A UAV task allocation and trajectory planning method with minimal energy consumption
By optimizing the UAV task allocation and trajectory planning through clustering algorithm and tabu search algorithm, the energy consumption problem of transmitting data center within the predetermined delay is solved, and the energy consumption of UAV is minimized.
Patent Information
- Application Number
- CN202411438705.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-15
- Publication Date
- 2025-09-05
- Estimated Expiration
- 2044-10-15
AI Technical Summary
In a drone-assisted IoT system, how to reduce the total energy consumption of all drones while transmitting data from sensor nodes to the data center within a predetermined delay.
A clustering algorithm is used to divide the monitoring area into K clusters, and the tabu search algorithm is used to determine the access order of each UAV to the data collection points in the corresponding cluster. Combined with the adaptive adjustment of the tabu table length, the task allocation and trajectory planning of the UAV are optimized.
The computational complexity of task allocation is significantly reduced, the trajectory planning performance and accuracy are improved, and the flight distance of each UAV is reduced, thereby reducing the total energy consumption.
Smart Images
Figure CN119322522B_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) assisted wireless communication, and in particular relates to a UAV task allocation and trajectory planning method with minimized energy consumption. Background Art
[0002] With the rapid development of the Internet of Things (IoT), sensor devices are widely deployed in various IoT scenarios. Due to the limited computing power of most sensor nodes, the sensor data they generate needs to be transmitted to a data center for further analysis. Sensor nodes can choose to transmit data directly to the data center or establish a multi-hop route. Direct or multi-hop transmission significantly increases the energy consumption of sensor nodes and the corresponding data transmission duration. For latency-sensitive IoT scenarios, ensuring that sensor nodes transmit data to the data center within a predetermined delay is a challenge.
[0003] The use of drones in the Internet of Things (IoT) has attracted widespread attention in recent years, as they aim to transmit data from sensor nodes to data centers within a predetermined delay. Specifically, drones depart from a data center, fly to corresponding sensor nodes, receive data from them, and then return to the data center to deliver the collected data. The key advantages of drones lie in their high maneuverability, adaptability, and low cost.
[0004] However, since the performance and operation time of drones are restricted by limited onboard energy, in drone-assisted IoT, in order to minimize the total energy consumption of all drones while meeting the constraint of transmitting data from sensor nodes to the data center within a predetermined delay, it is crucial to reasonably assign data collection tasks to each drone and optimize its flight trajectory. Summary of the Invention
[0005] The purpose of the present invention is to address the above problems and propose a drone task allocation and trajectory planning method with minimized energy consumption, so as to meet the requirements of transmitting the data of sensor nodes to the data center within a predetermined delay while minimizing the total energy consumption of all drones in the drone-assisted Internet of Things system.
[0006] To achieve the above object, the technical solution adopted by the present invention is:
[0007] The present invention proposes a method for allocating and planning drone tasks with energy-minimized performance, which is applied to a drone-assisted Internet of Things system. The drone-assisted Internet of Things system includes a data center, N sensor nodes, and K drones. The N sensor nodes are divided into M non-overlapping monitoring areas, where N, K, and M are all positive integers and K is a variable with an initial value of 1. The method for allocating and planning drone tasks with energy-minimized performance includes the following steps:
[0008] Step 1: Divide all monitoring areas into K clusters based on the parameters of the UAV-assisted IoT system. The clustering result is the task assignment result. Each monitoring area has only one data collection point. The parameters of the UAV-assisted IoT system include the geographical location of the data center, the geographical location of the data collection points in each monitoring area, and the current number of drones K. The data collection point is the hovering point of the drone when collecting data from the sensor nodes in the corresponding monitoring area.
[0009] Step 2: Assign one UAV to each cluster to collect data from all sensor nodes in each monitoring area of the corresponding cluster, and determine the order in which each UAV visits the data collection points in the corresponding cluster based on the tabu search algorithm. The order in which the UAV visits the data collection points with the shortest visit distance is used as the trajectory planning result of the cluster.
[0010] Step 3: Calculate the task completion time of each drone and determine whether each drone can deliver the collected data to the data center within the predetermined delay. That is, the task completion time of each drone is less than or equal to the predetermined delay. If so, determine the current clustering result and the access order of the data collection points in each cluster as the final result. Otherwise, set K = K + 1 and return to step 1.
