Optimization method and device of mobile edge network and storage medium

Abstract

The invention discloses a mobile edge network optimization method and device, and a storage medium. The method comprises the steps of constructing a three-layer mobile edge network model based on Internet of Things equipment, an unmanned aerial vehicle and a satellite; constructing a calculation model related to the three-layer mobile edge network model; wherein the calculation model determines the time delay and energy consumption of the three-layer mobile edge network model based on the following parameters: a first variable between the Internet of Things equipment and the unmanned aerial vehicle, a bandwidth allocation proportion between the Internet of Things equipment and the unmanned aerial vehicle, a second variable between the Internet of Things equipment and the unmanned aerial vehicle, and a first position coordinate of the unmanned aerial vehicle; according to the calculation model, a joint optimization problem is determined, and the joint optimization problem is used for indicating to solve the minimum calculation time delay; and dividing the joint optimization problem into different sub-problems through a block coordinate descent method, and respectively optimizing the first variable, the bandwidth allocation proportion, the second variable and the first position coordinate based on the sub-problems.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, device and storage medium for optimizing a mobile edge network. Background Art

[0002] With the continuous development of mobile communications, communication services have put forward higher requirements on communication systems in terms of latency, reliability, and speed. The traditional method is to transmit computing tasks to the cloud for processing, but this method will have a long latency. Based on this, mobile edge computing technology has been proposed. Mobile edge computing technology is a new computing technology that can sink computing power from the cloud to the edge of the network, thereby further improving data transmission capabilities and data transmission efficiency.

[0003] Drone-assisted mobile edge computing network refers to giving drones computing capabilities and enabling resource-constrained IoT devices to offload tasks with large amounts of data to drones with high computing capabilities, thereby achieving the technical effect of meeting the service quality requirements of ground users and reducing latency.

[0004] Furthermore, based on the drone-assisted mobile edge computing network, a three-layer mobile edge network model based on IoT devices-drones-satellites can be constructed, and the computing tasks of IoT devices can be cached to drones and offloaded to satellites for processing, thereby giving full play to the advantages of mobile edge computing networks.

[0005] In addition, in the three-layer mobile edge network model of IoT devices, drones, and satellites, minimizing mission latency is one of the most important problems to be solved. However, since there are many variables involved in the three-layer network model of IoT devices, drones, and satellites, the mission latency problem to be solved is relatively complicated.

[0006] With regard to the technical problem existing in the above-mentioned prior art that there are many variables involved in the three-layer mobile edge network model formed between IoT devices, drones and satellites, which leads to a complex task delay problem to be solved, no effective solution has been proposed so far. Summary of the invention

[0007] The embodiments of the present disclosure provide a method, device and storage medium for optimizing a mobile edge network, so as to at least solve the technical problem existing in the prior art that the task delay problem to be solved is relatively complicated due to the large number of variables involved in the three-layer mobile edge network model formed between the Internet of Things device, the drone and the satellite.

[0008] According to one aspect of an embodiment of the present disclosure, a method for optimizing a mobile edge network is provided, comprising: constructing a three-layer mobile edge network model based on an Internet of Things device, a drone, and a satellite; constructing a computing model related to the three-layer mobile edge network model, wherein the computing model determines the latency and energy consumption of the three-layer mobile edge network model based on the following parameters: a first variable between the Internet of Things device and the drone, a bandwidth allocation ratio between the Internet of Things device and the drone, a second variable between the Internet of Things device and the drone, and a first position coordinate of the drone; determining a joint optimization problem according to the computing model, wherein the joint optimization problem is used to indicate a computing latency to be minimized; and dividing the joint optimization problem into different sub-problems through a block coordinate descent method, and optimizing the first variable, the bandwidth allocation ratio, the second variable, and the first position coordinate based on the sub-problems.

[0009] According to another aspect of an embodiment of the present disclosure, a storage medium is further provided, the storage medium including a stored program, wherein when the program is running, a processor executes any one of the methods described above.

[0010] According to another aspect of an embodiment of the present disclosure, there is also provided an optimization device for a mobile edge network, including: a mobile edge network model construction module, used to construct a three-layer mobile edge network model based on an Internet of Things device, a drone and a satellite; a computing model construction module, used to construct a computing model related to the three-layer mobile edge network model, wherein the computing model determines the delay and energy consumption of the three-layer mobile edge network model based on the following parameters: a first variable between the Internet of Things device and the drone, a bandwidth allocation ratio between the Internet of Things device and the drone, a second variable between the Internet of Things device and the drone, and a first position coordinate of the drone; a joint optimization problem determination module, used to determine a joint optimization problem according to the computing model, wherein the joint optimization problem is used to indicate the computing delay to be minimized; and an optimization module, used to divide the joint optimization problem into different sub-problems through a block coordinate descent method, and optimize the first variable, the bandwidth allocation ratio, the second variable and the first position coordinate based on the sub-problems.

