CN111327355B - Unmanned aerial vehicle edge perception calculation and joint transmission method, device, medium and equipment - Google Patents
Unmanned aerial vehicle edge perception calculation and joint transmission method, device, medium and equipment Download PDFInfo
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- H04B7/00—Radio transmission systems, i.e. using radiation field
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Abstract
The embodiment of the application relates to the technical field of unmanned aerial vehicles, in particular to a method, a device, a medium and equipment for edge perception calculation and joint transmission of an unmanned aerial vehicle. The method for unmanned aerial vehicle edge perception computation and joint transmission comprises the following steps: the unmanned aerial vehicle calculates sensing time and transmission time required by executing each task according to an iterative algorithm minimizing the expected value of the AOI of each task; the unmanned aerial vehicle calculates sensing time and transmission time corresponding to each flight speed v according to the iterative algorithm; the unmanned aerial vehicle obtains the total time T for executing all tasks, the total energy E of the unmanned aerial vehicle, and the sensing time and transmission time data corresponding to each flight speed v, calculates the sequence of each task according to a knapsack problem algorithm, and determines the total flight track. The method optimizes the sensing and transmission time balance of the unmanned aerial vehicle, designs the sequence of a plurality of tasks of the unmanned aerial vehicle, and achieves the effect of minimizing information delay.
Description
Technical Field
The embodiment of the application relates to the technical field of unmanned aerial vehicles, in particular to a method, a device, a medium and equipment for edge perception calculation and joint transmission of an unmanned aerial vehicle.
Background
The unmanned aerial vehicle is a powerful thing networking perception equipment, can wide application in each field such as industry, agriculture, trade, military affairs.
When some information perception tasks are executed, under the condition that the data accuracy is guaranteed, the latest information data needs to be obtained so as to guarantee timeliness. Generally, an unmanned aerial vehicle is used for repeatedly sensing and transmitting a plurality of tasks so as to update sensing data. The unmanned aerial vehicle senses data near the task point and then transmits the data back through the cellular network. In order to ensure the correctness of perception data, an unmanned aerial vehicle generally needs to perceive a large amount of data at a position close to a perception target; in order to ensure the reliability of transmission, the unmanned aerial vehicle generally needs to perform reliable data transmission at a position close to the base station. Therefore, in a certain time, the unmanned aerial vehicle perceives that data quality and transmission quality are balanced, so that the information has optimal timeliness.
In current cellular networks, the perception and transmission of a task by a drone are generally considered as a whole, and a greedy algorithm is generally adopted to optimize a certain index, such as transmission rate or transmission delay, given a perception and transmission task. This approach does not result in optimal allocation of sensing and transmission at a given time, and does not guarantee real-time transmission of effective sensing data.
Disclosure of Invention
In order to solve the technical problem, an embodiment of the application provides a method, a device, a storage medium, and an apparatus for edge-aware computing and joint transmission of an unmanned aerial vehicle.
A first aspect of an embodiment of the present application provides a method for edge-aware computation and joint transmission of an unmanned aerial vehicle, which is applied to an unmanned aerial vehicle to execute multiple tasks, where the method includes:
the unmanned aerial vehicle calculates sensing time and transmission time required by executing each task according to an iterative algorithm minimizing the expected value of the AOI of each task;
the unmanned aerial vehicle calculates sensing time and transmission time required for executing each task at each flight speed v according to the iterative algorithm;
the unmanned aerial vehicle obtains total time T for executing all tasks, total energy E of the unmanned aerial vehicle, each flight speed v for executing each task and sensing time and transmission time data corresponding to each flight speed v, the sequence of each task is calculated according to a knapsack problem algorithm of minimizing AOI of a plurality of tasks, corresponding sensing time and transmission time, and a total flight track is determined according to the sequence of each task.
Optionally, the calculating, by the drone according to an iterative algorithm that minimizes an expected value of the AOI of the task, sensing time and transmission time required to execute each task includes:
during the optimization of perception time, setting a variable during transmission as a fixed value to obtain a value of perception time;
when optimizing the transmission time, setting a variable during sensing as a fixed value, wherein the fixed value adopts a value of the sensing time obtained in the last step to obtain a value of the transmission time;
iterating the obtained values until the values converge to maximize the average AOI reduction amount of each time slot;
the sensing time value and the transmission time value at the convergence time are used as the sensing time and the transmission time required for executing the task.
