A Multi-User Single-UAV-Assisted Edge Computing Allocation Method Based on Stackelberg Game and a Long-Term Edge Computing Allocation Method

Abstract

The present invention proposes a multi-user single UAV-assisted edge computing allocation method and a long-term edge computing allocation method based on Stackelberg game, including: constructing an edge computing allocation network composed of M user terminals and a single UAV serving as an edge computing server; assuming that the flight period of the UAV is T, dividing the flight period T into N discrete frames; dividing any discrete frame n into M time slots, and based on time division multiple access technology, the user terminals send the data to be processed to the UAV in the corresponding time slots; based on this edge computing allocation network, constructing a Stackelberg game model, in which the user terminals are followers and the UAV is the leader; by solving the Stackelberg game model, obtaining the optimal pricing of the unit data processing capacity; each user terminal determines the quantity of the purchased data processing capacity according to the optimal pricing of the unit data processing capacity.

Description

Technical Field

[0001] The present invention belongs to the technical field of edge computing allocation in wireless communication, and specifically relates to a multi-user single UAV-assisted edge computing allocation method and a long-term edge computing allocation method based on Stackelberg game. Background Technique

[0002] In recent years, with the explosive growth of the Internet of Things and mobile intelligent devices, traditional cloud computing models have faced problems such as transmission latency and bandwidth pressure. To solve these problems, edge computing allocation technology has emerged. By deploying computing resources at the network edge, it realizes local processing and analysis of data, thereby reducing latency and improving service quality. As a flexible and efficient aerial platform, unmanned aerial vehicles (UAVs) provide new possibilities for edge computing allocation. By carrying edge computing servers on UAVs, mobile and distributed computing services can be realized, providing more convenient and efficient computing support for ground users. This UAV-assisted edge computing allocation technology not only expands the application scenarios of edge computing allocation but also improves its flexibility and scalability.

[0003] UAV-assisted edge computing allocation technology has shown great application potential in many fields, such as intelligent transportation, environmental monitoring, disaster relief, etc. In the future, with the continuous progress of technology and the continuous expansion of application scenarios, this technology will bring more convenience and innovation to people's lives and work. However, due to the limited CPU computing resources of UAVs, the key problem to be solved is how to allocate the edge computing capabilities of UAVs to different users in a multi-user scenario. Summary of the Invention

[0004] Object of the Invention: To solve the problem of how to allocate the edge computing capabilities of UAVs to different users in a multi-user scenario, the present invention proposes a multi-user single UAV-assisted edge computing allocation method and a long-term edge computing allocation method based on Stackelberg game.

[0005] Technical Solution: A multi-user single UAV-assisted edge computing allocation method based on Stackelberg game includes:

[0006] Constructing a multi-user single UAV-assisted edge computing allocation network; the multi-user single UAV-assisted edge computing allocation network consists of M user terminals and a single UAV carrying an edge computing server; assuming the flight period of the UAV is T, dividing the flight period T into N discrete frames; for any discrete frame n, , and then dividing it into M time slots , is the length of each discrete frame. Based on the time division multiple access technology, the user terminal sends the data to be processed to the UAV in the corresponding time slot, and the UAV performs data processing; among them, is the time allocation coefficient, ;

[0007] Based on the multi-user single-UAV assisted edge computing allocation network, a Stackelberg game model is constructed. In the Stackelberg game model, the user terminal is the follower and the UAV is the leader; for each user terminal, in each discrete frame, maximizing its own utility function is used as its objective function. For the UAV, in each discrete frame, maximizing the total cost paid by all followers for purchasing its data processing ability is used as its objective function; for each user terminal, its own utility function is the difference between the purchased data processing ability of the UAV and the cost paid for purchasing this data processing ability.

[0008] In each discrete frame, by solving the Stackelberg game model, the optimal pricing of the unit data processing ability in this discrete frame is obtained.

[0009] Each user terminal determines the quantity of data processing ability purchased in this discrete frame according to the optimal pricing of the unit data processing ability.

[0010] Furthermore, for each user terminal, in each discrete frame, maximizing its own utility function as its objective function is expressed as:

[0011] ;

[0012] In the formula, B is the bandwidth, is the transmission power value of user terminal m at discrete frame n, is the receiver noise of the UAV, represents the channel gain from user terminal m to the UAV at discrete frame n, represents the price of the unit data processing ability sold by the UAV to the user terminal.

