT networks. Although UAVs have many advantages, they still face a series of challenges in assisting IoT communication networks, including limited battery capacity and complex path planning problems.

[0005] Currently, energy-efficient dynamic deployment of UAV-assisted data collection has attracted the attention of many scholars and experts, but existing work is mainly based on the characteristics and energy consumption model of rotor UAVs, and lacks research on fixed-wing UAV data collection. At the same time, most of the research assumes that UAVs fly at a constant speed for data collection, ignoring the different needs of different sensor nodes for information timeliness, which may lead to data invalidation problems. In view of the above analysis, the present application selects a fixed-wing UAV as a mobile data receiver to assist a wireless sensor network in real-time data collection, aiming to meet the data timeliness while minimizing UAV energy consumption. SUMMARY

[0006] In order to solve the problems existing in the prior art, the present application provides a flexible variable-speed fixed-wing UAV energy-saving data collection method, which is based on the combination of flight-collection mode of fixed-wing UAVs, introduces the freshness of AoI quantized node data, and gives the AoI calculation model under the strategy. At the same time, in order to support the differentiated needs of different node data timeliness, a flexible variable-speed fixed-wing UAV energy-saving data collection method and system are proposed.

[0007] In order to achieve the above purpose, the technical scheme adopted by the present application is a flexible variable-speed fixed-wing UAV energy-saving data collection method, comprising the following steps:

[0008] Step 1, obtain the position coordinates of the sensor nodes and the data center, initialize the access order, flight speed and collection speed;

[0009] Step 2: According to the given access path, the optimal solution of flight speed and collection speed is obtained by using convex optimization method; According to all possible orderings of the access order of all nodes, a new access order is obtained; It is judged whether the new path corresponding to the new access order satisfies the condition that the AOI of the node is less than the maximum AOI value, if not, the order is adjusted until it meets the condition; The total energy consumed by the UAV is calculated under the new path, and the total energy consumed by the UAV is calculated.

[0010] Step 3: It is judged whether the total energy consumed by the UAV meets the set condition, if it meets, the optimal path and speed are output; Otherwise, step 2 is continued to be executed until convergence.

[0011] Further, it includes flight mode: the UAV flies from the data center or sensor node to the sensor node and returns to the data center; Collection mode: after collecting data from the current sensor, the UAV moves to the next sensor at a higher speed, and the UAV enters the monitoring area of the sensor node and starts collecting data.

[0012] Further, in the collection mode, the UAV slows down and adopts a symmetrical trajectory to prolong the collection time, wherein represents the collection speed, the AOI performance index is introduced, and the AOI expression of the kth node visited by the node is:

[0013]

[0014] wherein represents the time when the data collection starts from , and , is the time for the UAV to fly from to , .

[0015] Further, the accurate position coordinates of the sensor nodes and the data center are obtained through the positioning and navigation system; According to the task demand and the node distribution, the access order is preliminarily planned.

[0016] Further, according to all possible orderings of the access order of all nodes, a new access order is obtained, including: based on all possible orderings of the access order of all nodes, the optimal access order of the node is obtained by dynamic programming, and the new access order is obtained by using genetic algorithm to approximately solve.

[0017] Further, the optimal solution of the flight speed and the collection speed is obtained by using a convex optimization method, including: firstly, giving the path of the UAV for speed optimization, expanding the expression of energy consumption, obtaining a convex function on the positive domain, and solving the flight speed and the collection speed by using a convex optimization theory.

[0018] Further, the set condition that the total energy consumption of the UAV satisfies is that the absolute value of the energy consumption value of the previous time minus the energy consumption value of the present time divided by the absolute value of the energy consumption value of the previous time is less than a, wherein a is a maximum tolerance.

[0019] The application also provides a fixed-wing UAV energy-saving data collection system with flexible speed change, which collects data based on the fixed-wing UAV energy-saving data collection method.

[0020] The application also provides a computer device, which comprises a processor and a memory, the memory is used for storing a computer executable program, the processor reads the computer executable program from the memory and executes, and the processor can realize the fixed-wing UAV energy-saving data collection method with flexible speed change when executing the computer executable program.

[0021] The application also provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program can realize the fixed-wing UAV energy-saving data collection method with flexible speed change when being executed by a processor.