[0011] Step 4: According to the final result, each drone in the corresponding cluster visits the data collection points in the corresponding monitoring area in turn to collect data and then deliver it to the data center.
[0012] Preferably, all monitoring areas are divided into K clusters according to the parameter information of the UAV-assisted Internet of Things system, and the obtained clustering result is the task allocation result, which is as follows:
[0013] Step 1.1: Randomly select one data collection point from all the data collection points in the monitoring area as the cluster center. The data collection points in the unselected monitoring areas are non-cluster centers. The cluster center set and non-cluster center set are represented by the center point set and non-center point set, respectively.
[0014] Step 1.2: Calculate the distances between all data collection points in the non-central point set and all data collection points in the central point set. Use the roulette wheel selection method to select one data collection point from the non-central point set and add it to the central point set. The probability of a data collection point in the non-central point set being selected is positively correlated with the minimum distance from the data collection point to all data collection points in the central point set. That is, the farther the data collection point in the non-central point set is from the data collection point in the central point set, the higher the probability of the data collection point being selected.
[0015] Step 1.3: Repeat step 1.2 until there are K data collection points in the center point set;
[0016] Step 1.4: Execute the i-th iteration, calculate the distance between each data collection point in the non-center point set and each data collection point in the center point set, and assign the data collection points in the non-center point set to the cluster corresponding to the data collection points in the center point set according to the proximity principle to obtain the clustering result. The proximity principle satisfies the following conditions:
[0017] With binary variable λ m,k Represents the association between the data collection points in the non-central point set and the data collection points in the central point set. If the data collection point of the mth monitoring area is in the non-central point set and is assigned to the cluster corresponding to the kth data collection point in the central point set, then λ m,k =1, otherwise, λ m,k = 0, that is, in the i-th iteration, the data collection point of the m-th monitoring area in the non-central point set is assigned to the clustering result λ of the k-th data collection point in the central point set m,k (i) The formula is as follows:
[0018]
[0019] in, Indicates N m (i) and R k (i) the distance between Indicates N m (i) and R z (i) The distance between them, N m (i) represents the set of non-central points at the i-th iteration The mth data collection point in R k (i) represents the center point set at the i-th iteration The kth data collection point in R z (i) represents the center point set at the i-th iteration The zth data collection point in, i = 1 ~ I P , I P is the first preset number of iterations, m=1~M, k=1~K, z=1~K;
[0020] Step 1.5: Gather from non-central points Randomly select a data collection point and calculate the data collection point and the center point set The distance between each data collection point in the , using roulette wheel selection method from the center point set Select one data collection point, for the data collection from the center point The data collection points selected in the set of non-central points The closer the randomly selected data collection points are, the higher the probability of being selected;
[0021] Step 1.6: Exchange the two data collection points selected in step 1.5, that is, the data collection point selected as the cluster center is regarded as a cluster member, and the data collection point selected as a cluster member is regarded as the cluster center, and update λ m,k (i) If the quality of the newly formed clustering result is better, keep the update, otherwise restore to the clustering result before the update. The quality of the newly formed clustering result is calculated by the cost function D(λ m,k (i)) is evaluated, the cost function D(λ m,k (i)) is smaller than the clustering result before the update, which means the quality of the newly formed clustering result is better. m,k The formula for (i)) is as follows:
[0022]
[0023] in, Indicates the data center C0 and R at the i-th iteration k (i) the distance between them;
[0024] Step 1.7: Set i = i + 1, and return to step 1.4 until I P Iterates to obtain the final clustering result.
[0025] Preferably, the order in which each drone accesses the data collection points in the corresponding cluster is determined based on a tabu search algorithm, as follows:
[0026] Step 2.1: For any data collection point in a cluster, use a greedy algorithm to generate the initial access order;
[0027] Step 2.2: Use the initial access order as the initial solution and use the 2-opt operation to generate a set of neighborhood solutions of the current solution. That is, randomly select two data collection points in the corresponding access order, express them as a swap pair, flip the access order of the data collection points between the swap pairs, and construct a neighborhood solution of the current solution.
