CN113282352A - Energy-saving unloading method based on multi-unmanned aerial vehicle cooperative auxiliary edge calculation - Google Patents

Energy-saving unloading method based on multi-unmanned aerial vehicle cooperative auxiliary edge calculation Download PDF

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CN113282352A
CN113282352A CN202110615178.7A CN202110615178A CN113282352A CN 113282352 A CN113282352 A CN 113282352A CN 202110615178 A CN202110615178 A CN 202110615178A CN 113282352 A CN113282352 A CN 113282352A
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aerial vehicle
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CN113282352B (en
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余雪勇
朱烨
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Nanjing University of Posts and Telecommunications
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    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/445—Program loading or initiating
    • G06F9/44594—Unloading
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F9/00—Arrangements for program control, e.g. control units
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    • H04W28/0221—Traffic management, e.g. flow control or congestion control based on user or device properties, e.g. MTC-capable devices power availability or consumption
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Abstract

Initializing configuration information of a user terminal and an unmanned aerial vehicle based on an energy-saving unloading method of multi-unmanned aerial vehicle collaborative auxiliary edge calculation; setting an iteration number variable, and initializing and calculating distribution of unloading task quantity, tracks of all unmanned aerial vehicles and a matching relation between multiple unmanned aerial vehicles and a multi-user terminal; obtaining an initial objective function value, and entering iteration; optimizing and calculating distribution of unloading task quantity, tracks of all unmanned aerial vehicles and matching relation of multiple unmanned aerial vehicles and multiple user terminals; and judging whether the difference between the objective function values of the current iteration and the previous iteration is smaller than a threshold value. If yes, obtaining the optimal locally-calculated task quantity, the optimal calculation unloaded task quantity and the unmanned aerial vehiclemOptimal track and unmanned aerial vehiclemAnd user terminalkThe optimal matching relation and the minimum user terminal energy consumption value are obtained; otherwise, the iteration times are added and returned. According to the invention, the time delay of local calculation of the user terminal, the unloading matching problem of multiple unmanned aerial vehicles and multiple user terminals and the safety distance problem among the multiple unmanned aerial vehicles are fully considered, and the practicability is improved.

Description

Energy-saving unloading method based on multi-unmanned aerial vehicle cooperative auxiliary edge calculation
Technical Field
The invention relates to the technical field of computer wireless communication, in particular to an energy-saving unloading method based on multi-unmanned aerial vehicle cooperative auxiliary edge computing in edge computing communication.
Background
With the explosion of Internet of Things (IoT), billions of wireless devices will enter the Internet in the future. Massive user data and rapidly increased data traffic urge various computing-intensive applications such as cloud games, short video live broadcast, image recognition and the like, and for most user terminal devices, the limited energy and computing capacity of the user terminal devices cannot well meet the application requirements.
Mobile Edge Computing (MEC) provides Edge intelligent services in the near vicinity by sinking Computing resources to the radio access network infrastructure closest to the user terminal, greatly relieving the Computing pressure of the user terminal. In a dangerous scene or a complex area lacking network infrastructure, by deploying the unmanned aerial vehicle carrying the MEC server, rapid, flexible and real-time edge computing service can be provided for the user terminal with lower deployment cost. However, consider that a single drone has limitations in endurance, load capacity, computational power, and coverage area. For scenes with a large number of user terminals, large requirements and wide distribution, a plurality of unmanned aerial vehicles need to be introduced to jointly provide edge computing service for the user terminals so as to ensure reliable service quality.
Based on the prospect that the unmanned aerial vehicle is applied to the wireless communication network, relevant scholars study the problem of unmanned aerial vehicle auxiliary edge calculation in recent years.
Zhou F, WuY, Sun H, et al, in his meeting paper "UAV-Enabled Mobile Edge Computing: Offloading Optimization and traffic Design," (2018IEEE International Conference on Communications) proposed an MEC system based on unmanned aerial vehicle wireless power supply, and studied the minimization problem of unmanned aerial vehicle energy consumption under the constraint of an energy collection model. The method is characterized in that a ground user can acquire energy from the unmanned aerial vehicle through a radio frequency signal and perform local calculation and calculation unloading by using the acquired energy.
The paper "User Association and Path Planning for UAV-aid Mobile Edge Computing With Energy management System" (IEEE Wireless Communications Letters,2019.) published by Y.Qian, F.Wang, J.Li et al improves the offloading strategy. Whether the user offloads, is local computation or offloads computation is specified by introducing a binary variable. In addition, the quality of service (QoS) is fully considered, the task complexity among users is differentiated, and the calculation and unloading data volume of the user terminal is maximized by taking the QoS index and the energy of the unmanned aerial vehicle as constraints.