[0022] Compared with the prior art, the application has at least the following beneficial effects: since the fixed-wing UAV cannot hover when collecting data, the application proposes a fixed-wing UAV information collection strategy combining the flight mode and the data receiving mode. The strategy fully considers the different requirements of the fixed-wing UAV in different modes: in the flight mode, the UAV needs to fly at a high speed to maintain the real-time nature of the node data; and in the data receiving mode, the speed needs to be reduced to ensure the complete reception of data. Based on this, the application further proposes the fixed-wing UAV energy-saving data collection method with flexible speed change, so that the fixed-wing UAV can be dynamically deployed with low energy consumption, and the relationship between energy saving and data freshness is effectively balanced. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 The flowchart for the realization of the application is shown in the figure;

[0024] Figure 2a The path and speed diagram when the AoI threshold is 100s under the strategy of the application is shown in the figure, Figure 2b The path and speed diagram when the AoI threshold is 100s under the dynamic programming strategy is shown in the figure, Figure 2c The path and speed diagram when the AoI threshold is 100s under the greedy strategy is shown in the figure;

[0025] Figure 3a The comparison chart of UAV energy consumption under different AoI threshold values for the strategy of the present application, dynamic programming and greedy strategy, Figure 3b The comparison chart of UAV task completion time under different AoI threshold values for the strategy of the present application, dynamic programming and greedy strategy.

[0026] Figure 4 The path and speed chart when the AoI threshold value of part of the nodes is changed to 40s for the strategy of the present application.

[0027] Figure 5a The AoI values of each sensor node before adjusting part of the AoI threshold value for the strategy of the present application.

[0028] Figure 5b The AoI values of each sensor node after adjusting part of the AoI threshold value for the strategy of the present application. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0030] The energy-saving data collection method of the flexible variable-speed fixed-wing UAV proposed in the present application is divided into two modes, flight mode and collection mode, according to the state of data collection by the UAV. The flight mode is the process of the UAV flying from the data center or a sensor node to the sensor node and returning to the data center . The sensor node is used to collect various types of data (such as environmental parameters, images, videos; the sensor node can be pre-deployed or can be deployed by the UAV). The UAV takes off from the starting point, follows the preset flight plan and navigation system, and goes to each sensor node for data collection. In the flight process, the UAV needs to monitor its own state in real time; when the UAV reaches the vicinity of the sensor node, the data is collected from the sensor node through wireless communication; after completing the data collection task, the UAV returns to the starting point.

[0031] After collecting data from the current sensor, in order to reduce the task completion time and improve the timeliness of data, the UAV should move to the next sensor at a higher speed, denoted as The collection mode is the process of the UAV entering the sensor node the monitoring area, in this mode, the UAV should slow down and adopt a symmetrical trajectory to prolong the data collection time, wherein represents the collection speed, since the information generated by the sensor has a high requirement for timeliness, the AOI performance index is introduced, the AOI expression of the kth node is

[0032]

[0033] represents the time when the data collection starts from , and , is the time for the UAV to fly from to , The total energy consumption of the UAV in the data collection process can be divided into energy consumption generated by flight and energy consumption generated by data collection, and the calculation method is as follows:

[0034]

[0035] The UAV has limited on-board energy, therefore, the total energy consumption of the UAV is taken as a performance index. By optimizing the visiting sequence, flight speed and collection speed, the total energy consumption of the UAV is minimized. To solve the problem, the problem is divided into two sub-problems: speed optimization and visiting sequence optimization. The speed optimization is to find a set of optimal speeds and , for speed optimization, the path of the UAV is given first, the expression of the energy consumption is expanded, the function is a convex function on the positive domain, the flight speed and the collection speed are obtained by solving the convex optimization theory; after obtaining the flight speed and the collection speed, a path is found to minimize the total energy consumption of the UAV according to the flight speed and the collection speed, the feasible solution set of this problem is defined by all possible permutations of the visiting sequence of the nodes, which is a typical Traveling Salesman Problem, therefore, the optimal visiting sequence of the nodes is obtained by using dynamic programming, and then the genetic algorithm is used to approximately solve the problem.

[0036] With reference to the accompanying drawings, the specific implementation steps of the application are as follows: Figure 1

[0037] Step 1: input the position coordinates of the sensor nodes and the data center, and initialize the visiting sequence, flight speed and collection speed.

[0038] ​​​Step 2: Under the given access path, we use convex optimization techniques to seek the optimal flight speed and collection speed; after obtaining the optimal solution of the speed, in all node access sequences all possible sequences, the optimal access sequence of the node is obtained through dynamic programming, and then the new access sequence is obtained by means of genetic algorithm to approximately solve, whether the new path generated satisfies the condition that the AOI of the node is less than the maximum AOI value, that is , if not, the sequence is adjusted again until it meets the condition; then the total energy consumed by the UAV under the path is calculated.