[0028] Step 2.3: Select the optimal solution from the set of neighborhood solutions of the current solution, that is, the solution with the shortest flight distance. The flight distance d of the kth UAV is f,k , the formula is as follows:
[0029]
[0030] Among them, V k,l represents the data collection point visited by the k-th drone, represents the data center C0 and the data collection point V visited by the k-th drone for the first time k,1 The distance between represents the data collection point V visited by the k-th drone k,land the data collection point V visited by the k-th drone (l+1) k,l+1 The distance between represents the number of data collection points that the k-th drone needs to visit, Indicates the data collection point last visited by the kth drone The distance C0 between the data center and the network.
[0031] Step 2.4: If the optimal solution is the historical optimal solution, it is used as the current solution for the next iteration and the taboo table is updated. Otherwise, the suboptimal solution that is not in the taboo table is selected as the current solution for the next iteration and the taboo table is updated. The taboo table is updated if the optimal solution or suboptimal solution is not in the taboo table, otherwise the optimal solution or suboptimal solution is updated to the end of the taboo table.
[0032] Preferably, the length of the tabu table of the tabu search algorithm satisfies the following formula:
[0033]
[0034] Among them, L T (j) represents the length of the taboo table at the jth iteration, j = 1 ~ I T Indicates the iterative subscript of the tabu search algorithm, I T Indicates the second preset number of iterations, L T,max Indicates the maximum allowed length of the taboo table, L T,min Indicates the minimum allowed length of the taboo table, Indicates rounding down.
[0035] Preferably, the mission completion time of each drone is calculated as follows:
[0036]
[0037] Among them, t k represents the task completion time of the kth UAV, C m represents the data collection point of the mth monitoring area, represents the set of data collection points that the k-th drone needs to visit, t h,m represents the hovering time of the UAV in the mth monitoring area to collect data, t f,k represents the flight time of the kth UAV, H represents the flight altitude of the UAV, v h Indicates the speed of the drone when ascending or descending.
[0038] Preferably, when the sensor node sends data to the UAV, a time division multiplexing protocol is used, and the data is sent in order from small to large channel gain.
[0039] Compared with the prior art, the present invention has the following beneficial effects:
[0040] The clustering algorithm adopted by this method can greatly reduce the computational complexity of task allocation to as low as one-third of the existing technology. By improving the length of the taboo table in the taboo search algorithm, the length of the taboo table can be changed with the search process, without manually setting the taboo table length, which greatly improves the trajectory planning performance and accuracy. According to the predetermined delay constraint of the drone data collection and the minimum drone energy consumption constraint, the clustering results of the monitoring area and the access order of the data collection points in each cluster can be determined, and the data collection task can be completed in hovering mode. By reducing the flight distance of each drone, the total energy consumption of all drones can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] Figure 1 A flowchart of the energy-minimizing UAV task allocation and trajectory planning method of the present invention;
[0042] Figure 2 Schematic diagram of the application scenario of the present invention. DETAILED DESCRIPTION
[0043] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.
[0044] It should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art in the art of this application. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.
[0045] like Figure 1-2 As shown in FIG, a method for minimizing energy consumption of UAV task allocation and trajectory planning is applied to a UAV-assisted Internet of Things system. The UAV-assisted Internet of Things system includes a data center, N sensor nodes, and K UAVs. The N sensor nodes are divided into M non-overlapping monitoring areas, where N, K, and M are all positive integers, and K is a variable with an initial value of 1. The method for minimizing energy consumption of UAV task allocation and trajectory planning includes the following steps:
[0046] Step 1: Divide all monitoring areas into K clusters based on the parameter information of the UAV-assisted IoT system. The clustering result is the task allocation result. Each monitoring area has only one data collection point. The parameter information of the UAV-assisted IoT system includes the geographical location of the data center, the geographical location of the data collection point in each monitoring area, and the current number of drones K. The data collection point is the hovering point of the drone when collecting data from the sensor nodes in the corresponding monitoring area.
[0047] The application scenarios of this embodiment are as follows Figure 2 As shown in Figure 2, the UAV-assisted IoT system includes: N sensor nodes, K drones and 1 data center. Among them, the N sensor nodes are distributed in M non-overlapping monitoring areas in the UAV-assisted IoT system. Figure 2 In the UAV-assisted IoT system, all monitoring areas are divided into K clusters, indicated by dashed circles. K drones are dispatched to collect data from sensor nodes. One drone is associated with each cluster and is used to access all monitoring areas within that cluster. Each drone departs from the data center and visits all monitoring areas within a predetermined delay, following the order in which they are accessed within their cluster. It then returns to the data center and delivers the collected data. When collecting data in a monitoring area, the drone hovers over the data collection point corresponding to that area. The sensor nodes collect data from the environment and transmit the collected data to the drone.