Optionally, the calculating, according to the knapsack problem algorithm that minimizes AOI of a plurality of tasks, a sequence of each task, corresponding sensing time and transmission time, and determining a total flight trajectory include:
defining U as an action for completing the task i at the time t with the speed v, defining e as energy consumption of the action, G as benefit of the action, defining U as a set of all actions, and defining G as maximum benefit obtained by each time slot on average, namely maximum value of reduction of AOI of each time slot;
in the case of certain U and E, the maximum gain G obtained per slot on average is the maximum of two cases:
(1) taking an action U consuming a corresponding energy consumption E, the sum of the gain g of the action and the maximum gain obtained in the case of U-U and E-E;
(2) maximum benefit obtained without action U, in the case of U-U and E;
and performing recursive optimization on the U and the E to obtain an optimal task sequence, obtaining the corresponding perception time and transmission time of each task, and determining the total flight trajectory according to the respective sequence of the plurality of tasks.
Optionally, the method for determining the flight trajectory in each task includes:
the unmanned aerial vehicle directly flies to the sensing point to acquire data, the data acquisition is not performed in the flying process, and when the sensing point is reached, the unmanned aerial vehicle hovers at the sensing point to acquire the data;
the unmanned aerial vehicle flies to a transmission point along the direction of improving the transmission signal-to-noise ratio to be the fastest to transmit data, data transmission is carried out when the transmission signal-to-noise ratio is larger than a threshold value, and after the transmission point is reached, if data transmission is not finished, the unmanned aerial vehicle hovers at the transmission point to continue transmission until the data transmission is finished.
A second aspect of the embodiments of the present application provides an apparatus for edge-aware computing and joint transmission of an unmanned aerial vehicle, which is applied to an unmanned aerial vehicle to perform multiple tasks, the apparatus including:
the optimization module is configured to calculate sensing time and transmission time required by the unmanned aerial vehicle to execute each task according to an iterative algorithm which minimizes the expected value of the AOI of each task;
the calculation module is configured to calculate sensing time and transmission time required for executing each task at each flight speed v by the unmanned aerial vehicle according to the iterative algorithm;
the sequencing module is configured to acquire the total time T for executing all tasks, the total energy E of the unmanned aerial vehicle, each flight speed v for executing each task and sensing time and transmission time data corresponding to each flight speed v, calculate the sequencing of each task according to a knapsack problem algorithm minimizing AOI of the whole task, and determine a total flight trajectory according to the respective sequencing of a plurality of tasks.
Optionally, the optimization module configured to calculate, by the drone according to an iterative algorithm that minimizes the expected value of the AOI of the task, the perception time and the transmission time required to execute each task includes:
during the optimization of perception time, setting a variable during transmission as a fixed value to obtain a value of perception time;
when optimizing the transmission time, setting a variable during sensing as a fixed value, wherein the fixed value adopts a value of the sensing time obtained in the last step to obtain a value of the transmission time;
iterating the obtained values until the values converge to maximize the average AOI reduction amount of each time slot;
the sensing time value and the transmission time value at the convergence time are used as the sensing time and the transmission time required for executing the task.
Optionally, the sorting module further includes:
defining U as an action for completing the task i at the time t with the speed v, defining e as energy consumption of the action, G as benefit of the action, defining U as a set of all actions, and defining G as maximum benefit obtained by each time slot on average, namely maximum value of reduction of AOI of each time slot;
in the case of certain U and E, the maximum gain G obtained per slot on average is the maximum of two cases:
(1) taking an action U consuming a corresponding energy consumption E, the sum of the gain g of the action and the maximum gain obtained in the case of U-U and E-E;
(2) maximum benefit obtained without action U, in the case of U-U and E;
and performing recursive optimization on the U and the E to obtain an optimal task sequence, obtaining the corresponding perception time and transmission time of each task, and determining the total flight trajectory according to the respective sequence of the plurality of tasks.
Optionally, the sequencing module further includes a trajectory determination sub-module for determining a flight trajectory in each task;
the track determining submodule is configured to enable the unmanned aerial vehicle to directly fly to the sensing point for data acquisition, the data acquisition is not carried out in the process of flying, and after the sensing point is reached, the unmanned aerial vehicle hovers at the sensing point for data acquisition;
the unmanned aerial vehicle flies to a transmission point along the direction of improving the transmission signal-to-noise ratio to be the fastest to transmit data, data transmission is carried out when the transmission signal-to-noise ratio is larger than a threshold value, and after the transmission point is reached, if data transmission is not finished, the unmanned aerial vehicle hovers at the transmission point to continue transmission until the data transmission is finished.
A third aspect of embodiments of the present application provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps in the method according to the first aspect of the present application.
