Task unloading method based on unmanned aerial vehicle cooperation

Abstract

A task unloading method based on unmanned aerial vehicle cooperation, relating to the field of unmanned aerial vehicles. The method comprises: constructing an unmanned aerial vehicle cooperation task unloading system; according to the number of CPU cycles which can be executed by a ground user in unit time and the number of CPU cycles of a task, creating a local task unloading model of the ground user; according to the transmitting power of the ground user, the number of CPU cycles which can be executed by MEC servers on an original unmanned aerial vehicle and a cooperative unmanned aerial vehicle in unit time and the number of CPU cycles of the task, creating an unmanned aerial vehicle task unloading model of the ground user; according to the hovering power and the hovering delays of the unmanned aerial vehicles, creating a hovering model of the unmanned aerial vehicles; and, according to the local task unloading model of the ground user, the unmanned aerial vehicle task unloading model of the ground user and the hovering model of the unmanned aerial vehicles, computing by using a second-price sealed-bid auction algorithm to obtain an optimal task unloading allocation solution so as to unload the task. The present invention uses unmanned aerial vehicle assisted mobile edge computing for performing task unloading, thereby improving quality of service and experience of service for users.

Description

A task offloading method based on UAV collaboration

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on July 11, 2023, with application number 202310842568.7 and invention name “A task offloading method based on drone collaboration”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present invention belongs to the field of unmanned aerial vehicles (UAVs), and in particular relates to a task offloading method based on UAV collaboration. Background Art

[0003] Unmanned aerial vehicles (UAVs) boast rapid deployment, high flexibility, strong adaptability, enhanced functionality, and low cost, and hold great promise for applications in wireless communication systems and cellular networks. They can act as both relays and flying mobile terminals, greatly assisting in supporting communications and increasing network capacity. Mobile Edge Computing (MEC), a technology that computes task resources at the edge, can handle resource-intensive and latency-sensitive tasks, and improve user Quality of Service (QoS). It is widely used in areas such as the Internet of Vehicles, artificial intelligence, and the Internet of Things.

[0004] Drone mobile edge computing is a combination of drones and mobile edge computing, that is, installing mobile edge servers on drones. It combines the advantages of drones and mobile edge computing, and can not only establish a good line-of-sight link with ground users (GUs), but also process complex and delay-sensitive tasks in real time.

[0005] For task offloading scenarios involving ground users in remote areas (deserts and plateaus), UAV (unmanned aerial vehicle) communication plays a more advantageous role than ground-based communication due to the sparse population and lack of basic communication equipment in remote areas (deserts and plateaus). It not only promptly addresses the problem of low user offloading efficiency, but also fully utilizes the low cost and high mobility of drones to improve communication service quality and communication channel gain. However, drone energy consumption and task offloading latency are critical factors in UAV communication. In recent years, many studies have focused on optimizing drone energy consumption, task offloading latency, or the weighted sum of drone energy consumption and offloading latency. However, these studies have not considered how to incentivize ground users to offload tasks to drones. When appropriate, tasks are offloaded to drones, while when inappropriate, tasks are offloaded locally. Furthermore, most studies have not considered the task allocation problem between multiple drones and multiple ground users, and have not formulated a reasonable task allocation to maximize the benefits of drone edge computing systems.

[0006] Summary of the Invention

[0007] The purpose of this invention is to propose a task offloading method based on UAV collaboration to improve the task offloading efficiency of users in remote areas and rationally plan the task offloading allocation between UAVs and ground users. The method includes:

[0008] S1: Build a UAV collaborative task offloading system, which includes: M = {1,...m...,M} ground users and N = {1,...n...,N} UAVs with MEC servers. When a ground user directly communicates with any UAV, the ground user generates a task that needs to be offloaded.

[0009] S2: Create a local task offloading model for ground users based on the number of CPU cycles that can be executed by the ground user in unit time, the number of CPU cycles of the task, the data size of the task, and the tolerable delay of the task;

[0010] S3: Create a drone task offloading model for ground users based on the ground user's transmission power, the number of CPU cycles that can be executed per unit time by the MEC servers on the original drone and the cooperative drone, the number of CPU cycles of the task, the data size of the task, and the tolerable delay of the task. The original drone represents the drone that communicates directly with the ground user, and the cooperative drone represents the drone that communicates indirectly with the ground user.