[0011] According to another aspect of an embodiment of the present disclosure, there is also provided an optimization device for a mobile edge network, comprising: a processor; and a memory connected to the processor, for providing the processor with instructions for processing the following processing steps: constructing a three-layer mobile edge network model based on an Internet of Things device, an unmanned aerial vehicle, and a satellite; constructing a computing model related to the three-layer mobile edge network model, wherein the computing model determines the latency and energy consumption of the three-layer mobile edge network model based on the following parameters: a first variable between the Internet of Things device and the unmanned aerial vehicle, a bandwidth allocation ratio between the Internet of Things device and the unmanned aerial vehicle, a second variable between the Internet of Things device and the unmanned aerial vehicle, and a first position coordinate of the unmanned aerial vehicle; determining a joint optimization problem according to the computing model, wherein the joint optimization problem is used to indicate the computing delay to be minimized; and dividing the joint optimization problem into different sub-problems through a block coordinate descent method, and optimizing the first variable, the bandwidth allocation ratio, the second variable, and the first position coordinate based on the sub-problems.

[0012] The present application provides an optimization method for a mobile edge network. First, the processor constructs a three-layer mobile edge network model based on an Internet of Things device, a drone, and a satellite. Then, the processor constructs a computing model related to the three-layer mobile edge network model. Among them, the computing model determines the latency and energy consumption of the three-layer mobile edge network model based on the following parameters: the first variable between the Internet of Things device and the drone, the bandwidth allocation ratio between the Internet of Things device and the drone, the second variable between the Internet of Things device and the drone, and the first position coordinates of the drone. Further, the processor determines a joint optimization problem based on the computing model. Finally, the processor divides the joint optimization problem into different sub-problems through the block coordinate descent method, and optimizes the first variable, the bandwidth allocation ratio, the second variable, and the first position coordinates based on the sub-problems.

[0013] From the above, it can be seen that after the processor in the present application determines the joint optimization problem (i.e., the problem of minimizing the computing delay) according to the computing model, it divides the joint optimization problem into different sub-problems through the block coordinate descent method (i.e., given the bandwidth allocation ratio and the first position coordinate, solve the associated variable (i.e., the first variable) and the cache decision variable (i.e., the second variable); given the associated variable, the cache decision variable and the first position coordinate, solve the bandwidth allocation ratio; given the associated variable, the bandwidth allocation ratio and the cache decision variable, solve the first position coordinate). Then, the processor converts each non-convex sub-problem into a convex sub-problem, and solves each sub-problem separately, so as to determine the minimized computing delay, thereby ensuring the communication quality.

[0014] This solves the technical problem in the prior art that the task delay problem to be solved is complicated due to the large number of variables involved in the three-layer mobile edge network model composed of IoT devices, drones and satellites. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present application. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0016] Figure 1 is a hardware structure block diagram of a computing device for implementing the method according to Embodiment 1 of the present disclosure;

[0017] Figure 2 It is a schematic diagram of a three-layer mobile edge network model corresponding to an Internet of Things terminal—unmanned aerial vehicle—satellite according to Embodiment 1 of the present disclosure;

[0018] Figure 3 is a flow chart of a method for optimizing a mobile edge network according to the first aspect of Embodiment 1 of the present disclosure;

[0019] Figure 4 is a schematic diagram of an optimization device for a mobile edge network according to the first aspect of Embodiment 2 of the present disclosure;

[0020] Figure 5 It is a schematic diagram of an optimization device for a mobile edge network according to the first aspect of Embodiment 3 of the present disclosure. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0023] Example 1

[0024] According to this embodiment, a method embodiment for optimizing a mobile edge network is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0025] The method embodiment provided in this embodiment can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computing device for realizing optimization of a mobile edge network. Figure 1 As shown, the computing device may include one or more processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, a transmission device for communication functions, and an input / output interface. The memory, the transmission device, and the input / output interface are connected to the processor via a bus. In addition, it may also include: a display, a keyboard, and a cursor control device connected to the input / output interface. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0026] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the computing device. As involved in the embodiments of the present disclosure, the data processing circuitry acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0027] The memory can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for optimizing the mobile edge network in the embodiment of the present disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the method for optimizing the mobile edge network of the above-mentioned application program is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computing device. In one example, the transmission device includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.

[0029] The display may be, for example, a touch screen liquid crystal display (LCD) that may enable a user to interact with a user interface of the computing device.

[0030] It should be noted that, in some optional embodiments, the above Figure 1 The computing device shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware elements and software elements. Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computing devices described above.

[0031] Figure 2Schematic diagram of a three-layer mobile edge network model corresponding to an IoT terminal, a drone, and a satellite according to this embodiment. Figure 2 As shown in Figure 2, the model includes: multiple IoT devices Li, multiple drones U j And satellite S. Wherein, i = 1 ~ k, j = 1 ~ n.

[0032] In addition, reference Figure 2 As shown, UAV U j Able to communicate with multiple IoT devices within the coverage area i Establish a communication connection to receive data from multiple IoT devices L i Offload the second computing task.

[0033] IoT Devices i With the only drone U j Establish a connection, thereby offloading the second computing task to the drone U j superior.

[0034] UAV j Able to communicate with multiple IoT devices within the coverage area i Establish communication connections so that multiple IoT devices L within the communication coverage area i When the second computing task offloaded is different, UAV U j The total task to be calculated is each IoT device L i The sum of the second computing tasks that are offloaded. Multiple IoT devices L within the communication coverage i When the second computing task offloaded is the same, UAV U j The total task required to be calculated is equal to the second calculation task. That is, multiple IoT devices L within the communication coverage area i When the second computing task offloaded is the same, UAV U j No need to double count.

[0035] Satellite S and multiple drones U j Communication connection, so that the satellite S can pass through the drone U j Received by IoT terminal L i Offload the third computing task.

[0036] It should be noted that the IoT devices, drones and satellites in the system can all be applied to the hardware structure described above.