[0013] Furthermore, the constraint conditions of the objective function of each user terminal include:

[0014] ;

[0015] ;

[0016] Among them, represents the minimum amount of data processing transferred to the UAV at discrete frame n.

[0017] Furthermore, for the UAV, in each discrete frame, the objective function is to maximize the total cost paid by all followers to purchase its data processing capacity, expressed as:

[0018] ;

[0019] In the formula, represents the price per unit data processing capacity sold by the UAV to the user terminal.

[0020] Furthermore, the constraint conditions of the UAV's objective function include:

[0021] The data processed in discrete frame n is less than or equal to the maximum computing amount in the corresponding frame, expressed as:

[0022] ;

[0023] And

[0024] ;

[0025] In the formula, F represents the data processing capacity of the UAV per unit time, B is the bandwidth, is the transmission power value of user terminal m at discrete frame n, is the receiver noise of the UAV, represents the channel gain from user terminal m to the UAV at discrete frame n.

[0026] The present invention discloses a multi-user single-UAV-assisted long-term edge computing allocation method based on Stackelberg game, including:

[0027] Constructing a multi-user single-UAV-assisted edge computing allocation network; the multi-user single-UAV-assisted edge computing allocation network consists of M user terminals and a single UAV equipped with an edge computing server; assuming the flight period of the UAV is T, dividing the flight period T into N discrete frames; for any discrete frame n, , and then dividing it into M time slots , is the length of each discrete frame. Based on time division multiple access technology, user terminals send the data to be processed to the UAV in the corresponding time slots, and the UAV performs data processing; among them, is the time allocation coefficient, ;

[0028] Based on the multi-user single-UAV assisted edge computing allocation network, a Stackelberg game model is constructed. In the Stackelberg game model, the user terminal is the follower and the UAV is the leader. For each user terminal, within the entire flight cycle, maximizing its own utility function is taken as its objective function. For the UAV, within the entire flight cycle, maximizing the total cost paid by all followers for purchasing its data processing capacity is taken as its objective function. For each user terminal, the average data processing capacity of the UAV over the entire flight cycle is taken as the revenue, and the difference between the revenue of the user terminal and the cost paid for purchasing the average data processing capacity is taken as its own utility function.

[0029] By solving the Stackelberg game model, the optimal pricing of the unit data processing capacity within the entire flight cycle is obtained.

[0030] Each user terminal determines the quantity of data processing capacity purchased within the entire flight cycle according to the optimal pricing of the unit data processing capacity within the entire flight cycle.

[0031] Furthermore, for each user terminal, within the entire flight cycle, taking maximizing its own utility function as its objective function is expressed as:

[0032] ;

[0033] In the formula, B is the bandwidth, is the transmission power value of user terminal m at discrete frame n, is the receiver noise of the UAV, represents the channel gain from user terminal m to the UAV at discrete frame n, represents the price of the unit data processing capacity sold by the UAV to the user terminal.

[0034] Furthermore, the constraint conditions of the objective function of each user terminal include:

[0035] ;

[0036] and

[0037] ;

[0038] Among them, represents the minimum data processing volume transferred to the UAV at discrete frame n.

[0039] Furthermore, the objective function of the UAV is expressed as:

[0040] ;

[0041] In the formula, It represents the price of the unit data processing capacity of the drone sold to the user terminal.

[0042] Furthermore, the constraint conditions of the objective function of the drone are as follows:

[0043] ;

[0044] and

[0045] ;

[0046] In the formula, F represents the data processing capacity of the drone per unit time, B is the bandwidth, is the transmission power value of user terminal m at discrete frame n, is the receiver noise of the drone, represents the channel gain from user terminal m to the drone at discrete frame n.

[0047] Beneficial effects: Compared with the prior art, the method of the present invention applies the competition mechanism to the edge computing allocation problem assisted by drones. Through resource games among multiple users, the edge computing allocation assisted by multiple users with a single drone is realized. The method of the present invention physically conforms to the actual application scenario and will be able to be effectively applied to engineering practice. Description of the Drawings

[0048] Figure 1 It is a schematic diagram of the edge computing allocation network model assisted by multiple users with a single drone constructed for Embodiment 1;

[0049] Figure 2 It is a schematic diagram of a method for edge computing allocation assisted by multiple users with a single drone based on Stackelberg game proposed in Embodiment 1;

[0050] Figure 3 It is a schematic diagram of a method for long-term edge computing allocation assisted by multiple users with a single drone based on Stackelberg game proposed in Embodiment 2. Detailed Embodiments

[0051] The technical solution of the present invention will be further elaborated below in conjunction with the drawings and embodiments.