[0011] S4: Create a hovering model of the drone based on the drone's hovering power and hovering delay;

[0012] S5: Based on the local task offloading model of the ground user, the UAV task offloading model of the ground user and the hovering model of the UAV, the second-price auction algorithm is used to calculate the optimal task offloading allocation plan to offload the tasks.

[0013] Furthermore, the local task offloading model of the ground user includes:

[0014] in, represents the task generated at time t when ground user m communicates directly with UAV n, For the task The data size, For the task The number of CPU cycles, For the task Tolerable delay; Indicates a task The latency of local offloading; Indicates a task Energy consumption for local unloading; f m k represents the number of CPU cycles that can be executed by ground user m in unit time; m represents the effective capacitance coefficient of the ground user m, Indicates the battery capacity of the ground user; Indicates a task tolerable delay.

[0015] Furthermore, the creation of a UAV task offloading model for ground users includes:

[0016] S31: Calculate the uplink transmission rate between the ground user and the original UAV based on the ground user's transmission power:

[0017] Where ω is the channel bandwidth, n0 is the noise power, and p m is the transmission power of ground user m; P interf is the maximum received average interference power; is the channel gain between ground user m and original UAV n; is the uplink transmission rate between ground user m and original UAV n;

[0018] S32: Based on the uplink transmission rate between the ground user and the original UAV, the number of CPU cycles that the MEC servers on the original UAV and the collaborative UAV can execute per unit time, the data size of the task, and the tolerable delay of the task, the latency and energy consumption of the task offloaded to the original UAV and the collaborative UAV are calculated respectively:

[0019] in, Indicates a task The time delay of offloading on the original drone n; represents the queuing delay of ground user m to unload the task to the original UAV n; Indicates a task The time delay of offloading on the cooperative UAV h; represents the transmission rate between the original UAV n and the cooperative UAV h; F represents the queuing delay of ground user m to unload tasks to cooperative UAV h; n F represents the number of CPU cycles that the MEC server on the original UAV n can execute per unit time; h k represents the number of CPU cycles that can be executed by the MEC server on the collaborative drone h per unit time; n represents the effective capacitance coefficient of the original UAV n; k h represents the effective capacitance coefficient of the collaborative drone h; p n represents the transmission power of the original UAV n; Indicates a task Energy consumption offloaded on the cooperative UAV h; Indicates a task Energy consumption offloaded on the original UAV n; Indicates that the ground user m will task Uplink transmission energy consumption uploaded to UAV n.

[0020] S33: Create a drone task offloading model for ground users based on the latency and energy consumption of offloading tasks to the original drone and the collaborative drone:

[0021] in, Indicates a task Total delay of unloading on the drone; Indicates a task Total energy consumption offloaded on the drone; Indicates a task The actual processing position parameters, when When indicating a task The task is offloaded to the collaborative UAV, while the opposite means that the task is offloaded to the original UAV.

[0022] Furthermore, the drone hovering model includes: E N =P0·T′

[0023] Where P0 represents the hovering power of the UAV; T′ is the hovering delay of the UAV; E N Indicates the hovering energy consumption of the drone.

[0024] Furthermore, the step of calculating the optimal task offloading allocation scheme using the second price auction algorithm to offload tasks includes:

[0025] S51: Each drone is regarded as a seller, and the ground user that directly communicates with the drone is regarded as a buyer; the lowest asking price of the drone to each ground user is calculated based on the ground user's drone task offloading model, the drone's hovering model, and the number of times the ground user participates in the auction; and the bid content of each ground user is initialized based on the lowest asking price of the drone to each ground user; the bid content of each ground user includes:

[0026] in, represents the bidding content of ground user m, represents the lowest asking price of drone seller n to ground user m, that is, the initial bid price of ground user m.