[0037] In the above operating environment, according to the first aspect of this embodiment, a method for optimizing a mobile edge network is provided. The method comprises: Figure 1 The processor implementation shown in . Figure 3A schematic diagram showing the process of the method is shown in FIG. Figure 3 As shown, the method includes:

[0038] S302: Construct a three-layer mobile edge network model based on IoT devices, drones, and satellites;

[0039] S304: constructing a computing model related to the three-layer mobile edge network model, wherein the computing model determines the latency and energy consumption of the three-layer mobile edge network model based on the following parameters: a first variable between the IoT device and the drone, a bandwidth allocation ratio between the IoT device and the drone, a second variable between the IoT device and the drone, and a first position coordinate of the drone;

[0040] S306: determining a joint optimization problem according to the computing model, wherein the joint optimization problem is used to indicate a computing delay to be minimized; and

[0041] S308: Divide the joint optimization problem into different sub-problems by using a block coordinate descent method, and optimize the first variable, the bandwidth allocation ratio, the second variable, and the first position coordinates based on the sub-problems.

[0042] Specifically, refer to Figure 2 As shown, first, the processor constructs a three-layer mobile edge network model between the IoT device, the drone, and the satellite (S302). Since each drone is equipped with a mobile edge network server, each drone can provide computing services for the IoT devices within its communication coverage. Thus, the IoT device can choose to process the computing task locally, or it can choose to offload the computing task to the drone's mobile edge network server or the satellite-side cloud server for processing.

[0043] Then, multiple IoT devices L on the ground i The set is represented by L i ={L1,L2,L3,......,L k}. j =(U1,U,U3,......,U n}.

[0044] Furthermore, the processor determines the computing task I of the ith IoT device in the current time slot i , and determine the number of CPU cycles required for the i-th IoT device to calculate 1 bit and the computing task size D of the i-th IoT device i And the maximum deadline T for the i-th IoT device to complete the computing task i .

[0045] Among them, the computing task size of the i-th IoT device is D iIt can be divided into three parts: as well as in, represents the task calculated on the IoT terminal (i.e., the first calculation task), represents the task computed on the drone (i.e., the second computing task) and represents the task calculated on the satellite (i.e., the third computing task). Assuming that the division of computing tasks does not result in additional input data, the computing task of the i-th IoT device is equal to the sum of the tasks calculated on the IoT terminal, the tasks calculated on the drone, and the tasks calculated on the satellite. That is:

[0046]

[0047] It is worth noting that when the processor sets When the computation is performed on the UAV, the entire computing task is offloaded to the UAV.

[0048] Furthermore, the processor constructs a computational model related to the three-layer mobile edge network model. Specifically, first, the processor determines the association variable (i.e., the first variable) a={a i,j}. Among them, the associated variable a i,j Used to indicate whether the IoT device is connected to the drone. In the case where the IoT device is connected to the drone, define a i,j =1; when the IoT device is not connected to the drone, define a i,j = 0. Furthermore, since each IoT device can be connected to at most one drone, the formula is as follows:

[0049]

[0050] Then, the processor determines the cache decision variable (i.e., the second variable) c={c i,j}. Among them, the cache decision variable c i,j It is used to indicate whether the IoT device decides to cache the computing task to the drone. In the case where the IoT device decides to cache the computing task to the drone, c i,j =1; otherwise, c i,j =0.

[0051] Further, the processor determines the ratio of the bandwidth allocated to the IoT device to the bandwidth of the i-th IoT device to offload the computing task to the j-th drone b = {b i,j}.

[0052] In addition, the processor determines the computing tasks assigned to the IoT device. Computational tasks assigned to drones and computing tasks assigned to satellites Among them, the total computing task

[0053] Finally, the processor determines the first position coordinates of the drone Among them, H j Indicates the flight altitude of the drone. Indicates the horizontal position coordinates of the drone.

[0054] The processor determines the association variable a={a i,j}, the cache decision variable c between the i-th IoT device and the j-th drone = {c i,j}, the ratio of the bandwidth allocated to IoT devices to the bandwidth of the i-th IoT device offloading computing tasks to the j-th drone b = {b i,j} and the first position coordinates of the drone And the delay and energy consumption of the three-layer mobile edge network model are determined according to the above parameters (S304).

[0055] Then, the processor constructs a joint optimization problem (S306) based on the calculation model. That is, a formula for minimizing the calculation delay is constructed. The specific formula is as follows:

[0056]

[0057] And wherein, the constraints corresponding to the above joint optimization problem include:

[0058] C1:

[0059] C2:E i ≤E max ,

[0060] C3:

[0061] C4:

[0062] C5:

[0063] C6:

[0064] C7:a i,j ∈{0,1},c i,j ∈{0,1}

[0065] C8:

[0066] C9: m≠j

[0067] C10:H min ≤H j ≤H max

[0068] C11: b i,j ,

[0069] Among them, E max represents the maximum available energy of the i-th IoT device.