A fourth aspect of the embodiments of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method according to the first aspect of the present application.
By adopting the method for edge perception calculation and joint transmission of the unmanned aerial vehicle, data perception and data transmission are jointly considered, the data perception and data transmission quality are guaranteed, and meanwhile, information delay is reduced, and the method has the advantages that the optimization target is as follows: the expected value of the AOI contains information of perception quality and transmission quality, so that the situation that the unmanned aerial vehicle blindly reduces time delay and ignores the perception and transmission quality is avoided, and the perception and transmission time of the unmanned aerial vehicle is balanced;
the method considers the balancing problem of sensing time and transmission time in given time, optimizes the track of the unmanned aerial vehicle under the balancing condition of sensing time and transmission time, and designs the execution sequence of a plurality of tasks of the unmanned aerial vehicle, thereby achieving the effect of minimizing information delay.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments of the present application will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a schematic view of an application scenario of a method for edge-aware computing and joint transmission of an unmanned aerial vehicle according to an embodiment of the present application;
fig. 2 is a flowchart of a method for edge-aware computing and joint transmission of an unmanned aerial vehicle according to an embodiment of the present application;
fig. 3 is a partial flowchart of a method for edge-aware computing and joint transmission of a drone according to another embodiment of the present application;
fig. 4 is a schematic diagram of an apparatus for edge-aware computing and joint transmission of an unmanned aerial vehicle according to an embodiment of the present application;
FIG. 5 is a schematic diagram illustrating AOI of a perception task over time in one embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a schematic view of an application scenario of a method for edge-aware computing and joint transmission of an unmanned aerial vehicle according to an embodiment of the present application, where the method includes a base station and multiple task aware points, where the unmanned aerial vehicle acquires information from a flight direction aware point each time the task is executed, and then flies to a position near the base station to perform information transmission, where it is assumed that the positions where the unmanned aerial vehicle flies to the base station to perform transmission each time are the same, t1 is a time when sensing of the unmanned aerial vehicle starts, and t2 is a time when the unmanned aerial vehicle starts to transmit after the sensing ends; t3, t4 are the sensing start time and the transmission start time of the next task, respectively; t5 is the perceived start time of the next task, and so on.
Referring to fig. 2, fig. 2 is a flowchart of a method for edge-aware computing and joint transmission of an unmanned aerial vehicle according to an embodiment of the present application. As shown in fig. 2, the method is applied to a drone for executing a plurality of tasks, and comprises the following steps:
in step S11, the drone calculates the perception time and transmission time required to execute each task according to an iterative algorithm that minimizes the AOI of each task;
in this embodiment, AOI refers to a time period from the last valid information generation to the current time, and is a physical quantity for measuring the timeliness of the perception data. The expected value of the AOI for each task is a measure of the timeliness of the perceptual data within that task, and figure 5 shows the form of the AOI of a perceptual task over time,
the moment when the unmanned aerial vehicle starts to collect information,
and finishing the information transmission moment for the unmanned aerial vehicle. AOI is linear in time when information is not completely transmittedAnd (3) a linear increase, wherein the slope between the AOI and the time is 1. When the information transmission is completed, the AOI will drop, the extent of which is related to the probability of successful transmission. If the success rate of one sensing and transmission is p, then
At the moment, in case of a successful transmission,
is composed of
I.e. the probability that the AOI has p becomes
With a probability of (1-p) sensing and transmission failure, AOI continues to grow linearly with time. I.e. the expected value of AOI, in
At the moment of time of
Since the value of the AOI is related to the flight speed of the drone, that is, the faster the flight speed of the drone is, the less time it takes to execute the task is, and the smaller the value of the AOI is, when the iterative algorithm is used to calculate the sensing time and the transmission time required for executing each task, the flight speed of the drone is a certain value, and when the iterative sensing time and the iterative transmission time converge, the time allocation of sensing and transmission at the above speed is obtained.
In step S12, the drone calculates, according to the iterative algorithm, the sensing time and the transmission time required for each task to be executed at each flying speed v;
in this embodiment, when the unmanned aerial vehicle executes a task, energy is consumed, that is, the electric quantity of the unmanned aerial vehicle, the faster the flight speed is, the larger the power of the unmanned aerial vehicle is, the more the consumed energy is, the different AOI values corresponding to different speeds are different, the consumed energy is also different, and the less the consumed energy is, the better the AOI of the task satisfies the requirements. And under different flight speeds, the sensing time and the transmission time are distributed differently, so that the unmanned aerial vehicle calculates the sensing time and the transmission time required under different flight speeds according to the iterative algorithm in the step S11. In actual operation, it is impossible to calculate sensing time and transmission time corresponding to all flight speeds, a flight speed interval can be determined according to past experience, a value of the flight speed corresponding to the interval is taken, and then the sensing time and the transmission time corresponding to the flight speed of each value are calculated, wherein the value of the flight speed needs to ensure that AOI (namely timeliness of information) in a task meets corresponding requirements when the task is executed according to the speed.