[0027] S52: creating a profit model for the ground user and the drone based on the bid content of each buyer, the drone task offloading model of the ground user, the drone's hovering model, and the number of times the ground user participates in the auction;

[0028] S53: Each ground user determines whether to participate in the auction based on preset auction participation conditions. When a ground user participates in the auction, it is determined whether the ground user's original drone is idle. If the ground user's original drone is idle, the task processing location is determined. If the ground user's original drone is not idle, it is determined whether the ground user's cooperative drone is idle. If the ground user's cooperative drone is idle, the task processing location is determined. Otherwise, the task is offloaded locally. The preset auction participation conditions include:

[0029] in, Indicates that when ground user m and drone n perform task offloading, the coverage radius of drone n must be greater than or equal to the horizontal distance between ground user m and drone n, X m,nrepresents the horizontal distance between the ground user m and its original UAV n, H is the height of the UAV from the ground, and θ is the fixed beamwidth between the UAV and the ground user;

[0030] S54: creating a profit optimization model for the UAV collaborative task offloading system based on the task processing location of the ground users and the profit model of the ground users and the UAVs, with maximizing the actual total profit of all ground users and all UAVs as the optimization goal; solving the profit optimization model for the UAV collaborative task offloading system to determine the optimal ground user corresponding to each UAV seller as the winner for task offloading, and the remaining ground users as losers; and determining the actual bid of each winning ground user according to the second payment rule;

[0031] S55: The losing ground user updates the bid price for the next round of auction, and the winning ground user uses the actual bid as the bid price for the next round of auction, and then repeats steps S53-S55.

[0032] Furthermore, the minimum asking price of the drone to each ground user includes:

[0033] in, represents the lowest asking price of drone seller n to ground user m, that is, the initial bid price of ground user m; γ E and γ C They represent the economic cost per unit energy consumption and per unit time respectively; τ′ represents the duration of each auction; Represents the number of times ground user m participates in the auction.

[0034] Furthermore, the profit model for ground users and drones includes:

[0035] in, represents the task of ground user m The profit of the ground user m when unloading from the drone; Represents the UAV's mission to the ground user m The profit of the drone when unloading; η m is the weight, representing the bidding index of ground user m.

[0036] Furthermore, the profit optimization model of the UAV collaborative task offloading system includes: stC1-C9 C7:d n1,n2 ≥d min

[0037] Among them, P1 is the target optimization function, T represents the total time slot of the system, Indicates that UAV n has established a direct communication connection with ground user m, and vice versa; C1 represents the energy consumption constraint of the ground user, Indicates a task Unloading is performed on drone n, but not vice versa; Indicates a task Energy consumption of offloading locally; Indicates that the ground user m will task The uplink transmission energy consumption of the transmission to UAV n; C2 represents the delay constraint of the ground user; C3 represents the task offloading decision and task processing location constraint of the ground user; C4 represents the communication establishment constraint between the ground user and the UAV; C5 represents the transmission power constraint of the ground user and the UAV; and denote the maximum transmission power of ground users and UAVs respectively; C6 denotes the coverage constraint between ground users and UAVs; C7 denotes the constraint condition for avoiding collision between UAVs, d n1,n2 Represents the distance between the two drones, d min represents the minimum distance between two UAVs, C8 represents the maximum number of unloaded tasks of the UAV group, Q represents the total number of tasks that the ground user needs to unload, and n max is the maximum number of tasks that can be processed by each drone; C9 represents the number constraint of drones providing offloading services in each round of auction.

[0038] Furthermore, determining the actual bid of each ground user winner according to the second payment rule includes:

[0039] The bid prices of all ground users corresponding to each UAV are grouped into a set P. The bid price of the winning ground user and the bid price higher than the winning ground user are removed from the set P. The highest bid among the remaining bids in the set P is selected as the actual bid of the winning ground user.

[0040] Furthermore, the losing ground user updates the bid price for the next round of auction, including:

[0041] Among them, 1<α<2, Represents the bid price of the losing ground user in the next round of auction.

[0042] The present invention has at least the following beneficial effects

[0043] This paper comprehensively considers practical application scenarios in remote areas and proposes a solution that uses UAV mobile edge computing to assist in offloading GUs in remote areas. This solution, applied in complex, changing environments with low population density, leverages the flexibility, low cost, and compactness of UAVs to provide wider coverage. Compared to base station MEC offloading services, this solution not only meets the service offloading needs of a wider range of GUs but also improves the QoS of these GUs.

[0044] At the same time, the present invention proposes a multi-UAV auction algorithm based on MEC to encourage communication between users and drones, optimize the user task allocation problem, and comprehensively consider the actual bids, asking price willingness, and personal preferences of UAVs and GUs under the constraints of UAVs and GUs transmission power, energy consumption, and latency, so as to maximize the social welfare of the system while ensuring the authenticity of the auction system.