[0070] Among them, constraint C1 means that the delay of processing computing tasks cannot exceed the maximum delay; constraint C2 means that the total energy consumption of each IoT device cannot exceed the maximum available energy; constraint C3 means that the sum of the computing tasks assigned to the IoT device, the computing tasks assigned to the drone and the computing tasks assigned to the satellite is equal to the total computing tasks; constraint C4 means that the CPU cycle frequency of the drone must not exceed the maximum allowed CPU cycle frequency; constraint C5 means that each IoT device can only be associated with one drone at most; constraint C6 means that the sum of bandwidth ratios is equal to 1; constraint C7 means that there is only association or no association between IoT devices and drones, and that computing tasks are assigned to the drones. Constraint C8 indicates that the computing tasks cached by the drone must not exceed the maximum cache capacity; Constraint C9 indicates that the distance between drones should be greater than the minimum distance allowed to avoid collisions between drones; Constraint C10 indicates that the flight altitude of the drone cannot be less than the minimum altitude allowed and cannot be greater than the maximum altitude allowed; Constraint C11 indicates the bandwidth allocation ratio, the computing resources allocated to the IoT devices, the computing resources allocated to the drones, the computing resources allocated to the satellites, the computing tasks allocated to the IoT devices, the computing tasks allocated to the drones, and the computing tasks allocated to the satellites must all be greater than 0.

[0071] Finally, the processor divides the joint optimization problem into different sub-problems through the block coordinate descent method, and optimizes the associated variables, bandwidth allocation ratio, cache decision variables and the first position coordinates based on the sub-problems (S308). Specifically, 1. Given a bandwidth allocation ratio b = {b i,j} and the first position coordinates of the drone In the case of i,j} and cache decision variable c = {c i,j}; 2. Given an associated variable a = {a i,j}, cache decision variable c = {c i,j} and drone location In the case ofi,j}; 3. Given a bandwidth allocation ratio b = {b i,j}, associated variable a={a i,j} and cache decision variable c = {c i,j}, solve the optimization of drone position The content here will be described in detail later, so it will not be repeated here.

[0072] As described in the background technology, based on the drone-assisted mobile edge computing network, a three-layer mobile edge network model based on IoT devices-drones-satellites can be constructed, and the computing tasks of IoT devices can be cached to drones and offloaded to satellites for processing, thereby giving full play to the advantages of mobile edge computing networks.

[0073] In addition, in the three-layer mobile edge network model of IoT devices, drones, and satellites, minimizing mission latency is one of the most important problems to be solved. However, since there are many variables involved in the three-layer network model of IoT devices, drones, and satellites, the mission latency problem to be solved is relatively complicated.

[0074] In view of this, after the processor in the present application determines the joint optimization problem (i.e., the problem of minimizing the computational delay) according to the computational model, it divides the joint optimization problem into different sub-problems (i.e., given the bandwidth allocation ratio and the first position coordinate, solving the associated variables and the cache decision variables; given the associated variables, the cache decision variables and the first position coordinate, solving the bandwidth allocation ratio; given the associated variables, the bandwidth allocation ratio and the cache decision variables, solving the first position coordinate). Then, the processor converts each non-convex sub-problem into a convex sub-problem, and solves each sub-problem separately, so as to determine the minimized computational delay, thereby ensuring the communication quality.

[0075] This solves the technical problem in the prior art that the task delay problem to be solved is complicated due to the large number of variables involved in the three-layer mobile edge network model composed of IoT devices, drones and satellites.

[0076] Optionally, the operation of constructing a computing model related to the three-layer mobile edge network model includes: constructing a channel model between the IoT device and the drone; and constructing a computing model between the IoT device, the drone, and the satellite based on the channel model. Further optionally, the operation of constructing a channel model between the IoT device and the drone includes: determining a first position coordinate corresponding to the drone and a second position coordinate corresponding to the IoT device according to a Euclidean coordinate system model, wherein the first position coordinate includes the flight altitude of the drone and the horizontal position coordinate of the drone, and the second position coordinate is used to indicate the horizontal position coordinate of the IoT device; determining the distance between the IoT device and the drone; determining the channel coefficient between the IoT device and the drone; and determining the data transmission rate at which the IoT device offloads the computing task to the drone based on the determined distance and channel coefficient.

[0077] Specifically, first, the processor adopts a three-dimensional Euclidean coordinate system model and assumes that the flight altitude of the jth UAV is H j , horizontal position coordinates Horizontal location coordinates of IoT devices Therefore, the distance between the IoT device and the drone can be determined by the following formula:

[0078]

[0079] The channel coefficient from the i-th IoT device to the j-th UAV is modeled as

[0080]

[0081] Among them, Δ i,j represents the path loss factor between the i-th IoT device and the j-th UAV, Identify the small-scale fading between the i-th IoT device and the j-th UAV.

[0082] Furthermore, the path loss factor can be expressed by the following formula:

[0083]

[0084] Where h0 represents the reference channel gain at distance d0 = 1m, α is the path loss exponent, and d i,j Indicates the distance between the IoT device and the drone.

[0085] In addition, small-scale fading can be expressed by the following formula:

[0086]

[0087] Where M represents the Rice fading factor, represents the component that satisfies the sight distance, represents the non-line-of-sight component. And where, (0,1).

[0088] If the i-th IoT device decides to offload its computing task to the j-th UAV, the data transmission rate of the computing task can be expressed by the following formula:

[0089]

[0090] Where B represents bandwidth, P i It represents the fixed uplink transmission power of IoT devices, and N0 represents the noise power.