[0048] This method focuses on clustering the monitoring area and optimizing the access order of drone data collection points in the cluster. In a specific embodiment, a clustering algorithm based on partitioning around a center point is proposed to cluster the data collection points in the monitoring area according to their geographical locations. This clustering algorithm has better robustness to isolated points and has lower computational complexity than the partitioning algorithm around a center point. The clustering algorithm based on partitioning around a center point is as follows:
[0049] Input: The locations of data collection points in all monitoring areas, the number of clusters K and the first preset number of iterations I P ;
[0050] Output: A set of K clusters of monitoring areas.
[0051] In one embodiment, all monitoring areas are divided into K clusters based on the parameter information of the UAV-assisted Internet of Things system. The obtained clustering result is the task allocation result, which is as follows:
[0052] Step 1.1: Randomly select one data collection point from all the data collection points in the monitoring area as the cluster center. The data collection points in the unselected monitoring areas are non-cluster centers. The cluster center set and non-cluster center set are represented by the center point set and non-center point set, respectively.
[0053] Step 1.2: Calculate the distances between all data collection points in the non-central point set and all data collection points in the central point set. Use the roulette wheel selection method to select one data collection point from the non-central point set and add it to the central point set. The probability of a data collection point in the non-central point set being selected is positively correlated with the minimum distance from the data collection point to all data collection points in the central point set. That is, the farther the data collection point in the non-central point set is from the data collection point in the central point set, the higher the probability of the data collection point being selected.
[0054] Step 1.3: Repeat step 1.2 until there are K data collection points in the center point set;
[0055] Step 1.4: Execute the i-th iteration, calculate the distance between each data collection point in the non-center point set and each data collection point in the center point set, and assign the data collection points in the non-center point set to the cluster corresponding to the data collection points in the center point set according to the proximity principle to obtain the clustering result. The proximity principle satisfies the following conditions:
[0056] With binary variable λ m,k Represents the association between the data collection points in the non-central point set and the data collection points in the central point set. If the data collection point of the mth monitoring area is in the non-central point set and is assigned to the cluster corresponding to the kth data collection point in the central point set, then λ m,k =1, otherwise, λ m,k = 0, that is, in the i-th iteration, the data collection point of the m-th monitoring area in the non-central point set is assigned to the clustering result λ of the k-th data collection point in the central point set m,k (i) The formula is as follows:
[0057]
[0058] in, Indicates N m (i) and R k (i) the distance between Indicates N m (i) and R z (i) The distance between them, N m (i) represents the set of non-central points at the i-th iteration The mth data collection point in R k (i) represents the center point set at the i-th iteration The kth data collection point in R z (i) represents the center point set at the i-th iteration The zth data collection point in, i = 1 ~ I P , I Pis the first preset number of iterations, m=1~M, k=1~K, z=1~K;
[0059] Step 1.5: Gather from non-central points Randomly select a data collection point and calculate the data collection point and the center point set The distance between each data collection point in the , using roulette wheel selection method from the center point set Select one data collection point, for the data collection from the center point The data collection points selected in the set of non-central points The closer the randomly selected data collection points are, the higher the probability of being selected;
[0060] Step 1.6: Exchange the two data collection points selected in step 1.5, that is, the data collection point selected as the cluster center is regarded as a cluster member, and the data collection point selected as a cluster member is regarded as the cluster center, and update λ m,k (i) If the quality of the newly formed clustering result is better, keep the update, otherwise restore to the clustering result before the update. The quality of the newly formed clustering result is calculated by the cost function D(λ m,k (i)) is evaluated, the cost function D(λ m,k (i)) is smaller than the clustering result before the update, which means the quality of the newly formed clustering result is better. mk The formula for (i)) is as follows:
[0061]
[0062] in, Indicates the data center C0 and R at the i-th iteration k (i) the distance between them;
[0063] Step 1.7: Set i = i + 1, and return to step 1.4 until I P After iterations, the final clustering result is obtained, that is, the M monitoring areas are finally divided into K clusters.
[0064] In one embodiment, when the sensor node sends data to the drone, a time division multiplexing protocol is used, and the data is sent sequentially in the order of channel gain from small to large.