[0045] The present invention optimizes the efficiency of the entire system by designing a second-price auction algorithm that maximizes total profit, and the entire auction process can be carried out sustainably and smoothly. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] FIG1 is a schematic diagram of a UAV collaborative task offloading system according to the present invention;

[0047] FIG2 is a diagram showing the auction role structure of the present invention;

[0048] FIG3 is a diagram of an auction system model of the present invention;

[0049] FIG4 is a flowchart of the auction process of the present invention. DETAILED DESCRIPTION

[0050] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0051] Aiming at the scenario of offloading demands from multiple ground users in remote areas, a task offloading solution based on UAV collaboration was designed. First, the network model, computation offloading model, UAV hovering model and profit model of the entire system were designed. Then, the objective function was constructed according to the profit model, and an auction algorithm was used to incentivize ground users to offload tasks to UAVs. The objective optimization function was solved by a second-price auction algorithm based on maximizing total profit, thus maximizing the total profit of the entire system while ensuring the performance characteristics of the entire auction system.

[0052] As shown in FIG1 , the present invention provides a task offloading method based on UAV collaboration, the method comprising:

[0053] S1: Build a UAV collaborative task offloading system, which includes: M = {1,...m...,M} ground users and N = {1,...n...,N} UAVs with MEC servers. When a ground user directly communicates with any UAV, the ground user generates a task that needs to be offloaded.

[0054] S2: Create a local task offloading model for ground users based on the number of CPU cycles that can be executed by the ground user in unit time, the number of CPU cycles of the task, the data size of the task, and the tolerable delay of the task;

[0055] Furthermore, the local task offloading model of the ground user includes:

[0056] in, represents the task generated at time t when ground user m communicates directly with UAV n, For the task The data size, For the task The number of CPU cycles, For the task Tolerable delay; Indicates a task The latency of local offloading; Indicates a task Energy consumption for local unloading; f m k represents the number of CPU cycles that can be executed by ground user m in unit time; m represents the effective capacitance coefficient of the ground user m, Indicates the battery capacity of the ground user; Indicates a task tolerable delay.

[0057] S3: Create a drone task offloading model for ground users based on the ground user's transmission power, the number of CPU cycles that can be executed per unit time by the MEC servers on the original drone and the cooperative drone, the number of CPU cycles of the task, the data size of the task, and the tolerable delay of the task. The original drone represents the drone that communicates directly with the ground user, and the cooperative drone represents the drone that communicates indirectly with the ground user.

[0058] Furthermore, the creation of a UAV task offloading model for ground users includes:

[0059] S31: Calculate the uplink transmission rate between the ground user and the original UAV based on the ground user's transmission power:

[0060] Where ω is the channel bandwidth, n0 is the noise power, and p m is the transmission power of ground user m; P interf is the maximum received average interference power; is the channel gain between ground user m and original UAV n; is the uplink transmission rate between ground user m and original UAV n;

[0061] S32: Based on the uplink transmission rate between the ground user and the original UAV, the number of CPU cycles that the MEC servers on the original UAV and the collaborative UAV can execute per unit time, the data size of the task, and the tolerable delay of the task, the latency and energy consumption of the task offloaded to the original UAV and the collaborative UAV are calculated respectively:

[0062] in, Indicates a task The time delay of offloading on the original drone n; represents the queuing delay of ground user m to unload the task to the original UAV n; Indicates a task The time delay of offloading on the cooperative UAV h; represents the transmission rate between the original UAV n and the cooperative UAV h; F represents the queuing delay of ground user m to unload tasks to cooperative UAV h; n F represents the number of CPU cycles that the MEC server on the original UAV n can execute per unit time; h k represents the number of CPU cycles that can be executed by the MEC server on the collaborative drone h per unit time; nrepresents the effective capacitance coefficient of the original UAV n; k h represents the effective capacitance coefficient of the collaborative drone h; p n represents the transmission power of the original UAV n; Indicates a task Energy consumption offloaded on the cooperative UAV h; Indicates a task Energy consumption offloaded on the original UAV n; Indicates that the ground user m will task Uplink transmission energy consumption uploaded to UAV n.