[0091] Optionally, the operation of constructing a computing model related to the three-layer mobile edge network model includes: dividing the total computing task of the physical network device into a first computing task, a second computing task and a third computing task, wherein the first computing task is calculated on the Internet of Things device, the second computing task is calculated on the drone, and the third computing task is calculated on the satellite; determining a first computing delay when the Internet of Things device calculates the first computing task, a second computing delay when the drone calculates the second computing task, and a third computing delay when the satellite calculates the third computing task; determining a first energy consumption when the Internet of Things device calculates the first computing task, a second energy consumption when the drone calculates the second computing task, and a third energy consumption when the satellite calculates the third computing task; determining a total delay during the computing task based on the first computing delay, the second computing delay and the third computing delay; and determining the total energy consumption during the computing task based on the first energy consumption, the second energy consumption and the third energy consumption.

[0092] Specifically, when computing tasks are processed by IoT devices, the computing delay is Indicates that energy consumption Indicates that when the computing task is processed by the drone, the computing delay is Indicates that energy consumption Indicates that when the computing task is processed on the satellite, the computing delay is Indicates that energy consumption express.

[0093] 1. When the first computing task is processed by the IoT device, the computing delay It can be calculated by the following formula:

[0094]

[0095] in, represents the first computing task of the IoT device processor, F i Indicates the total number of CPU cycles required to calculate 1 bit. Represents the computing resources allocated to IoT devices.

[0096] The energy consumption of the IoT device processing the first computing task can be expressed by the following formula:

[0097]

[0098] Where η is a constant representing the energy conversion efficiency.

[0099] 2. When the drone processes the second computing task, the computing delay can be expressed by the following formula:

[0100] Among them, R i,j Indicates the transfer rate of the computing task.

[0101] The energy consumption of the drone processing the second computing task can be expressed by the following formula:

[0102]

[0103] 3. When the satellite processes the third computing task, the computing delay can be expressed by the following formula:

[0104] Among them, R s Indicates the transmission rate between the drone and the satellite.

[0105] The energy consumption of the third computing task during the transmission from the drone to the satellite can be expressed by the following formula:

[0106]

[0107] Therefore, the total delay in processing the computing task can be expressed as follows:

[0108]

[0109] Right now,

[0110] If the computing task has been cached on the drone side, the IoT device does not need to upload the computing task. Therefore, the total delay can be expressed by the following formula:

[0111]

[0112] Thus, the total energy consumption when processing computing tasks can be expressed as:

[0113]

[0114] Right now,

[0115] Optionally, the joint optimization problem is divided into different sub-problems through the block coordinate descent method, and the first variable and the second variable are optimized based on the sub-problems, including: given a bandwidth allocation ratio and a first position coordinate; introducing a third variable, and rewriting the joint optimization problem into a first sub-problem based on the third variable; according to McCormick envelope theory, replacing the first non-convex constraint condition with a first convex constraint condition, wherein the first non-convex constraint condition is used to indicate the coupling relationship between the first variable and the second variable; and solving the first variable and the second variable according to the first convex constraint condition and the first sub-problem.

[0116] Specifically, given the bandwidth allocation ratio and the first position coordinates (ie, {b,U U}), the associated variable a i,j ∈{(0,1)} is relaxed to a continuous variable 0≤a i,j ≤1, the associated decision variable c i,j ∈{(0,1)} is relaxed to a continuous variable 0≤c i,j ≤1.

[0117] In the objective function of the optimization problem (i.e., Formula 16 above), the cache decision variable c i,j and associated variable a i,j The two variables are tightly coupled in the form of a product, which makes the problem difficult to solve. To solve this problem, a new variable (i.e., the third variable) z is introduced. i,j , i∈K, j∈N. Among them, z i,j =(1-c i,j ) i,j , and define z = {z i,j}. i,j Used to indicate the cache decision variable c i,j and associated variable a i,j The coupling relationship between . Therefore, the joint optimization problem can be rewritten as the first subproblem:

[0118]

[0119] stC1-C3, C6-C9

[0120] z i,j =(1-c i,j ) i,j , i∈K, j∈N (19)

[0121] Furthermore, according to McCormick's envelope theory, the non-convex constraint z i,j =(1-c i,j ) i,j can be replaced by its McCormick convex relaxation condition, which can be expressed as:

[0122] z i,j ≥a i,j -c i,j , i∈K, j∈N (20)

[0123] z i,j ≥0, i∈N, j∈N (21)

[0124] z i,j ≤a i,j , i∈N, j∈N (22)

[0125] z i,j ≤1-c i,j , i∈N, j∈N (23)

[0126] Among them, the above constraints indicate that max(a i,j -c i,j ,0)≤z i,j ≤min(a i,j ,1-c i,j ). In particular, due to the binary nature of the optimization variables, z i,j =(1-c i,j ) i,j can be shown to be equivalent to the above constraints.

[0127] Therefore, the first sub-problem can be further expressed as:

[0128]

[0129] stC1-C3, C6-C11

[0130] (2-20)-(2-23)

[0131] The above problems can be solved using various convex optimization toolboxes.

[0132] Optionally, the joint optimization problem is divided into different sub-problems through a block coordinate descent method, and the bandwidth allocation ratio is optimized based on the sub-problems, including: given a first variable, a second variable, and a first position coordinate; rewriting the joint optimization problem into a second sub-problem; determining a second convex constraint corresponding to the second sub-problem; and solving the bandwidth allocation ratio according to the second convex constraint and the second sub-problem.