[0039] Step 3: whether the energy consumption value of the UAV satisfies the condition that the absolute value of the energy consumption value minus the absolute value of the energy consumption value of the previous time divided by the absolute value of the energy consumption value of the previous time is less than a, that is, whether it converges, wherein a is the maximum tolerance, that is ; if it meets, the optimal path and speed are output; otherwise, step 2 is continued until convergence.

[0040] The application can provide a fixed-wing unmanned aerial vehicle energy-saving data acquisition system with flexible speed change, and data acquisition is carried out based on the fixed-wing unmanned aerial vehicle energy-saving data acquisition method described above.

[0041] The effect of the application can be further illustrated by the following simulation examples.

[0042] In the simulation of the application, the computer system is Windows 10, and the simulation environment is MATLAB 2019b; with reference to Figure 2a , Figure 2b and Figure 2c , the path and speed comparison chart of the application and dynamic programming, greedy strategy when the AoI threshold is 100s. It can be seen that the length of the path obtained by the application and dynamic programming is the shortest. The total distance of the path generated by the greedy strategy is obviously longer, so the flight speed of the comparison scheme must also be increased accordingly to ensure that the information freshness of the node is lower than the set value. The fundamental reason for this difference is the difference in strategy selection. Specifically, dynamic programming can find the optimal path by decomposing the problem and storing the optimal solution of the sub-problem. However, with the increase of the number of nodes, the complexity also increases sharply, which leads to that the solution of large-scale TSP becomes quite time-consuming. The scheme proposed in the application is improved based on genetic algorithm. The information age limit and energy consumption requirement are used as part of the screening mechanism to ensure that the generated population meets the conditions, and the chromosomes with high fitness are also retained to reduce the search time. When the greedy strategy is used, the UAV only selects the nearest sensor to collect data until all sensor nodes are accessed. Obviously, the node freshness limit is ignored.

[0043] Reference Figure 3a and Figure 3b, the present application and dynamic programming, greedy strategy in different AoI threshold value of UAV energy consumption, task completion time comparison chart, it is not difficult to find that with the increase of information age limit threshold, the energy consumption of UAV is reduced and then tends to be stable. Because with the relaxation of information age limit, UAV can reduce the flight speed to reduce the energy consumption, thereby increasing the task completion time. This phenomenon shows that the pursuit of higher information freshness will inevitably be accompanied by higher energy consumption. In addition, the energy consumption of the UAV generated by the method described in the present application is slightly lower than that based on the greedy strategy. At the same time, the task completion time also shows similar results.

[0044] Figure 4 To change the information age threshold of sensor and to 40s, the path and speed chart generated by the method proposed in the present application. In Figure 2b , the access order of the UAV is: 0→9→8→1→7→3→4→5→2→6→0. When the AoI threshold of and is reduced, its access order becomes: 0→6→2→8→1→5→4→7→3→9→0. We can find that the order of nodes and is adjusted to the last. Therefore, it is concluded that the down-regulation of the AoI threshold of the node will make the UAV visit them last. And this adjustment of the order ensures that the information storage time of the node is compressed to the shortest before the UAV returns to the data center, so that the actual value of their AoI is lower than the threshold.

[0045] Reference Figure 5a and Figure 5b intuitively describes the influence of the node AoI limit condition on the UAV path planning generated by the present application, wherein Figure 5a is the AoI value of each sensor node before adjusting the AoI threshold, Figure 5b is the AoI value of each sensor node after adjusting. By comparison, it can be seen that when the AoI threshold is lowered, the actual AoI value of sensors and are lower than 40s, indicating that the method proposed in the present application can flexibly respond to the individualized requirements of different nodes for AoI, and adjust the node access order of the UAV in real time to ensure that the information freshness requirement is met.

[0046] Based on the concept of the method, the present application can also provide a flexible variable-speed fixed-wing UAV energy-saving data acquisition system, which comprises an initialization module, a planning energy consumption calculation module and a result acquisition module:

[0047] The initialization module is used for acquiring position coordinates of the sensor nodes and the data center, and initializing the visiting sequence, the flight speed and the collection speed;

[0048] The energy consumption planning and calculation module is used for obtaining the optimal solution of the flight speed and the collection speed by using a convex optimization method according to a given visiting path, obtaining a new visiting sequence according to all possible sequences of the visiting sequence of all nodes, judging whether a new path corresponding to the new visiting sequence satisfies the condition that the AOI of the node is less than the maximum AOI value, adjusting the sequence if the condition is not satisfied until the condition is satisfied, and calculating the total energy consumption of the UAV under the new path.

[0049] The result acquisition module is used for judging whether the total energy consumption of the UAV satisfies a set condition, outputting the optimal path and speed if the condition is satisfied, and jumping back to the energy consumption planning and calculation module for planning and calculation until convergence if the condition is not satisfied.