[0063] S33: Create a drone task offloading model for ground users based on the latency and energy consumption of offloading tasks to the original drone and the collaborative drone:

[0064] in, Indicates a task Total delay of unloading on the drone; Indicates a task Total energy consumption offloaded on the drone; Indicates a task The actual processing position parameters, when When indicating a task The task is offloaded to the collaborative UAV, while the opposite means that the task is offloaded to the original UAV.

[0065] S4: Create a hovering model of the drone based on the drone's hovering power and hovering delay;

[0066] Furthermore, the drone hovering model includes: E N =P0·T′

[0067] Where P0 represents the hovering power of the UAV; T′ is the hovering delay of the UAV; E N Indicates the hovering energy consumption of the drone.

[0068] S5: Based on the local task offloading model of the ground user, the UAV task offloading model of the ground user and the hovering model of the UAV, the second-price auction algorithm is used to calculate the optimal task offloading allocation plan to offload the tasks.

[0069] Please refer to Figure 4. Considering the above design goals, the energy consumption of drone nodes is minimized to extend the drone's flight time, increase the network's lifespan, and always pay attention to the latency requirements of ground user tasks. Figure 2 is the auction role structure diagram of the present invention. Based on the above design goals, the drone auction system model diagram shown in Figure 3 is established. Furthermore, the use of the second price auction algorithm to calculate the optimal task offloading allocation plan to offload tasks includes:

[0070] S51: Each drone is considered a seller, and the ground user that directly communicates with the drone is considered a buyer. The drone's lowest asking price for each ground user is calculated based on the ground user's drone task offloading model, the drone's hovering model, and the number of times the ground user participates in the auction. The bid content of each ground user is initialized based on the drone's lowest asking price for each ground user.

[0071] In order to make the basic profit of the drone greater than 0 in each auction, the minimum bid of the ground user can be set to the minimum asking price of the drone, thereby ensuring that the profit of the drone in the first round of auction is positive.

[0072] Furthermore, the minimum asking price of the drone to each ground user includes:

[0073] in, represents the lowest asking price of drone seller n to ground user m, that is, the initial bid price of ground user m; γ E and γ C They represent the economic cost per unit energy consumption and per unit time respectively; τ′ represents the duration of each auction; Represents the number of times ground user m participates in the auction.

[0074] Furthermore, the bidding content of each ground user includes:

[0075] in, Indicates the bidding content of ground user m.

[0076] S52: creating a profit model for the ground user and the drone based on the bid content of each buyer, the drone task offloading model of the ground user, the drone's hovering model, and the number of times the ground user participates in the auction;

[0077] Preferably, the profit model for ground users and drones includes:

[0078] in, represents the task of ground user m The profit of the ground user m when unloading from the drone; Represents the UAV's mission to the ground user m The profit of the drone when unloading; η m represents the weight, which represents the bidding index of ground user m.

[0079] Before the drone and ground users participate in the auction, the drone's asking price and the ground user's bid will be initialized first, and it is necessary to ensure that the ground user's bid in each round is always greater than the drone's initial asking price. In order to ensure that the drone's basic profit is greater than 0, the drone's unloading cost can be set to the drone's lowest asking price to ensure that the drone's profit in each round of auction is always positive.

[0080] S53: Each ground user determines whether to participate in the auction based on preset auction participation conditions. When a ground user participates in the auction, it is determined whether the ground user's original drone is idle. If the ground user's original drone is idle, the task processing location is determined. If the ground user's original drone is not idle, it is determined whether the ground user's cooperative drone is idle. If the ground user's cooperative drone is idle, the task processing location is determined. Otherwise, the task is offloaded locally. The preset auction participation conditions include:

[0081] in, Indicates that when ground user m and drone n perform task offloading, the coverage radius of drone n must be greater than or equal to the horizontal distance between ground user m and drone n, X m,n represents the horizontal distance between the ground user m and its original UAV n, H is the height of the UAV from the ground, and θ is the fixed beamwidth between the UAV and the ground user;

[0082] S54: creating a profit optimization model for the UAV collaborative task offloading system based on the task processing location of the ground users and the profit model of the ground users and the UAVs, with maximizing the actual total profit of all ground users and all UAVs as the optimization goal; solving the profit optimization model for the UAV collaborative task offloading system to determine the optimal ground user corresponding to each UAV seller as the winner for task offloading, and the remaining ground users as losers; and determining the actual bid of each winning ground user according to the second payment rule;