[0133] Specifically, given the bandwidth allocation ratio {b}, the first position coordinate {U u} and associated variable {a}, the joint optimization problem can be rewritten as the second subproblem:

[0134]

[0135] st

[0136] C12: i∈K

[0137] C13: i∈K

[0138] C14:

[0139] C15:b i,j ,

[0140] C16:

[0141] C17:

[0142] Among them, C16 is composed of Converted from, C17 is According to convex optimization theory, the perspective function of a concave function is also a concave function. Obviously, It is a concave function Therefore, It is proved to be i,j is a concave function. In addition, due to is convex, so constraints C12 and C13 are convex. Combined with other linear constraints and the objective function, it can be seen that the second sub-problem above is a convex problem.

[0143] Thus, it can be solved by various convex optimization toolboxes.

[0144] Optionally, the joint optimization problem is divided into different sub-problems through the block coordinate descent method, and the position coordinates are optimized based on the sub-problems, including: given a first variable, a second variable, and a bandwidth allocation ratio; rewriting the joint optimization problem into a third sub-problem; determining a second non-convex constraint corresponding to the third sub-problem, and converting the second non-convex constraint into a third convex constraint; solving the first position coordinate according to the third convex constraint and the third sub-problem.

[0145] Specifically, given the bandwidth allocation ratio {b}, cache decision variables {c} and association variables {a}, the joint optimization problem can be rewritten as the third sub-problem:

[0146]

[0147] st

[0148] C18:

[0149] C19:

[0150] C9: m≠j

[0151] C10:H min ≤H j ≤H max

[0152] C11:

[0153] C17:

[0154] In order to solve the non-convex constraint problems C18 and C19, we first introduce the auxiliary variable {x i}, and re-express C18 as:

[0155]

[0156]

[0157] Based on the method of successive convex approximation, it can be achieved by Use the first-order Taylor approximation to derive the concave lower term on the left:

[0158]

[0159] Then, we need to solve the non-convex constraint C10. and We get the next term to the left of C10:

[0160]

[0161] By replacing the left sides of C18, C19, and C10 with their lower bounds at a given local feasible point, we obtain the following convex problem:

[0162]

[0163] st(31)

[0164] C20:

[0165] C21:

[0166] C22:

[0167] C23:

[0168] C10:H min ≤Hj ≤H max

[0169] The above can be solved by convex optimization toolbox, such as CVX. At the kth iteration, we pass and To calculate the solution Since the feasible set of 31 is a subset of the original problem 26, the upper bound solution of 26 can be found by solving the approximate problem of 26.

[0170] Simulation results show that the improved algorithm proposed in the embodiment of the present application has a significant improvement in minimizing the latency of computing tasks in the mobile edge network model compared to other benchmark algorithms.

[0171] In addition, reference Figure 1 As shown, according to the second aspect of this embodiment, a storage medium is provided, wherein the storage medium includes a stored program, wherein when the program is run, a processor executes any one of the above methods.

[0172] Thus, according to this embodiment, after the processor in this application determines the joint optimization problem (i.e., the problem of minimizing the computational delay) according to the computational model, it divides the joint optimization problem into different sub-problems (i.e., given the bandwidth allocation ratio and the first position coordinate, solving the associated variables and the cache decision variables; given the associated variables, the cache decision variables and the first position coordinate, solving the bandwidth allocation ratio; given the associated variables, the bandwidth allocation ratio and the cache decision variables, solving the first position coordinate). Then, the processor converts each non-convex sub-problem into a convex sub-problem, and solves each sub-problem respectively, so as to determine the minimized computational delay, thereby ensuring the communication quality.

[0173] This solves the technical problem in the prior art that the task delay problem to be solved is complicated due to the large number of variables involved in the three-layer mobile edge network model composed of IoT devices, drones and satellites.

[0174] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0175] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0176] Example 2

[0177] Figure 4 The optimization device 400 for a mobile edge network according to the first aspect of this embodiment is shown, and the device 400 corresponds to the method according to the first aspect of embodiment 1. Figure 4 As shown, the device 400 includes: a mobile edge network model construction module 410, which is used to construct a three-layer mobile edge network model based on an Internet of Things device, a drone and a satellite; a computing model construction module 420, which is used to construct a computing model related to the three-layer mobile edge network model, wherein the computing model determines the delay and energy consumption of the three-layer mobile edge network model based on the following parameters: a first variable between the Internet of Things device and the drone, a bandwidth allocation ratio between the Internet of Things device and the drone, a second variable between the Internet of Things device and the drone, and a first position coordinate of the drone; a joint optimization problem determination module 430, which is used to determine a joint optimization problem according to the computing model, wherein the joint optimization problem is used to indicate the computing delay to be minimized; and an optimization module 440, which is used to divide the joint optimization problem into different sub-problems through a block coordinate descent method, and optimize the first variable, the bandwidth allocation ratio, the second variable and the first position coordinate based on the sub-problems.

[0178] Optionally, the computing model construction module 420 includes: a channel model construction module, used to construct a channel model between the IoT device and the drone; and a computing model construction sub-module, used to construct a computing model between the IoT device, the drone and the satellite based on the channel model.

[0179] Optionally, the channel model building module includes: a position coordinate determination module, used to determine a first position coordinate corresponding to the drone and a second position coordinate corresponding to the Internet of Things device according to a Euclidean coordinate system model, wherein the first position coordinate includes the flight altitude of the drone and the horizontal position coordinate of the drone, and the second position coordinate is used to indicate the horizontal position coordinate of the Internet of Things device; a distance determination module, used to determine the distance between the Internet of Things device and the drone; a channel coefficient determination module, used to determine the channel coefficient between the Internet of Things device and the drone; and a data transmission rate determination module, used to determine the data transmission rate at which the Internet of Things device offloads computing tasks to the drone based on the determined distance and channel coefficient.