In step S13, the drone obtains the total time T for executing all tasks, the total energy E of the drone, the flight speeds v for executing each task, and the sensing time and transmission time data corresponding to the flight speeds v, calculates the sequence of each task according to the knapsack problem algorithm that minimizes the AOI of the plurality of tasks, and determines the total flight trajectory according to the respective sequences of the plurality of tasks.
In this embodiment, the unmanned aerial vehicle needs to calculate the optimal total flight trajectory when executing a plurality of tasks, and when executing each task, after determining the sensing time and the transmission time, the flight trajectory in the task can be determined, and when determining the execution sequence of each task, the optimal total flight trajectory when the unmanned aerial vehicle executes a plurality of tasks can be determined. In practical application, the total time length T of the unmanned aerial vehicle executing a plurality of tasks is determined, the total energy E of the unmanned aerial vehicle itself, that is, the total electric quantity of the unmanned aerial vehicle itself before executing the tasks is also determined, and under the condition of the determined total time length T and the total energy E, the unmanned aerial vehicle needs to determine an optimal flight trajectory to ensure that the timeliness of information is the best (that is, the AOIs of the plurality of tasks are the minimum). The Knapsack problem (Knapack problem) is a NP complete problem optimized by combination, the algorithm of the Knapsack problem can continuously obtain the optimized AOI in a plurality of tasks in a recursive mode, and when the recursive algorithm is completed, the sequence of each task, the flight speed of the executed task, and the sensing time and the transmission time when the task is executed can be determined.
Fig. 3 is a partial flow diagram illustrating a method of drone edge aware computing and joint transmission according to another example embodiment; referring to fig. 3, the method includes the steps of:
on the basis of the above embodiment, in another embodiment of the present application, the calculating, by the drone, the sensing time and the transmission time required for executing each task according to an iterative algorithm that minimizes the AOI of each task includes:
in step S111, when optimizing the sensing time, setting the variable for transmission as a fixed value to obtain a sensing time value;
in this embodiment, there are three variables, namely, AOI, sensing time and transmission time, when performing the calculation, and under the goal of minimizing AOI, one of the variables needs to be determined, so that a convex function relationship is formed between the value of AOI and the other variable, and when obtaining the extreme value of AOI, the value of the corresponding variable is determined.
In step S112, in the optimization of the transmission time, setting the variable for sensing as a fixed value, and obtaining a transmission time value by using the sensing time value obtained in the previous step as the fixed value of the variable for sensing;
in this embodiment, the same principle as that of step S111 is used, the variable for sensing is set to be a fixed value, so that the AOI value and the other variable have a convex functional relationship during transmission, the corresponding value during transmission is determined when the extreme value of the AOI is obtained, and the value during sensing obtained in step S111 is substituted into this step, so that the two steps are linked and mutually influenced during sensing and transmission, so as to obtain the target value.
In step S113, the obtained sensing time value and transmission time value are iterated until the sensing time value and transmission time value converge to maximize the average reduction amount of AOI per slot; the sensing time value and the transmission time value at the convergence time are used as the sensing time and the transmission time required for executing the task.
In this embodiment, step S111 and step S112 are repeated continuously, and the sensing duration value and the transmission duration value are iterated continuously, and both values affect each other, until the sensing duration value and the transmission duration value converge, the average reduction amount of AOI per time slot is maximized, that is, the AOI value of the task is minimized, and the sensing duration value and the transmission duration value at this time are the target values.
The above steps are described below by taking as an example a scenario in which one drone repeatedly senses and transmits N tasks in an urban road with a single base station.
For each task, the unmanned aerial vehicle performs joint optimization of sensing and transmission according to iteration.
Recording the flight time in the ith task as the sensing flight time
The total time of flight and information collection is taken as the sensing time TsThe time when the information transmission is completed is recorded as the transmission time Tt。
When perception optimization is carried out, T is used for transmission by unmanned aerial vehicletSet to a fixed value, and then sense flight time
And sensing total time of use TsOptimizing to maximize average AOI per slot reduction
The problem can be written as follows:
t0indicating that when a unit of unmanned aerial vehicle perception is performed,
representing the sensing execution times of the unmanned aerial vehicle; an (t) denotes the AOI of drone n at time t.