[0083] Furthermore, the profit optimization model of the UAV collaborative task offloading system includes: stC1-C9 C7:d n1,n2 ≥d min

[0084] Among them, P1 is the target optimization function, T represents the total time slot of the system, Indicates that UAV n has established a direct communication connection with ground user m, and vice versa; C1 represents the energy consumption constraint of the ground user, Indicates a task Unloading is performed on drone n, but not vice versa; Indicates a task Energy consumption of offloading locally; Indicates that the ground user m will task The uplink transmission energy consumption of the transmission to UAV n; C2 represents the delay constraint of the ground user; C3 represents the task offloading decision and task processing location constraint of the ground user; C4 represents the communication establishment constraint between the ground user and the UAV; C5 represents the transmission power constraint of the ground user and the UAV; and denote the maximum transmission power of ground users and UAVs respectively; C6 denotes the coverage constraint between ground users and UAVs; C7 denotes the constraint condition for avoiding collision between UAVs, d n1,n2 Represents the distance between the two drones, d min represents the minimum distance between two UAVs, C8 represents the maximum number of unloaded tasks of the UAV group, Q represents the total number of tasks that the ground user needs to unload, and n max is the maximum number of tasks that can be processed by each drone; C9 represents the number constraint of drones providing offloading services in each round of auction.

[0085] Furthermore, determining the actual bid of each ground user winner according to the second payment rule includes:

[0086] The bid prices of all ground users corresponding to each UAV are grouped into a set P. The bid price of the winning ground user and the bid price higher than the winning ground user are removed from the set P. The highest bid among the remaining bids in the set P is selected as the actual bid of the winning ground user.

[0087] S55: The losing ground user updates the bid price for the next round of auction, and the winning ground user uses the actual bid as the bid price for the next round of auction, and then repeats steps S53-S55.

[0088] Furthermore, the losing ground user updates the bid price for the next round of auction, including:

[0089] Among them, 1<α<2, Represents the bid price of the losing ground user in the next round of auction.

[0090] Please refer to Figure 3. In this embodiment, a second-price auction algorithm based on total profit maximization is designed. Figure 3 is a model diagram of the entire auction system. Its specific winner determination rules and payment rule processes are as follows: 1) First, UAV 1 and UAV 2 calculate their own lowest asking prices, and ground users 1-6 bid according to the lowest asking prices of the UAVs.

[0091] 2) Based on the bid of ground user 1, drone 1 calculates the buyer's profit of ground user 1 and the seller's profit of drone 1, and then sums them up to obtain the total profit of ground user 1's bid.

[0092] 3) Drone 2 calculates the buyer's profit of ground user 4 and the seller's profit of drone 2 based on the bid of ground user 4, and then sums them up to obtain the total profit of ground user 4's bid; Note: Drone 1 and Drone 2 are carried out in parallel.

[0093] 4) UAV 1 then calculates the total profit of the bids of ground users 2 and 3, and UAV 2 then calculates the total profit of the bids of ground users 5 and 6.

[0094] 5) Afterwards, UAV 1 and UAV 2 determine the winner of this round of auction based on the calculated total profits, that is, the bid with the highest total profit is the winner of this round of auction.

[0095] 6) Then, UAV 1 and UAV 2 sort the bids from ground users 1-3 and ground users 4-6 from large to small, record them as sets A1 and A2, and assume that the winning bids determined by UAV 1 and UAV 2 are a1 and a2.