[0052] Embodiment 1: This embodiment proposes a method for edge computing allocation assisted by multiple users with a single drone based on Stackelberg game. As Figure 2 shown, it mainly includes the following steps:

[0053] Step 1: Construct an edge computing allocation network model assisted by multiple users with a single drone, as Figure 1As shown in the figure, in this edge computing allocation network model, it is assumed that there are a total of M user terminals, defined as , since the computing resources and computing capabilities of user terminals are limited, a single drone equipped with an edge computing server is deployed. By sending data to the drone, user terminals can offload the computing load to the drone equipped with a mobile edge computing device (MEC), and then perform calculations on the drone.

[0054] Define the horizontal coordinate of user terminal m as , , since the drone performs directional flight in the air, the channel gain between the drone and each user terminal will change at different time intervals. Assume that the total flight time of the drone is T, which can also be considered as the flight period T. The flight period T is discretely divided into N discrete frames , and the length of each discrete frame is . If is small enough, it can be assumed that the position of the drone remains unchanged within each discrete frame, denoted as , , represents the position information of the drone at discrete frame n, and respectively represent the position information of the drone on the X-axis and Y-axis at discrete frame n.

[0055] Assume that the cruising altitude of the drone is , let represent the channel gain at a distance of 1 meter from the drone. Therefore, at discrete frame n, the channel gain from user terminal m to the drone is expressed as:

[0056] ;

[0057] Since user terminals need to send data to be processed to the drone, in order to avoid interference between user terminals during the task offloading process, time division multiple access (TDMA) technology is adopted. Therefore, for any discrete frame n, , is further divided into M time slots. During the corresponding time slots, user terminal m sends data to the drone, where is the time allocation coefficient of TDMA, and

[0058] ;

[0059] and

[0060] ;

[0061] Step 2: Based on the multi-user single-drone-assisted edge computing allocation network model, the data transmission rate sent by user terminal m to the drone at discrete frame n is expressed as:

[0062] ;

[0063] Where B is the bandwidth, is the transmit power value of user terminal m in discrete frame n, which is known. is the receiver noise of the drone.

[0064] After receiving the data from each user terminal, the drone performs edge computing allocation on the data according to the drone's data processing capacity F (in bit / s) per unit time. Therefore, the amount of data transmitted to the drone from all user terminals in discrete frame n should not be greater than the total amount of data it can process, that is,

[0065] ;

[0066] Since the data transmitted from each user terminal to the UAV in discrete frame n should meet the minimum data processing amount that needs to be transferred to the UAV, we have:

[0067] ;

[0068] in, It represents the minimum amount of data processing transferred to the drone at discrete frame n, in bits.

[0069] In this embodiment, considering that the data processing capacity of the drone is limited per unit time, when multiple user terminals have different computing requirements, how the drone reasonably allocates these computing requirements is a key issue that needs to be solved urgently.

[0070] Considering that all user terminals want to purchase the data processing capabilities of drones for their data calculations, the more data processing capabilities of drones are allocated to a user terminal, the faster it will obtain the calculation results. Therefore, user terminals with different edge computing allocation requirements are in a competitive relationship in purchasing the limited data processing capabilities of drones. Steinberg game is a pure strategy non-cooperative sequential game model in economics. The participants in the game can be divided into leaders and followers according to the priority of actions and the completeness of information. Followers only have partial information and act first; while leaders have all the information of followers and act later. Among them, leaders need to consider the optimal response of followers when setting game strategies, and followers give their own optimal resource purchase size according to the optimal decision of the leader.

[0071] In this embodiment, since the drone has global information and limited computing resources, the drone is used as the leader of the game model, and the user terminal that only has partial information acts as a follower.

[0072] In this embodiment, the purpose for each follower to purchase the data processing capacity of the UAV in the game mechanism is to improve the computing speed of its data.

[0073] The cost paid by each follower is defined as the cost generated by the time allocation coefficient assigned to it by the UAV when the TDMA technology is adopted. That is, the cost function is expressed as , where represents the price of the unit data processing capacity sold by the UAV to the user terminal. Therefore, the utility function of the user terminal as a follower consists of these two parts: the revenue function and the cost function. Among them, the purchased data processing capacity of the UAV is used as the revenue function. Therefore, the utility function of the user terminal is the difference between the revenue function and the cost function, which is expressed as:

[0074] ;

[0075] As the user terminal increases the purchase of data processing capacity, the revenue it obtains also increases, but its cost function will also increase. Therefore, for the follower in the game model, its goal is to obtain the highest revenue at the lowest cost in the game behavior, that is, to maximize its own utility function. Therefore, at discrete frame n, the objective function of the user terminal is expressed as:

[0076] ;

[0077] And the constraint conditions are: and . Among them, is the data processing capacity of the UAV purchased by the terminal user m at discrete frame n.