Firstly, it is obtained by mathematical analysis
And TsCan then prove the relationship of
For TsMonotonically increasing first and then monotonically decreasing, so TsAnd
the optimal solution of (a) can be found quickly in a form of traversal.
When transmission optimization is performed, the unmanned aerial vehicle flies for perception
And time for sensing TsSet to a constant value and then time for transmission TtOptimization is carried out, and according to the formula, the flight time is sensed
And time for sensing TsAll are constant, time for transmission TtReduction from AOI per time slot
In a convex functional relationship by maximizing the average AOI reduction per time slot
That is, the corresponding transmission time T can be obtainedt。
In another embodiment, after the average reduction amount of AOI of each time slot at different speeds of each task and the corresponding energy consumption of the unmanned aerial vehicle are obtained, the unmanned aerial vehicle selects and sequences the completed tasks based on a knapsack problem algorithm; the step of calculating the sequence of each task according to the knapsack problem algorithm of minimizing the AOI of a plurality of tasks, corresponding sensing time consumption and transmission time consumption, and determining the total flight track comprises the following steps:
defining U as an action for completing the task i at the time t with the speed v, defining e as energy consumption of the action, G as benefit of the action, defining U as a set of all actions, and defining G as maximum benefit obtained by each time slot on average, namely maximum value of reduction of AOI of each time slot;
in the case of certain U and E, the maximum gain G obtained per slot on average is the maximum of two cases:
(1) taking an action U consuming a corresponding energy consumption E, the sum of the gain g of the action and the maximum gain obtained in the case of U-U and E-E;
(2) maximum benefit obtained without action U, in the case of U-U and E;
and performing recursive optimization on the U and the E to obtain an optimal task sequence, obtaining the corresponding perception time and transmission time of each task, and determining the total flight trajectory according to the respective sequence of the plurality of tasks.
U-U is the set of all actions remaining after the removal of action U, and E-E is the sum of the energy remaining after the consumption of energy E in the total energy E.
In a specific embodiment, we define
An action for completing the acquisition sensing and transmission of task i at the speed of v at the time of t is defined
The energy consumption for this action is such that,
the benefit for this action, i.e. the average per slot AOI reduction in this action;
definition of
Is a set of all actions;
G(A,Ei) Meaning when all actions are taken from set A and the drone energy remains as EiAverage AOI per time slot decrease
Is measured.
The objective is to obtain the value of G (U, E), which can be obtained by the following recursive relationship. For the
It satisfies the following recursive relation:
i.e. for collections
The optimal solution which can be reached by all the actions is that the actions are adopted
And then, obtaining an optimal solution by using the residual action set and energy:
or take no action
From the collection
The optimal solution obtained in
Through the above algorithm, for
And recursion is continuously carried out, and finally an optimal solution is obtained, so that the optimal task sequence, the flight speed of the unmanned aerial vehicle for executing each task, the corresponding sensing time and the corresponding transmission time of each task are determined, and then the total flight trajectory is determined according to the respective sequence of the tasks.
Based on the same inventive concept, an embodiment of the application provides a device for edge perception calculation and joint transmission of an unmanned aerial vehicle. Referring to fig. 4, fig. 4 is a schematic diagram of an apparatus for edge-aware computing and joint transmission of a drone according to an embodiment of the present application. As shown in fig. 4, the device is applied to a drone for executing a plurality of tasks, and comprises:
the optimization module 1 is configured to calculate the sensing time and the transmission time required by the unmanned aerial vehicle to execute each task according to an iterative algorithm which minimizes the AOI of each task;
the calculation module 2 is configured to calculate the sensing time and the transmission time required for executing each task at each flight speed v by the unmanned aerial vehicle according to the iterative algorithm;
the sequencing module 3 is configured to acquire the total time T for executing all tasks, the total energy E of the unmanned aerial vehicle, the flight speeds v for executing each task, and sensing time and transmission time data corresponding to the flight speeds v, calculate the sequencing for each task according to a knapsack problem algorithm minimizing the AOI of the whole task, and determine the total flight trajectory according to the respective sequencing of the tasks.
In another embodiment, the optimization module 1 configured for the drone to calculate the perceived time and the transmission time required to execute each task according to an iterative algorithm that minimizes the expectation value of the AOI of the tasks, comprises:
during the optimization of perception time, setting a variable during transmission as a fixed value to obtain a value of perception time;
when optimizing the transmission time, setting a variable for sensing as a fixed value, wherein the fixed value of the variable for sensing adopts a sensing time value obtained in the last step to obtain a transmission time value;
iterating the obtained values until the values converge to maximize the average AOI reduction amount of each time slot;
the sensing time value and the transmission time value at the convergence time are used as the sensing time and the transmission time required for executing the task.