[0180] Optionally, the computing model building module 420 includes: a computing task division module, which is used to divide the total computing task of the physical network device into a first computing task, a second computing task and a third computing task, wherein the first computing task is calculated on the Internet of Things device, the second computing task is calculated on the drone, and the third computing task is calculated on the satellite; a computing delay determination module, which is used to determine the first computing delay when the Internet of Things device calculates the first computing task, the second computing delay when the drone calculates the second computing task, and the third computing delay when the satellite calculates the third computing task; an energy consumption determination module, which is used to determine the first energy consumption when the Internet of Things device calculates the first computing task, the second energy consumption when the drone calculates the second computing task, and the third energy consumption when the satellite calculates the third computing task; a total delay determination module, which is used to determine the total delay during the computing task based on the first computing delay, the second computing delay and the third computing delay; and a total energy consumption determination module, which is used to determine the total energy consumption during the computing task based on the first energy consumption, the second energy consumption and the third energy consumption.

[0181] Optionally, the optimization module 440 includes: a first given module, used to give a bandwidth allocation ratio and a first position coordinate; a first rewriting module, used to introduce a third variable and rewrite the joint optimization problem into a first sub-problem based on the third variable; a first replacement module, used to replace the first non-convex constraint condition with a first convex constraint condition according to McCormick envelope theory, wherein the first non-convex constraint condition is used to indicate the coupling relationship between the first variable and the second variable; and a first solving module, used to solve the first variable and the second variable according to the first convex constraint condition and the first sub-problem.

[0182] Optionally, the optimization module 440 includes: a second given module, used to give the first variable, the second variable and the first position coordinate; a second rewriting module, used to rewrite the joint optimization problem into a second sub-problem; a first determination module, used to determine the second convex constraint corresponding to the second sub-problem; and a second solving module, used to solve the bandwidth allocation ratio according to the second convex constraint and the second sub-problem.

[0183] Optionally, the optimization module 440 includes: a third given module, used to give the first variable, the second variable and the bandwidth allocation ratio; a third rewriting module, used to rewrite the joint optimization problem into a third sub-problem; a second determination module, used to determine the second non-convex constraint corresponding to the third sub-problem, and convert the second non-convex constraint into a third convex constraint; a third solving module, used to solve the position coordinates according to the third convex constraint and the third sub-problem.

[0184] Therefore, according to this embodiment, it can be known that after the processor in this application determines the joint optimization problem (i.e., the problem of minimizing the computational delay) according to the computational model, it divides the joint optimization problem into different sub-problems (i.e., given the bandwidth allocation ratio and the first position coordinate, solving the associated variables and the cache decision variables; given the associated variables, the cache decision variables and the first position coordinate, solving the bandwidth allocation ratio; given the associated variables, the bandwidth allocation ratio and the cache decision variables, solving the first position coordinate). Then, the processor converts each non-convex sub-problem into a convex sub-problem, and solves each sub-problem respectively, so as to determine the minimized computational delay, thereby ensuring the communication quality.

[0185] Example 3

[0186] Figure 5 The mobile edge network optimization device 500 according to the first aspect of this embodiment is shown, and the device 500 corresponds to the method according to the first aspect of embodiment 1. Figure 5 As shown, the device 500 includes: a processor 510; and a memory 520, which is connected to the processor 510 and is used to provide the processor 510 with instructions for processing the following processing steps: constructing a three-layer mobile edge network model based on an IoT device, a drone and a satellite; constructing a computing model related to the three-layer mobile edge network model, wherein the computing model determines the delay and energy consumption of the three-layer mobile edge network model based on the following parameters: a first variable between the IoT device and the drone, a bandwidth allocation ratio between the IoT device and the drone, a second variable between the IoT device and the drone, and a first position coordinate of the drone; determining a joint optimization problem according to the computing model, wherein the joint optimization problem is used to indicate the computing delay to be minimized; and dividing the joint optimization problem into different sub-problems through the block coordinate descent method, and optimizing the first variable, the bandwidth allocation ratio, the second variable and the first position coordinate based on the sub-problems.

[0187] Therefore, according to this embodiment, it can be known that after the processor in this application determines the joint optimization problem (i.e., the problem of minimizing the computational delay) according to the computational model, it divides the joint optimization problem into different sub-problems (i.e., given the bandwidth allocation ratio and the first position coordinate, solving the associated variables and the cache decision variables; given the associated variables, the cache decision variables and the first position coordinate, solving the bandwidth allocation ratio; given the associated variables, the bandwidth allocation ratio and the cache decision variables, solving the first position coordinate). Then, the processor converts each non-convex sub-problem into a convex sub-problem, and solves each sub-problem respectively, so as to determine the minimized computational delay, thereby ensuring the communication quality.