[0078] For the leader in the game relationship, that is, the UAV, it sells limited data processing capacity to multiple competing terminal users for their data computing. Therefore, its objective function is defined as maximizing the total cost paid by all user terminals for purchasing data processing capacity, which is expressed as:

[0079] ;

[0080] Also, due to the limited data processing capacity of the UAV, the constraint condition for the leader in the Stackelberg game relationship is that the data processed by the edge computing server at discrete frame n should be less than or equal to the maximum computing volume of the edge server CPU in the corresponding frame, that is:

[0081] ;

[0082] And

[0083] ;

[0084] The objective functions at the follower and the leader together constitute the Stackelberg game. By conducting game actions according to certain rules, the two parties in the game can obtain the final Stackelberg equilibrium, that is, the optimal pricing for the unit data processing capacity. Then, each user terminal determines the quantity of computing resources to purchase based on the optimal pricing of the unit data processing capacity.

[0085] Embodiment 2: As can be seen from the Stackelberg game proposed in Embodiment 1, each Stackelberg game is carried out for a discrete frame. If the total flight period T of the UAV is too long or the number of discrete frames is too large, it will cause the network to continuously update the position information of the UAVs on different discrete frames, resulting in the algorithm constantly repeating calculations for the edge computing resource allocation problem, wasting resources and being unfavorable to the robustness of the system. Therefore, in order to reduce the impact on the system robustness caused by the need to bid for the limited data processing capabilities of multiple user terminals on each discrete frame in the edge computing allocation method proposed in Embodiment 1, this embodiment proposes a long-term edge computing allocation method assisted by a multi-user single UAV based on the Stackelberg game.

[0086] In this embodiment, for user terminal m, the average data processing capacity of the purchased UAV over the entire flight period is taken as the revenue, and its objective function is expressed as:

[0087] ;

[0088] The corresponding constraint conditions are:

[0089] ;

[0090] and

[0091] ;

[0092] Where: is the cost function of user terminal m over the entire flight period T, is the amount of data sent by user terminal m to the UAV for processing over the entire flight period T, represents the minimum amount of data transferred to the UAV at discrete frame n.

[0093] The objective function of the UAV in the game relationship is defined as maximizing the total cost paid by all user terminals for purchasing data processing capacity over the entire flight period T of the UAV. Therefore, the objective function of the UAV is expressed as:

[0094] ;

[0095] The corresponding constraint conditions are as follows:

[0096] ;

[0097] and

[0098] ;

[0099] The objective functions at the follower and the leader together constitute a Stackelberg game. By conducting game actions according to certain rules, the two parties in the game can obtain the final Stackelberg equilibrium, that is, the optimal pricing for the unit data processing capacity. Each user terminal determines the quantity of data processing capacity to purchase according to the optimal pricing of the unit data processing capacity.

[0100] See Figure 3 In the distribution method proposed in this embodiment, the game is only executed once during the entire flight cycle T of the unmanned aerial vehicle.

Claims

1. A multi-user single UAV-assisted edge computing allocation method based on Stackelberg game, characterized in that: Including: Construct a multi-user single-UAV-assisted edge computing allocation network; The multi-user single-UAV-assisted edge computing allocation network consists of M user terminals and a single UAV carrying an edge computing server; assuming the flight period of the UAV is T, the flight period T is divided into N discrete frames; For any discrete frame n, it is further divided into M time slots α m [n]ε, where ε is the length of each discrete frame. Based on the time division multiple access technology, the user terminal sends the data to be processed to the UAV in the corresponding time slot, and the UAV processes the data; among them, α m [n] is the time allocation coefficient, Based on the multi-user single-UAV-assisted edge computing allocation network, construct a Stackelberg game model. In the Stackelberg game model, the user terminal is the follower and the UAV is the leader; for each user terminal, in each discrete frame, maximizing its own utility function is used as its objective function. For the UAV, in each discrete frame, maximizing the total cost paid by all followers for purchasing its data processing capacity is used as its objective function; for each user terminal, its own utility function is the difference between the data processing capacity purchased from the UAV and the cost paid for purchasing this data processing capacity; In each discrete frame, by solving the Stackelberg game model, obtain the optimal pricing of the unit data processing capacity in this discrete frame; Each user terminal determines the quantity of data processing capacity purchased in this discrete frame according to the optimal pricing of the unit data processing capacity; For each user terminal, in each discrete frame, maximizing its own utility function is used as its objective function, which is expressed as: where B is the bandwidth, p m [n] is the transmit power value of user terminal m at discrete frame n, σ 2 is the receiver noise of the UAV, h m [n] represents the channel gain from user terminal m to the UAV at discrete frame n, and β represents the price per unit data processing capacity sold by the UAV to the user terminal; For the UAV, in each discrete frame, maximizing the total cost paid by all followers for purchasing its data processing capacity is used as its objective function, which is expressed as: In the formula, β represents the price of the unit data processing capacity sold by the UAV to the user terminal.