In another embodiment, the sorting module 3 further comprises:
defining U as an action for completing the task i at the time t with the speed v, defining e as energy consumption of the action, G as benefit of the action, defining U as a set of all actions, and defining G as maximum benefit obtained by each time slot on average, namely maximum value of reduction of AOI of each time slot;
in the case of certain U and E, the maximum gain G obtained per slot on average is the maximum of two cases:
(1) taking an action U consuming a corresponding energy consumption E, the sum of the gain g of the action and the maximum gain obtained in the case of U-U and E-E;
(2) maximum benefit obtained without action U, in the case of U-U and E;
and performing recursive optimization on the U and the E to obtain an optimal task sequence, obtaining the corresponding perception time and transmission time of each task, and determining the total flight trajectory according to the respective sequence of the plurality of tasks.
In another embodiment, the sequencing module 3 further comprises a trajectory determination sub-module 31 for determining the flight trajectory within each task;
the track determining submodule 31 is configured to acquire data from the sensing point by the direct flying of the unmanned aerial vehicle, acquire no data during the flying process, and when the sensing point is reached, the unmanned aerial vehicle hovers at the sensing point to acquire data;
the unmanned aerial vehicle flies to a transmission point along the direction of improving the transmission signal-to-noise ratio to be the fastest to transmit data, data transmission is carried out when the transmission signal-to-noise ratio is larger than a threshold value, and after the transmission point is reached, if data transmission is not finished, the unmanned aerial vehicle hovers at the transmission point to continue transmission until the data transmission is finished.
Based on the same inventive concept, another embodiment of the present application provides a readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the method for edge-aware computing and joint transmission for drones according to any of the embodiments of the present application.
Based on the same inventive concept, another embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the method for edge-aware computing and joint transmission of an unmanned aerial vehicle according to any of the above embodiments of the present application is implemented.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one of skill in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The method, the device, the storage medium and the equipment for unmanned aerial vehicle edge perception computation and joint transmission provided by the application are introduced in detail, a specific example is applied in the text to explain the principle and the implementation mode of the application, and the description of the embodiment is only used for helping to understand the method and the core idea of the application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (6)
1. A method for unmanned aerial vehicle edge perception computation and joint transmission is applied to an unmanned aerial vehicle to execute a plurality of tasks, and is characterized by comprising the following steps:
the unmanned aerial vehicle calculates sensing time and transmission time required by executing each task according to an iterative algorithm minimizing AOI of each task, wherein the AOI of each task refers to the time length from the generation of the latest effective information to the current moment when each task is executed;
the unmanned aerial vehicle calculates sensing time and transmission time required for executing each task at each flight speed v according to the iterative algorithm;
the unmanned aerial vehicle obtains total time T for executing all tasks, total energy E of the unmanned aerial vehicle, each flight speed v for executing each task and sensing time and transmission time data corresponding to each flight speed v, the sequence of each task is calculated according to a knapsack problem algorithm of minimizing AOI of a plurality of tasks, corresponding sensing time and transmission time, and a total flight track is determined according to the sequence of each task;
the unmanned aerial vehicle calculates sensing time and transmission time required for executing each task according to an iterative algorithm of an expected value of the AOI of the minimized task, and the method comprises the following steps:
during the optimization of perception time, setting a variable during transmission as a fixed value to obtain a value of perception time;
when optimizing the transmission time, setting a variable for sensing as a fixed value, wherein the fixed value of the variable for sensing adopts a sensing time value obtained in the last step to obtain a transmission time value;
iterating the obtained values until the values converge to maximize the average AOI reduction amount of each time slot, namely, the AOI value of the task is minimum;
using the sensing time value and the transmission time value during convergence as the sensing time and the transmission time required for executing the task;