[0065] To ensure that the drone receives all data generated by all sensor nodes in the monitoring area corresponding to the data collection point during each hover, the drone's hovering time at the data collection point depends on the data transmission time of the sensor node with the worst channel quality, considering that the channel gain follows a large-scale fading model. When the sensor nodes send data to the drone, they use a time-division multiplexing protocol, sending data to the drone in order of increasing channel gain.
[0066] Step 2: Assign one UAV to each cluster to collect data from all sensor nodes in each monitoring area of the corresponding cluster, and determine the access order of each UAV to the data collection points in the corresponding cluster based on the tabu search algorithm. The access order of the data collection points with the shortest access distance is used as the trajectory planning result of the cluster.
[0067] In one embodiment, the order in which each drone accesses the data collection points in the corresponding cluster is determined based on a tabu search algorithm, as follows:
[0068] Step 2.1: For any data collection point in a cluster, use a greedy algorithm to generate the initial access order;
[0069] Step 2.2: Use the initial access order as the initial solution and use the 2-opt operation to generate a set of neighborhood solutions of the current solution. That is, randomly select two data collection points in the corresponding access order, express them as a swap pair, flip the access order of the data collection points between the swap pairs, and construct a neighborhood solution of the current solution.
[0070] Step 2.3: Select the optimal solution from the set of neighborhood solutions of the current solution, that is, the solution with the shortest flight distance. The flight distance d of the kth UAV is f,k , the formula is as follows:
[0071]
[0072] Among them, V k,l represents the data collection point visited by the k-th drone, represents the data center C0 and the data collection point V visited by the k-th drone for the first time k,1 The distance between represents the data collection point V visited by the k-th drone k,l and the data collection point V visited by the k-th drone (l+1) k,l+1 The distance between represents the number of data collection points that the k-th drone needs to visit, Indicates the data collection point last visited by the kth drone The distance C0 between the data center and the network.
[0073] Step 2.4: If the optimal solution is the historical optimal solution, it is used as the current solution for the next iteration and the taboo table is updated. Otherwise, the suboptimal solution that is not in the taboo table is selected as the current solution for the next iteration and the taboo table is updated. The taboo table is updated if the optimal solution or suboptimal solution is not in the taboo table, otherwise the optimal solution or suboptimal solution is updated to the end of the taboo table.
[0074] Specifically, for any UAV k, trajectory planning is performed based on the tabu search algorithm, and its trajectory planning performs the following operations:
[0075] Input: The set of monitoring areas associated with the kth UAV and the second preset number of iterations I T ;
[0076] Output: The order in which the kth UAV visits the data collection points in its corresponding cluster;
[0077] The greedy algorithm is used to generate the initial access sequence. Specifically, the drone corresponding to the cluster starts from the data center and visits the data collection point closest to the current location each time. After visiting all data collection points in the cluster, it returns to the data center. The taboo table is a container used to store taboo objects in the taboo search algorithm. Taboo objects refer to solutions that are temporarily prohibited from being searched again during the search process. In the taboo search algorithm of this embodiment, an adaptive taboo table length L is used. T (j) was proposed, which allows the length of the tabu table to change during the search process without manually setting the tabu table length, greatly improving the search performance and accuracy.
[0078] In one embodiment, the length of the tabu table of the tabu search algorithm satisfies the following formula:
[0079]
[0080] Among them, L T (j) represents the length of the taboo table at the jth iteration, j = 1 ~ I T Indicates the iterative subscript of the tabu search algorithm, I T Indicates the second preset number of iterations, L T,max Indicates the maximum allowed length of the taboo table, L T,min Indicates the minimum allowed length of the taboo table, Indicates rounding down.
[0081] Step 3: Calculate the mission completion time of each drone and determine whether each drone can deliver the collected data to the data center within the predetermined delay, that is, the mission completion time of each drone is less than or equal to the predetermined delay. If so, determine the current clustering result and the access order of the data collection points in each cluster as the final result. Otherwise, set K = K + 1 and return to step 1.