2. The multi-user single UAV-assisted edge computing allocation method based on Stackelberg game according to claim 1, characterized in that: The constraint conditions of the objective function of each user terminal include: 0≤α m [n]≤1； where Q m [n] represents the minimum amount of data processing transferred to the UAV at discrete frame n.

3. A multi-user single UAV-assisted edge computing allocation method based on Stackelberg game according to claim 1, characterized in that: The constraint conditions of the objective function of the UAV include: The data processed in discrete frame n is less than or equal to the maximum computing amount in the corresponding frame, which is expressed as: And Where, F represents the data processing capacity of the UAV per unit time, B is the bandwidth, p m [n] is the transmission power value of user terminal m at discrete frame n, σ 2 is the receiver noise of the UAV, h m [n] represents the channel gain from user terminal m to the UAV at discrete frame n.

4. A multi-user single UAV-assisted long-term edge computing allocation method based on Stackelberg game, characterized in that: Including: Construct a multi-user single-UAV-assisted edge computing allocation network; The multi-user single-UAV-assisted edge computing allocation network consists of M user terminals and a single UAV carrying an edge computing server; assuming the flight period of the UAV is T, the flight period T is divided into N discrete frames; For any discrete frame n, it is further divided into M time slots α m [n]ε, where ε is the length of each discrete frame. Based on the time division multiple access technology, the user terminal sends the data to be processed to the UAV in the corresponding time slot, and the UAV processes the data; among them, α m [n] is the time allocation coefficient, Based on the multi-user single-UAV-assisted edge computing allocation network, construct a Stackelberg game model. In the Stackelberg game model, the user terminal is the follower and the UAV is the leader; for each user terminal, during the entire flight period, maximizing its own utility function is used as its objective function. For the UAV, during the entire flight period, maximizing the total cost paid by all followers for purchasing its data processing capacity is used as its objective function; for each user terminal, taking the average data processing capacity of the UAV during the entire flight period as the revenue, and taking the difference between the revenue of the user terminal and the cost paid for purchasing the average data processing capacity as its own utility function; By solving the Stackelberg game model, obtain the optimal pricing of the unit data processing capacity during the entire flight period; Each user terminal determines the quantity of data processing capabilities purchased during the entire flight cycle according to the optimal pricing of the unit data processing capabilities during the entire flight cycle; For each user terminal, during the entire flight cycle, maximizing its own utility function is used as its objective function, expressed as: where B is the bandwidth, p m [n] is the transmit power value of user terminal m at discrete frame n, σ 2 is the receiver noise of the UAV, h m [n] represents the channel gain from user terminal m to the UAV at discrete frame n, and β represents the price per unit data processing capacity sold by the UAV to the user terminal; The objective function of the UAV, expressed as: In the formula, β represents the price of the unit data processing capabilities sold by the UAV to the user terminal.

5. A long - term edge computing allocation method for multi - user single - UAV assisted based on Stackelberg game according to claim 4, characterized in that: The constraint conditions of the objective function of each user terminal include: and 0≤α m [n]≤1 where Q m [n] represents the minimum amount of data processing transferred to the UAV at discrete frame n.

6. A multi-user single UAV-assisted long-term edge computing allocation method based on Stackelberg game according to claim 4, characterized in that: The constraint condition of the objective function of the UAV is: and Where, F represents the data processing capacity of the UAV per unit time, B is the bandwidth, p m [n] is the transmit power value of user terminal m at discrete frame n, σ 2 is the receiver noise of the UAV, h m [n] represents the channel gain from user terminal m to the UAV at discrete frame n.

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