[0096] 7) Finally, the actual payment of the winning ground user corresponding to drone 1 is: remove a1 and the bid higher than it from set A1, and the highest bid among the remaining bids is the actual bid of winning ground user a1. Similarly, the actual bid of winning ground user a2 corresponding to drone 2 can be obtained.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A task offloading method based on UAV collaboration, characterized in that: include: S1: Construct a UAV collaborative task offloading system, which includes: M = {1,...m...,M} ground users and N = {1,...n...,N} UAVs with MEC servers; when a ground user directly communicates with any UAV, the ground user generates a task that needs to be offloaded; S2: Create a local task offloading model for ground users based on the number of CPU cycles that can be executed by the ground user in a unit time, the number of CPU cycles of the task, the data size of the task, and the tolerable delay of the task; S3: Create a drone task offloading model for ground users based on the transmission power of the ground user, the number of CPU cycles that can be executed per unit time by the MEC servers on the original drone and the cooperative drone, the number of CPU cycles of the task, the data size of the task, and the tolerable delay of the task; where the original drone represents the drone that communicates directly with the ground user, and the cooperative drone represents the drone that communicates indirectly with the ground user; S4: Create a hovering model of the drone according to the hovering power and hovering delay of the drone; S5: According to the local task offloading model of the ground user, the UAV task offloading model of the ground user and the hovering model of the UAV, the optimal task offloading allocation scheme is calculated using the second price auction algorithm to offload the task.

2. The task offloading method based on UAV collaboration according to claim 1 is characterized in that: The local task offloading model of the ground user includes: in, represents the task generated at time t when ground user m communicates directly with drone n, For the task The data size, For the task The number of CPU cycles, For the task Tolerable delay; Indicates the task The latency of offloading locally; Indicates the task In local Energy consumption of unloading; f m represents the number of CPU cycles that can be executed by ground user m per unit time; k m represents the effective capacitance coefficient of the ground user m, Indicates the battery capacity of the ground user; Indicates the task tolerable delay.

3. The task offloading method based on UAV collaboration according to claim 2 is characterized in that: The creation of a UAV task offloading model for ground users includes: S31: Calculate the uplink transmission rate between the ground user and the original UAV based on the transmission power of the ground user: Where ω is the channel bandwidth, n0 is the noise power, and p m is the transmission power of ground user m; P interf is the maximum received average interference power; is the channel gain between ground user m and original UAV n; is the uplink transmission rate between the ground user m and the original UAV n; S32: The latency and energy consumption of the task offloaded on the original UAV and the cooperative UAV are calculated based on the uplink transmission rate between the ground user and the original UAV, the number of CPU cycles that can be executed per unit time by the MEC servers on the original UAV and the cooperative UAV, the data size of the task, and the tolerable delay of the task: in, Indicates the task The time delay of unloading on the original drone n; represents the queuing delay of ground user m to unload the task to the original UAV n; Indicates the task The time delay of offloading on the cooperative UAV h; represents the transmission rate between the original UAV n and the cooperative UAV h; represents the queuing delay of ground user m to unload tasks to cooperative UAV h; F n represents the number of CPU cycles that the MEC server on the original drone n can execute per unit time; F h represents the number of CPU cycles that can be executed by the MEC server on the collaborative drone h per unit time; k n represents the effective capacitance coefficient of the original UAV n; k h represents the effective capacitance coefficient of the cooperative drone h; p n represents the transmission power of the original UAV n; Indicates the task Energy consumption offloaded on the cooperative drone h; Indicates the task Energy consumption offloaded on the original UAV n; Indicates that the ground user m will task The uplink transmission energy consumption uploaded to UAV n; S33: Create a drone task offloading model for ground users based on the latency and energy consumption of offloading tasks to the original drone and the collaborative drone: in, Indicates the task Total time delay for unloading on the drone; Indicates the task In no Total energy consumption offloaded from man and machine; Indicates the task The actual processing position parameters, when When indicating a task offloaded on the collaborative UAV, whereas the opposite means that the task is offloaded on the original UAV.

4. The task offloading method based on UAV collaboration according to claim 3 is characterized in that: The UAV hovering model includes: N =P0·T′ Where P0 represents the hovering power of the UAV; T′ is the hovering delay of the UAV; E N Indicates the hovering energy consumption of the drone.