Zhang J, Zhou L, Zhou F, et al, in its published paper "Computation-Efficient Offloading and objective Scheduling for Multi-UAV Assisted Mobile Edge Computing" (IEEE Transactions on Vehicular Technology, 2019), proposes a Multi-drone Assisted MEC system that comprehensively considers the matching relationship of drones and users, CPU rotation frequency constraints, power and spectrum resource allocation constraints, and Trajectory Scheduling constraints to maximize computational efficiency. Aiming at the non-convex problem of nonlinear coupling with different variables, the non-convex problem is restated as a parameterized planning problem, and an iterative search algorithm of a double-loop structure is provided for solving.
In the above studies, there are two problems: 1) the time delay of the local computation of the user terminal during the task is ignored, which may result in the user computation being offloaded and the local computation being completed asynchronously within the specified task time. 2) For a multi-drone assisted edge computing system, the problem of offloading matching is simplified to the problem of selecting single drone for offloading by a single user during the modeling process, which may result in a waste of computing performance of other drones at corresponding time slots.
Disclosure of Invention
Aiming at the problems, the invention provides an energy-saving unloading method based on multi-unmanned aerial vehicle cooperative auxiliary edge calculation. The method aims at minimizing the energy consumption of a user terminal, combines the unloading matching problem of multiple unmanned aerial vehicles and multiple user terminals and the safety distance problem among the multiple unmanned aerial vehicles, adopts a partial unloading strategy, comprehensively considers the application requirements of the user terminal and the performance conditions of each unmanned aerial vehicle, and constructs a multi-unmanned aerial vehicle assisted multi-user edge computing system model. In order to solve the model, a three-step iterative optimization algorithm based on block coordinate reduction is provided, the task quantity of local calculation of the user terminal, the task quantity of calculation unloading, the track of the unmanned aerial vehicle and the unloading matching relation of multiple unmanned aerial vehicles and multiple user terminals are jointly optimized, and the energy consumption of the user terminal is remarkably reduced.
Energy-saving unloading method based on multi-unmanned aerial vehicle collaborative auxiliary edge calculation is characterized in that: the method comprises the following steps:
s1, initializing configuration information of the user terminal and the unmanned aerial vehicle;
s2, setting an iteration variable, and initializing distribution of calculation unloading task quantity, tracks of all unmanned aerial vehicles and a matching relation between multiple unmanned aerial vehicles and multiple user terminals;
s3, obtaining an initial objective function value according to the initialized condition, and entering iteration;
s4, optimizing and calculating the distribution of the unloading task amount;
s5, optimizing the track of each unmanned aerial vehicle;
s6, optimizing the matching relationship between multiple unmanned aerial vehicles and multiple user terminals;
s7, judging whether the difference between the objective function value of the ith iteration and the objective function value of the (i-1) th iteration is smaller than a preset threshold epsilon; if yes, executing step S8, otherwise, returning to step S4 if the iteration number i is i + 1;
s8, obtaining the optimal task amount of local calculation
Figure BDA0003097099330000041
Optimal computation of offloaded task volume
Figure BDA0003097099330000042
Optimal trajectory for drone m
Figure BDA0003097099330000043
Optimal matching relation between unmanned aerial vehicle m and user terminal k
Figure BDA0003097099330000044
And a minimum user terminal energy consumption value.
Further, the configuration information of the user terminal and the drone in step S1 includes:
task duration T, time frame
Figure BDA0003097099330000045
Number of user terminals K, specific position z of user terminal Kk＝(xk，yk，0)，
Figure BDA0003097099330000046
Task volume L of user terminal kkComputing power f of user terminal kkComplexity of user terminal k task CkEffective switched capacitor gamma of a user terminal k processorkNumber M of unmanned aerial vehicles, flying height H of unmanned aerial vehicles, and instantaneous position q of unmanned aerial vehicle Mm[n]＝(xm[n]，ym[n]，H)，
Figure BDA0003097099330000047
Starting point position q of unmanned aerial vehicle mm，0And end position qm，fM speed of unmanned aerial vehicle
Figure BDA0003097099330000048
Optimal matching relation b between unmanned aerial vehicle m and user terminal kkm[n]Upper limit value v of unmanned aerial vehicle speedmaxSafe distance d between unmanned aerial vehiclesminMass M of unmanned aerial vehicle, and computing power f of unmanned aerial vehiclecEffective switch capacitor gamma of unmanned aerial vehiclecBandwidth B of wireless channel, average channel gain between user terminal k and drone m
Figure BDA0003097099330000049
The error threshold epsilon.