[0050] In another aspect, the application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the energy-saving data acquisition method of the flexible variable-speed fixed-wing UAV.

[0051] The application further provides a computer device, which comprises a processor and a memory, the memory is used for storing a computer executable program, the processor reads the computer executable program from the memory and executes, and the processor executes the computer executable program to implement the energy-saving data acquisition method of the flexible variable-speed fixed-wing UAV.

[0052] The computer device can be a notebook computer, a desktop computer or a workstation.

[0053] The processor can be a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC) or a ready programmable gate array (FPGA).

[0054] The memory can be an internal storage unit of the notebook computer, the desktop computer or the workstation, such as a memory or a hard disk, or can be an external storage unit, such as a mobile hard disk or a flash card.

[0055] The computer-readable storage medium can include a computer storage medium and a communication medium. The computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. The computer-readable storage medium can include read-only memory (ROM), random access memory (RAM), solid state disk (SSD), optical disk, etc. Among them, the random access memory can include resistance random access memory (ReRAM) and dynamic random access memory (DRAM).

[0056] The above is only to illustrate the technical idea of the present application, and cannot limit the protection scope of the present application. Any modification made according to the technical idea of the present application on the basis of the technical scheme falls within the protection scope of the claims of the present application.

Claims

1. A flexible, variable-speed, energy-saving data acquisition method for fixed-wing unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: Step 1: Obtain the location coordinates of the sensor nodes and the data center, and initialize the access order, flight speed, and collection speed; Step 2: Based on the given access path, use convex optimization methods to obtain the optimal solutions for flight speed and collection speed; based on all possible sorting of the access order of all nodes, obtain a new access order; determine whether the new path corresponding to the new access order satisfies that the AOI of the node is less than the maximum AOI value. If not, adjust the order until the condition is met; calculate the total energy consumed by the UAV under the new path. Step 3: Determine whether the total energy consumption of the UAV meets the set conditions. If it does, output the optimal path and speed; otherwise, continue to execute Step 2 until convergence. Including flight modes: drones from data centers or sensor nodes Fly to sensor node And return to the data center; Acquisition mode: After acquiring data from the current sensor, the drone moves to the next sensor at a higher speed, and the drone enters the sensor node. Data collection begins after the monitored area is identified; In data acquisition mode, the drone slows down and adopts a symmetrical trajectory to extend the data acquisition time. To represent the acquisition speed, an AOI performance metric is introduced, where the k-th node is the accessed node. The AOI expression is: in Indicates from The moment when data collection begins, and , For UAV from Fly to Time, .

2. The energy-saving data acquisition method for a flexible, variable-speed fixed-wing UAV according to claim 1, characterized in that, The precise location coordinates of sensor nodes and data centers are obtained through a positioning and navigation system; the access sequence is initially planned based on task requirements and node distribution.

3. The energy-saving data acquisition method for a flexible, variable-speed fixed-wing UAV according to claim 1, characterized in that, To obtain a new access order based on all possible sorting of all node access orders, the following steps are taken: based on all possible sorting of all node access orders, the optimal access order of nodes is obtained through dynamic programming, and a genetic algorithm is used to approximate the solution to obtain the new access order.

4. The energy-saving data acquisition method for a flexible, variable-speed fixed-wing UAV according to claim 1, characterized in that, The optimal solutions for flight speed and collection speed are obtained using convex optimization methods. For speed optimization, given the path of the UAV, the energy consumption expression is expanded to obtain a convex function in the positive domain. The flight speed and collection speed are then obtained by using convex optimization theory.

5. The energy-saving data acquisition method for a flexible, variable-speed fixed-wing UAV according to claim 1, characterized in that, The total energy consumption of the UAV is subject to the following condition: the absolute value of the previous energy consumption minus the current energy consumption divided by the absolute value of the previous energy consumption is less than a, where a is the maximum tolerance.

6. A flexible, variable-speed, energy-saving data acquisition system for fixed-wing unmanned aerial vehicles (UAVs), characterized in that: Data acquisition is performed based on the energy-saving data acquisition method for fixed-wing UAVs as described in any one of claims 1-5.

7. A computer device, characterized in that, It includes a processor and a memory, the memory being used to store a computer-executable program, the processor reading part or all of the computer-executable program from the memory and executing it, and the processor executing part or all of the computer-executable program enabling the implementation of claim 1. The energy-saving data acquisition method for a fixed-wing UAV with flexible speed control as described in any one of the following five claims.

8. A computer-readable storage medium, characterized in that, A computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of claim 1.

5. The energy-saving data acquisition method for a fixed-wing UAV with flexible speed control as described in any one of the above.

Images