[0082] In one embodiment, the mission completion time of each drone is calculated as follows:
[0083]
[0084] Among them, t k represents the task completion time of the kth UAV, C mrepresents the data collection point of the mth monitoring area, represents the set of data collection points that the k-th drone needs to visit, t h,m represents the hovering time of the UAV in the mth monitoring area to collect data, t f,k represents the flight time of the kth UAV, H represents the flight altitude of the UAV, v h Indicates the speed of the drone during ascent or descent. The drone's mission completion time includes both data collection time (hover time) and flight time. The scheduled delay is a preset time threshold and can be adjusted based on actual needs.
[0085] Step 4: According to the final result, each drone in the corresponding cluster visits the data collection points in the corresponding monitoring area in turn to collect data and then deliver it to the data center.
[0086] The clustering algorithm adopted in this method can greatly reduce the computational complexity of task allocation to as low as one-third of the existing technology. By improving the length of the taboo table in the taboo search algorithm, the length of the taboo table can be changed with the search process, and there is no need to manually set the length of the taboo table, which greatly improves the trajectory planning performance and accuracy. According to the predetermined delay constraint of the drone data collection and the minimum drone energy consumption constraint, the clustering results of the monitoring area and the access order of the data collection points in each cluster can be determined, and the data collection task can be completed in a hovering mode. By reducing the flight distance of each drone, the total energy consumption of all drones can be reduced. Finally, after the clustering results and the access order of the data collection points in each cluster are determined, each drone can visit the data collection points of the corresponding monitoring area in the corresponding cluster in turn to collect data and deliver it to the data center.
[0087] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0088] The above-described embodiments merely represent specific and detailed examples of the present application and should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.
Claims
1. A method for minimizing energy consumption of UAV task allocation and trajectory planning, applied to a UAV-assisted Internet of Things system, characterized by: The UAV-assisted Internet of Things system includes a data center, N sensor nodes, and K UAVs. The N sensor nodes are divided into M non-overlapping monitoring areas, where N, K, and M are all positive integers, and K is a variable with an initial value of 1. The UAV task allocation and trajectory planning method for minimizing energy consumption includes the following steps: Step 1: Divide all monitoring areas into K clusters based on the parameter information of the UAV-assisted IoT system. The clustering result is the task assignment result. Each monitoring area has only one data collection point. The parameter information of the UAV-assisted IoT system includes the geographical location of the data center, the geographical location of the data collection point in each monitoring area, and the current number of drones K. The data collection point is the hovering point of the drone when collecting data from the sensor nodes in the corresponding monitoring area; Step 2: Assign one UAV to each cluster to collect data from all sensor nodes in each monitoring area of the corresponding cluster, and determine the order in which each UAV visits the data collection points in the corresponding cluster based on the tabu search algorithm. The order in which the UAV visits the data collection points with the shortest visit distance is used as the trajectory planning result of the cluster. The tabu table length of the tabu search algorithm satisfies the following formula: ； in, represents the length of the tabu table at the jth iteration, represents the iterative subscript of the tabu search algorithm, represents the second preset number of iterations, Indicates the maximum allowed length of the taboo table, Indicates the minimum allowed length of the taboo table, Indicates rounding down; Step 3: Calculate the task completion time of each drone and determine whether each drone can deliver the collected data to the data center within the predetermined delay, that is, the task completion time of each drone is less than or equal to the predetermined delay. If so, the current clustering result and the access order of the data collection points in each cluster are determined as the final result. Otherwise, set , return to step 1; Step 4: According to the final result, each drone is ordered to visit the data collection points in the corresponding monitoring area in the corresponding cluster in turn to collect data and then deliver it to the data center.