Further, the specific method of step S2 is as follows:
setting an iteration number variable i, wherein an initial value i is 0;
setting the allocation scheme of the initial calculation unloading task amount to be full unloading, namely, unloading the tasks of all users to the unmanned aerial vehicle for processing, so that the initial local calculation task amount and the initial calculation unloading task amount are respectively
Figure BDA00030970993300000410
And
Figure BDA00030970993300000411
wherein the variable in parentheses is an iteration variable i; setting the initial track of each unmanned aerial vehicle to be a straight line from the starting point to the end point, keeping the constant speed in the flight process, not starting iteration at the moment, and representing the initial track of the nth frame of unmanned aerial vehicle m as qm，n[0](ii) a Meanwhile, initializing the matching relationship of the multiple unmanned aerial vehicles and the multiple user terminals, and constructing the matching relationship by taking the nearby relationship as a principle to obtain an initial matching relationship bkm，n[0]。
Further, the specific method of step S3 is as follows:
by the formula
Figure BDA0003097099330000051
Acquiring communication energy consumption generated by calculation unloading of user terminal k in nth frame
Figure BDA0003097099330000052
Wherein N is0(dBm/Hz) is the power spectral density of Additive White Gaussian Noise (AWGN) with zero mean;
by the formula
Figure BDA0003097099330000053
Obtaining the energy consumption of the local calculation of the user terminal k in the nth frame
Figure BDA0003097099330000054
By the formula
Figure BDA0003097099330000055
Acquiring energy consumption generated by processing task unloaded by user terminal k at nth frame by unmanned aerial vehicle m
Figure BDA0003097099330000056
By the formula
Figure BDA0003097099330000057
Obtaining unmanned aerial vehicleThe propulsive energy consumption generated by the flight of the n frame
Figure BDA0003097099330000058
Wherein κ is 0.5MuavΔ，MuavIs the mass of each drone, vm[n]The instantaneous speed of the unmanned plane m at the nth frame is obtained;
by the formula
Figure BDA0003097099330000059
Substituted into step S2
Figure BDA00030970993300000510
qm，n[0]And bkm，n[0]And further obtain the energy consumption F [0 ] of the initial user terminal]；
Setting an iteration variable i to 1 and a fixed variable qm,n[1]＝qm,n[0]、bkm,n[1]＝bkm,n[0]。
Further, the specific method of step S4 is as follows:
trajectory q of fixed unmanned aerial vehicle mm，n[i]Matching relation b between multiple unmanned aerial vehicles and multiple user terminalskm，n[i]Calculating the optimal local calculation task quantity and the optimal calculation unloading task quantity in the ith iteration by using a CVX tool of mathematic software Matlab, and respectively recording the optimal local calculation task quantity and the optimal calculation unloading task quantity as
Figure BDA0003097099330000061
And
Figure BDA0003097099330000062
further, the specific method of step S5 is as follows:
fixing the task volume of local computations
Figure BDA0003097099330000063
Computing offloaded task volumes
Figure BDA0003097099330000064
Matching relation b between multiple unmanned aerial vehicles and multiple user terminalskm，n[i]Calculating the optimal track of the unmanned aerial vehicle m during the ith iteration by using a CVX tool of mathematic software Matlab
Figure BDA0003097099330000065
Further, the specific method of step S6 is as follows:
fixing the task volume of local computations
Figure BDA0003097099330000066
Computing offloaded task volumes
Figure BDA0003097099330000067
And the trajectory of unmanned plane m
Figure BDA0003097099330000068
Calculating the optimal matching relation between the unmanned aerial vehicle m and the user terminal k during the ith iteration by using a CVX tool of mathematic software Matlab
Figure BDA0003097099330000069
Further, the specific method of step S7 is as follows:
comparing the objective function value obtained in the ith iteration with the objective function value obtained in the (i-1) th iteration, and if the difference value is in the range of [ -epsilon, epsilon]If yes, the iteration is ended, and step S8 is executed; otherwise, the variable i of the iteration number is i +1, and the variable i is obtained in step S5 and step S6
Figure BDA00030970993300000610
And
Figure BDA00030970993300000611
unmanned aerial vehicle trajectory q as next iterationm，n[i+1]And bkm，n[i+1]Input is made and execution returns to step S4.