specifically, for each task, the unmanned aerial vehicle performs joint optimization of perception and transmission according to iteration;
recording the flight time in the ith task as the sensing flight time
The total time of flight and information collection is taken as the sensing time TsThe time when the information transmission is completed is recorded as the transmission time Tt；
When perception optimization is carried out, T is used for transmission by unmanned aerial vehicletSet to a fixed value, and then sense flight time
And sensing total time of use TsOptimizing to maximize average AOI per slot reduction
The problem can be written as follows:
t0indicating that when a unit of unmanned aerial vehicle perception is performed,
representing the sensing execution times of the unmanned aerial vehicle; an (t) represents the AOI of drone n at time t;
firstly, it is obtained by mathematical analysis
And TsCan then prove the relationship of
For TsMonotonically increasing first and then monotonically decreasing, so TsAnd
the optimal solution can be quickly found out in a traversal mode;
when transmission optimization is performed, the unmanned aerial vehicle flies for perception
And time for sensing TsSet to a constant value and then time for transmission TtOptimization is carried out, and according to the formula, the flight time is sensed
And time for sensing TsAll are constant, time for transmission TtReduction from AOI per time slot
In a convex functional relationship by maximizing the average AOI reduction per time slot
Then the corresponding transmission time T can be obtainedt；
Wherein, the calculating the sequence of each task according to the knapsack problem algorithm of minimizing the AOI of a plurality of tasks, the corresponding sensing time and the corresponding transmission time, and determining the total flight track comprises the following steps:
defining U as an action for completing the task i at the time t with the speed v, defining e as energy consumption of the action, G as benefit of the action, defining U as a set of all actions, and defining G as maximum benefit obtained by each time slot on average, namely maximum value of reduction of AOI of each time slot;
in the case of certain U and E, the maximum gain G obtained per slot on average is the maximum of two cases:
(1) taking an action U consuming a corresponding energy consumption E, the sum of the gain g of the action and the maximum gain obtained in the case of U-U and E-E;
(2) maximum benefit obtained without action U, in the case of U-U and E;
performing recursive optimization on U and E to obtain an optimal task sequence, obtaining sensing time and transmission time corresponding to each task, and determining a total flight trajectory according to respective sequences of a plurality of tasks, wherein U-U is a set of all actions remaining after an action U is removed, and E-E is the sum of energy remaining after energy E is consumed in the total energy E;
in particular, define
An action for completing the acquisition sensing and transmission of task i at the speed of v at the time of t is defined
The energy consumption for this action is such that,
the benefit for this action, i.e. the average per slot AOI reduction in this action;
definition of
Is a set of all actions;
g (A, Ei) represents the average per slot AOI reduction when all actions are taken from set A and the drone energy remains Ei
Maximum value of (d);
the goal is to obtain the value of G (U, E), which can be obtained by the following recursive relationship; for the
It satisfies the following recursive relation:
i.e. for collections
The optimal solution which can be reached by all the actions is that the actions are adopted
And then, obtaining an optimal solution by using the residual action set and energy:
or take no action
From the collection
The optimal solution obtained in
Through the above algorithm, for
And recursion is continuously carried out, and finally an optimal solution is obtained, so that the optimal task sequence, the flight speed of the unmanned aerial vehicle for executing each task, the corresponding sensing time and the corresponding transmission time of each task are determined, and then the total flight trajectory is determined according to the respective sequence of the tasks.
2. The method of claim 1, wherein the determining of the flight trajectory within each mission comprises:
the unmanned aerial vehicle directly flies to the sensing point to acquire data, the data acquisition is not performed in the flying process, and when the sensing point is reached, the unmanned aerial vehicle hovers at the sensing point to acquire the data;
the unmanned aerial vehicle flies to a transmission point along the direction of improving the transmission signal-to-noise ratio to be the fastest to transmit data, data transmission is carried out when the transmission signal-to-noise ratio is larger than a threshold value, and after the transmission point is reached, if data transmission is not finished, the unmanned aerial vehicle hovers at the transmission point to continue transmission until the data transmission is finished.