2. The method for minimizing energy consumption of unmanned aerial vehicle (UAV) task allocation and trajectory planning according to claim 1, wherein: According to the parameter information of the UAV-assisted IoT system, all monitoring areas are divided into K clusters, and the obtained clustering results are the task allocation results, which are as follows: Step 1.1: Randomly select one data collection point from all the data collection points in the monitoring area as the cluster center. The data collection points in the unselected monitoring areas are non-cluster centers. The cluster center set and non-cluster center set are represented by the center point set and non-center point set, respectively. Step 1.2: Calculate the distances between all data collection points in the non-central point set and all data collection points in the central point set. Use the roulette wheel selection method to select one data collection point from the non-central point set and add it to the central point set. The probability of a data collection point in the non-central point set being selected is positively correlated with the minimum distance from the data collection point to all data collection points in the central point set. That is, the farther the data collection point in the non-central point set is from the data collection point in the central point set, the higher the probability of the data collection point being selected. Step 1.3: Repeat step 1.2 until there are K data collection points in the center point set; Step 1.4: Execute the i-th iteration, calculate the distance between each data collection point in the non-center point set and each data collection point in the center point set, and assign the data collection points in the non-center point set to the cluster corresponding to the data collection points in the center point set according to the proximity principle to obtain the clustering result. The proximity principle satisfies the following conditions: With binary variables Represents the association between the data collection points in the non-central point set and the data collection points in the central point set. If the data collection point of the mth monitoring area is in the non-central point set and is assigned to the cluster corresponding to the kth data collection point in the central point set, then ,otherwise, , that is, In the iteration, the data collection points of the mth monitoring area in the non-central point set are assigned to the kth data collection point in the central point set. The formula is as follows: ； in, express and The distance between express and The distance between Indicates the The set of non-central points at the iteration The mth data collection point in Indicates the The center point set at the iteration The kth data collection point in Indicates the The center point set at the iteration The zth data collection point in , , is the first preset number of iterations, m=1~M, k=1~K, z=1~K; Step 1.5: Gather from non-central points Randomly select a data collection point and calculate the data collection point and the center point set The distance between each data collection point in the , using roulette wheel selection method from the center point set Select one data collection point, for the data collection from the center point The data collection points selected in the set of non-central points The closer the randomly selected data collection points are, the higher the probability of being selected; Step 1.6: Exchange the two data collection points selected in step 1.5, that is, the data collection point selected as the cluster center is regarded as a cluster member, and the data collection point selected as a cluster member is regarded as the cluster center, and update If the quality of the newly formed clustering result is better, keep updating, otherwise restore to the clustering result before updating. The quality of the newly formed clustering result is determined by the cost function For evaluation, the cost function The value of is smaller than the clustering result before the update, which means the quality of the newly formed clustering result is better. The formula is as follows: ； in, Indicates the data center at the i-th iteration and the distance between them; Step 1.7: Set i=i+1 and return to step 1.4 until Iterates to obtain the final clustering result.
3. The method for minimizing energy consumption of unmanned aerial vehicle (UAV) task allocation and trajectory planning according to claim 1, wherein: The tabu search algorithm is used to determine the order in which each drone accesses the data collection points in the corresponding cluster, as follows: Step 2.1: For any data collection point in a cluster, use a greedy algorithm to generate the initial access order; Step 2.2: Use the initial access order as the initial solution and use the 2-opt operation to generate a set of neighborhood solutions of the current solution. That is, randomly select two data collection points in the corresponding access order, express them as a swap pair, flip the access order of the data collection points between the swap pairs, and construct a neighborhood solution of the current solution. Step 2.3: Select the optimal solution from the set of neighborhood solutions of the current solution, that is, the solution with the shortest flight distance. The flight distance of the kth UAV is , the formula is as follows: ； in, Indicates the kth UAV Data collection points visited, Indicates data center and the data collection point visited by the kth drone for the first time The distance between Indicates the kth UAV Data collection points visited and the kth drone Data collection points visited The distance between represents the number of data collection points that the k-th drone needs to visit, Indicates the data collection point last visited by the kth drone Distance to the data center ; Step 2.4: If the optimal solution is the historical optimal solution, it is used as the current solution for the next iteration and the taboo table is updated. Otherwise, the suboptimal solution that is not in the taboo table is selected as the current solution for the next iteration and the taboo table is updated. The update of the taboo table is to add the optimal solution or suboptimal solution if it is not in the taboo table, otherwise update the position of the optimal solution or suboptimal solution to the end of the taboo table.
4. The method for UAV task allocation and trajectory planning with minimized energy consumption according to claim 1, wherein: The mission completion time of each drone is calculated as follows: ； in, represents the mission completion time of the k-th UAV, represents the data collection point of the mth monitoring area, represents the set of data collection points that the k-th drone needs to visit, represents the hovering time of the UAV in the mth monitoring area to collect data, represents the flight time of the kth UAV, H represents the flight altitude of the UAV, Indicates the speed of the drone when ascending or descending.
5. The method for assigning and planning UAV tasks and trajectories with minimized energy consumption according to claim 1, wherein: When the sensor node sends data to the UAV, a time division multiplexing protocol is used, and the data is sent in order from small to large channel gain.