5. The task offloading method based on UAV collaboration according to claim 4 is characterized in that: The method of using the second price auction algorithm to calculate the optimal task offloading allocation scheme to offload the task includes: S51: Each drone is regarded as a seller, and the ground user that directly communicates with the drone is regarded as a buyer; the lowest asking price of the drone to each ground user is calculated according to the drone task offloading model of the ground user, the hovering model of the drone, and the number of times the ground user participates in the auction, and the bidding content of each ground user is initialized according to the lowest asking price of the drone to each ground user; the bidding content of each ground user includes: in, represents the bidding content of ground user m, represents the lowest asking price of drone seller n to ground user m, that is, the initial bid price of ground user m; S52: creating a profit model for the ground user and the drone according to the bidding content of each buyer, the drone task offloading model of the ground user, the drone hovering model, and the number of times the ground user participates in the auction; S53: Each ground user determines whether to participate in the auction according to the preset auction participation conditions. When the ground user participates in the auction, it is determined whether the original UAV of the ground user is idle. If the original UAV of the ground user is idle, the task processing location is determined. If the original UAV of the ground user is not idle, it is determined whether the cooperative UAV of the ground user is idle. If the cooperative UAV of the ground user is idle, the task processing location is determined. Otherwise, the task is locally unloaded. The preset auction participation conditions include: in, It means that when ground user m and drone n perform task unloading, the coverage radius of drone n must be greater than or equal to the horizontal distance between ground user m and drone n. m,n represents the horizontal distance between the ground user m and its original UAV n, H is the height of the UAV from the ground, and θ is the fixed beam width between the UAV and the ground user; S54: creating a profit optimization model of the UAV collaborative task offloading system based on the task processing position of the ground user, the profit model of the ground user and the UAV, with the maximum actual total profit of all ground users and all UAVs as the optimization target; solving the profit optimization model of the UAV collaborative task offloading system to determine the optimal ground user corresponding to each UAV seller as the winner for task offloading, and the remaining ground users as losers; and determining the actual bid of each ground user winner according to the second payment rule; S55: The losing ground user updates the bid price for the next round of auction, and the winning ground user uses the actual bid as the bid price for the next round of auction, and then repeats steps S53-S55.

6. The task offloading method based on UAV collaboration according to claim 5 is characterized in that: The minimum asking price for each ground user of the drone includes: in, represents the lowest asking price of drone seller n to ground user m, that is, the initial bidding price of ground user m; γ E and γ C denote the economic cost per unit energy consumption and per unit time respectively; τ′ denotes the available Duration; Represents the number of times ground user m participates in the auction.

7. The task offloading method based on UAV collaboration according to claim 6 is characterized in that: The profit model for ground users and drones includes: in, represents the task of ground user m The profit of the ground user m when unloading from the drone; Represents the mission of the UAV to the ground user m The profit of the drone when unloading; η m is the weight, representing the bidding index of ground user m.

8. The task offloading method based on UAV collaboration according to claim 7 is characterized in that: The profit optimization model of the UAV collaborative task offloading system includes: s.t.C1-C9 C7:d n1,n2 ≥d min Among them, P1 is the target optimization function, T represents the total time slot of the system, It means that UAV n has established a direct communication connection with ground user m, and vice versa; C1 represents the energy consumption constraint of the ground user, Indicates the task Unloading is performed on drone n, but not vice versa; Indicates the task Energy consumption for offloading locally; Indicates that the ground user m will task The uplink transmission energy consumption uploaded to UAV n; C2 represents the delay constraint of the ground user; C3 represents the task offloading decision and task processing location constraint of the ground user; C4 represents the communication establishment constraint between the ground user and the UAV; C5 represents the transmission power constraint of the ground user and the UAV; and They represent the maximum transmission power of ground users and UAVs respectively; C6 represents the coverage constraint between ground users and UAVs; C7 represents the constraint condition to avoid collision between UAVs, d n1,n2 represents the distance between the two drones, d min represents the minimum distance between two drones, C8 represents the maximum number of tasks that the drone group needs to unload, Q represents the total number of tasks that ground users need to unload, and n max is the maximum number of tasks that can be processed by each drone; C9 represents the number constraint of drones providing offloading services in each round of auction.

9. The method for unloading tasks based on UAV collaboration according to claim 5 is characterized in that: Determining the actual bid of each ground user winner according to the second payment rule includes: The bidding prices of all ground users corresponding to each drone are grouped into a set P. The bid of the winning ground user and the bid higher than that of the winning ground user are removed from the set P. The highest bid is selected from the remaining bids in the set P as the actual bid of the winning ground user.

10. The task offloading method based on UAV collaboration according to claim 5 is characterized in that: The bid price of the losing ground user when updating the next round of auction includes: Among them, 1<α<2, Represents the bid price of the losing ground user in the next round of auction.

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