[0188] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0189] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0190] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0191] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0192] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0193] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0194] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing a mobile edge network, characterized in that: include: Build a three-layer mobile edge network model based on IoT devices, drones, and satellites; Constructing a computing model related to the three-layer mobile edge network model, wherein the computing model determines the latency and energy consumption of the three-layer mobile edge network model based on the following parameters: a first variable between the IoT device and the drone, a bandwidth allocation ratio between the IoT device and the drone, a second variable between the IoT device and the drone, and a first position coordinate of the drone; Determining a joint optimization problem according to the computing model, wherein the joint optimization problem is used to indicate a computing delay to be minimized; as well as The joint optimization problem is divided into different sub-problems by block coordinate descent method, and the first variable, the bandwidth allocation ratio, the second variable and the first position coordinate are optimized respectively based on the sub-problems.

2. The method according to claim 1, characterized in that: The operations of constructing a computing model related to the three-layer mobile edge network model include: Constructing a channel model between the IoT device and the drone; and A computing model between the IoT device, the UAV and the satellite is constructed based on the channel model.

3. The method according to claim 1, characterized in that The operation of constructing a channel model between the IoT device and the drone includes: Determine, according to the Euclidean coordinate system model, a first position coordinate corresponding to the drone and a second position coordinate corresponding to the IoT device, wherein the first position coordinate includes a flight altitude of the drone and a horizontal position coordinate of the drone, and the second position coordinate is used to indicate a horizontal position coordinate of the IoT device; Determining a distance between the IoT device and the drone; Determining a channel coefficient between the IoT device and the drone; and According to the determined distance and the channel coefficient, a data transmission rate at which the IoT device offloads computing tasks to the drone is determined.

4. The method according to claim 1, characterized in that The operations of constructing a computing model related to the three-layer mobile edge network model include: Dividing the total computing task of the physical network device into a first computing task, a second computing task, and a third computing task, wherein the first computing task is calculated on the IoT device, the second computing task is calculated on the drone, and the third computing task is calculated on the satellite; Determine a first computing delay when the IoT device calculates the first computing task, a second computing delay when the drone calculates the second computing task, and a third computing delay when the satellite calculates the third computing task; Determine a first energy consumption when the IoT device calculates the first computing task, a second energy consumption when the drone calculates the second computing task, and a third energy consumption when the satellite calculates the third computing task; Determining a total delay in computing a task according to the first computing delay, the second computing delay, and the third computing delay; and The total energy consumption during the computing task is determined according to the first energy consumption, the second energy consumption and the third energy consumption.

5. The method according to claim 1, characterized in that The joint optimization problem is divided into different sub-problems by using a block coordinate descent method, and the first variable and the second variable are optimized based on the sub-problems, including: Given the bandwidth allocation ratio and the first location coordinates; Introducing a third variable, and rewriting the joint optimization problem into a first sub-problem based on the third variable; According to McCormick envelope theory, replacing the first non-convex constraint condition with a first convex constraint condition, wherein the first non-convex constraint condition is used to indicate a coupling relationship between the first variable and the second variable; and The first variable and the second variable are solved according to the first convex constraint and the first subproblem.

6. The method according to claim 1, characterized in that The joint optimization problem is divided into different sub-problems by using a block coordinate descent method, and the bandwidth allocation ratio is optimized based on the sub-problems, including: Given the first variable, the second variable and the first position coordinates; Rewrite the joint optimization problem into a second sub-problem; determining a second convex constraint corresponding to the second subproblem; and The bandwidth allocation ratio is solved according to the second convex constraint and the second sub-problem.

7. The method according to claim 1, characterized in that The joint optimization problem is divided into different sub-problems by using a block coordinate descent method, and the operation of optimizing the first position coordinates based on the sub-problems includes: Given the first variable, the second variable and the bandwidth allocation ratio; Rewrite the joint optimization problem into a third sub-problem; Determine a second non-convex constraint condition corresponding to the third sub-problem, and transform the second non-convex constraint condition into a third convex constraint condition; The position coordinates are solved according to the third convex constraint and the third subproblem.

8. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the processor executes the method according to any one of claims 1 to 7.

9. A mobile edge network optimization device, characterized in that: include: The mobile edge network model building module is used to build a three-layer mobile edge network model based on IoT devices, drones, and satellites; A computing model building module, used to build a computing model related to the three-layer mobile edge network model, wherein the computing model determines the latency and energy consumption of the three-layer mobile edge network model based on the following parameters: a first variable between the IoT device and the drone, a bandwidth allocation ratio between the IoT device and the drone, a second variable between the IoT device and the drone, and a first position coordinate of the drone; A joint optimization problem determination module, used to determine a joint optimization problem according to the calculation model, wherein the joint optimization problem is used to indicate a calculation delay to be minimized; as well as The optimization module is used to divide the joint optimization problem into different sub-problems through a block coordinate descent method, and optimize the first variable, the bandwidth allocation ratio, the second variable and the first position coordinates based on the sub-problems.

10. A mobile edge network optimization device, characterized in that: include: processor; as well as A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: Build a three-layer mobile edge network model based on IoT devices, drones, and satellites; Constructing a computing model related to the three-layer mobile edge network model, wherein the computing model determines the latency and energy consumption of the three-layer mobile edge network model based on the following parameters: a first variable between the IoT device and the drone, a bandwidth allocation ratio between the IoT device and the drone, a second variable between the IoT device and the drone, and a first position coordinate of the drone; Determining a joint optimization problem according to the computing model, wherein the joint optimization problem is used to indicate a computing delay to be minimized; as well as The joint optimization problem is divided into different sub-problems by block coordinate descent method, and the first variable, the bandwidth allocation ratio, the second variable and the first position coordinate are optimized respectively based on the sub-problems.

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