3. An apparatus for edge-aware computing and joint transmission of an unmanned aerial vehicle, applied to an unmanned aerial vehicle for performing a plurality of tasks, the apparatus comprising:
the optimization module is configured to calculate sensing time and transmission time required by execution of each task by the unmanned aerial vehicle according to an iterative algorithm which minimizes AOI of each task, wherein the AOI of each task refers to the time length from the generation of the latest effective information to the current time when each task is executed;
the calculation module is configured to calculate sensing time and transmission time required for executing each task at each flight speed v by the unmanned aerial vehicle according to the iterative algorithm;
the sequencing module is configured to acquire total time T for executing all tasks, total energy E of the unmanned aerial vehicle, each flight speed v for executing each task and sensing time and transmission time data corresponding to each flight speed v, calculate sequencing for each task according to a knapsack problem algorithm minimizing AOI of the whole task, corresponding sensing time and transmission time, and determine a total flight track according to respective sequencing of a plurality of tasks;
wherein the optimization module is specifically configured to:
during the optimization of perception time, setting a variable during transmission as a fixed value to obtain a value of perception time;
when optimizing the transmission time, setting a variable for sensing as a fixed value, wherein the fixed value of the variable for sensing adopts a sensing time value obtained in the last step to obtain a transmission time value;
iterating the obtained values until the values converge to maximize the average AOI reduction amount of each time slot, namely, the AOI value of the task is minimum;
using the sensing time value and the transmission time value during convergence as the sensing time and the transmission time required for executing the task;
specifically, for each task, the unmanned aerial vehicle performs joint optimization of perception and transmission according to iteration;
recording the flight time in the ith task as the sensing flight time
The total time of flight and information collection is taken as the sensing time TsThe time when the information transmission is completed is recorded as the transmission time Tt；
When perception optimization is carried out, T is used for transmission by unmanned aerial vehicletSet to a fixed value, and then sense flight time
And sensing total time of use TsOptimized to maximizeReducing amount of AOI per time slot by normalized average
The problem can be written as follows:
t0indicating that when a unit of unmanned aerial vehicle perception is performed,
representing the sensing execution times of the unmanned aerial vehicle; an (t) represents the AOI of drone n at time t;
firstly, it is obtained by mathematical analysis
And TsCan then prove the relationship of
For TsMonotonically increasing first and then monotonically decreasing, so TsAnd
the optimal solution can be quickly found out in a traversal mode;
when transmission optimization is performed, the unmanned aerial vehicle flies for perception
And time for sensing TsSet to a constant value and then time for transmission TtOptimization is carried out, and according to the formula, the flight time is sensed
And time for sensing TsAll are constant, time for transmission TtReduction from AOI per time slot
In a convex functional relationship by maximizing the average AOI reduction per time slot
Then the corresponding transmission time T can be obtainedt；
Wherein the sorting module is specifically configured to:
defining U as an action for completing the task i at the time t with the speed v, defining e as energy consumption of the action, G as benefit of the action, defining U as a set of all actions, and defining G as maximum benefit obtained by each time slot on average, namely maximum value of reduction of AOI of each time slot;
in the case of certain U and E, the maximum gain G obtained per slot on average is the maximum of two cases:
(1) taking an action U consuming a corresponding energy consumption E, the sum of the gain g of the action and the maximum gain obtained in the case of U-U and E-E;
(2) maximum benefit obtained without action U, in the case of U-U and E;
performing recursive optimization on U and E to obtain an optimal task sequence, obtaining sensing time and transmission time corresponding to each task, and determining a total flight trajectory according to respective sequences of a plurality of tasks, wherein U-U is a set of all actions remaining after an action U is removed, and E-E is the sum of energy remaining after energy E is consumed in the total energy E;
in particular, define
An action for completing the acquisition sensing and transmission of task i at the speed of v at the time of t is defined
The energy consumption for this action is such that,
the benefit for this action, i.e. the average per slot AOI reduction in this action;
definition of
Is a set of all actions;
G(A，Ei) Represents the average per-slot AOI decrement when all actions are taken from set A and the drone energy remains Ei
Maximum value of (d);
the goal is to obtain the value of G (U, E), which can be obtained by the following recursive relationship; for the
It satisfies the following recursive relation:
i.e. for collections
The optimal solution which can be reached by all the actions is that the actions are adopted
And then, obtaining an optimal solution by using the residual action set and energy:
or take no action
From the collection
The optimal solution obtained in
Through the above algorithm, for
And recursion is continuously carried out, and finally an optimal solution is obtained, so that the optimal task sequence, the flight speed of the unmanned aerial vehicle for executing each task, the corresponding sensing time and the corresponding transmission time of each task are determined, and then the total flight trajectory is determined according to the respective sequence of the tasks.
4. The apparatus of claim 3, wherein the sequencing module further comprises a trajectory determination sub-module that determines a flight trajectory within each task;
the track determining submodule is configured to enable the unmanned aerial vehicle to directly fly to the sensing point for data acquisition, the data acquisition is not carried out in the process of flying, and after the sensing point is reached, the unmanned aerial vehicle hovers at the sensing point for data acquisition;
the unmanned aerial vehicle flies to a transmission point along the direction of improving the transmission signal-to-noise ratio to be the fastest to transmit data, data transmission is carried out when the transmission signal-to-noise ratio is larger than a threshold value, and after the transmission point is reached, if data transmission is not finished, the unmanned aerial vehicle hovers at the transmission point to continue transmission until the data transmission is finished.
5. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 2.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 2 are implemented when the computer program is executed by the processor.
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