The invention achieves the following beneficial effects:
first, the invention considers the time delay of the local computation of the user terminal, ensures that the computation unloading of the user and the local computation are completed synchronously in the specified time, and improves the rationality of the method.
Secondly, the invention improves the unloading matching relation of multiple unmanned aerial vehicles and multiple users in the multi-unmanned aerial vehicle auxiliary edge computing system, and any unmanned aerial vehicle in the same time slot can receive the unloading tasks of multiple user terminals, thereby fully releasing the computing performance of the multiple unmanned aerial vehicles.
Drawings
Fig. 1 is a schematic flow chart of an energy saving unloading method in an embodiment of the present invention.
Fig. 2 is a schematic diagram of a system for cooperative assisted edge computing by multiple drones in an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
As shown in fig. 2, the edge computing system for collaborative assistance of multiple drones is composed of a drone with M-rack mounted MEC server in the air and K user terminals on the ground. Each user terminal has certain computing power, and the requirement of executing simple tasks locally is met. In contrast, the unmanned aerial vehicle has extremely strong computing power, can quickly complete computation-intensive tasks, and provides edge computing service for the user terminal. In the system, each user terminal is capable of performing local computation and computation offload simultaneously. Similarly, the drone can also perform the task of offloading the user terminal to the drone while in flight.
Assuming that the tasks of the user terminal can be divided and placed on the user local and unmanned aerial vehicle edge computing servers for execution, a partial unloading strategy is adopted to jointly optimize the task amount of the local computation of the user terminal
Figure BDA0003097099330000071
Computing offloaded task volumes
Figure BDA0003097099330000072
Trajectory q of unmanned aerial vehiclem[n]And unloading matching relation b of multiple unmanned aerial vehicles and multiple user terminalskm[n]The energy consumption of the user terminal is minimized.
Based on the above description, the energy consumption problem of the user terminal can be described as problem P1:
Figure BDA0003097099330000081
in question P1, C1 specifies the matching relationship of the user terminal with the drone during the offloading process; c2 ensures that the uplink unloaded data volume of each user terminal can be sufficiently distributed to the corresponding unmanned aerial vehicle for processing; c3 specifies the upper energy limit for each drone, Em,0Representing the total energy of each unmanned aerial vehicle, and C4 ensures that tasks of all user terminals can be completed within the time length T; c5 ensures that the local computation of the user terminal k can be finished within the duration T; c6 ensures that the task volume of user terminal k is non-negative in any frame; c7 specifies the starting point and the end point of the flight path of each unmanned aerial vehicle; c8 limits the speed at which each drone flies; c9 specifies a safe distance between drones to avoid a collision accident.
Since the objective function of the problem P1 has coupling among multiple optimization variables and the constraint C3 has non-convexity, the problem P1 has non-convexity, and belongs to the non-convex non-linear programming problem. For the solution of the problems, the problem of difficult decoupling exists, and an effective means for obtaining the strict optimal solution is lacked at present. The embodiment of the invention provides a three-step iterative optimization algorithm for solving the problem P1 based on block coordinate descent.
First, the target problem P1 needs to be decomposed into three convex sub-problems. And secondly, solving the subproblems sequentially through a convex optimization algorithm. And finally, carrying out global optimization based on a block coordinate descent method, and solving the minimum value of the energy consumption of the user terminal.
Communication energy consumption generated by user terminal calculation unloading
Figure BDA0003097099330000091
Track q of unmanned aerial vehiclem[n]Correlation, and calculation of offloaded task volume with user terminal
Figure BDA0003097099330000092
Correlation, the coupling of the two results in non-convexity of the objective function. In order to further decompose the target problem into sub-problems according to the optimization variables, the pair is needed
Figure BDA0003097099330000093
Rewriting the expression (c) and performing formal separation:
Figure BDA0003097099330000094
energy consumption due to local calculation of user terminal
Figure BDA0003097099330000095
Task volume with local computation
Figure BDA0003097099330000096
The components are in positive correlation with each other,
Figure BDA0003097099330000097
the following can be rewritten:
Figure BDA0003097099330000098
in summary, the question P1 can be rewritten as follows:
Figure BDA0003097099330000099
it can be found that the problem P1 is composed of three sub-problems, which are the problem of allocation of the task amount of the user terminal, the problem of trajectory optimization of the drone, and the problem of offloading matching of multiple drones-multiple user terminals.
The specific process of the present invention is described below with reference to FIG. 1.
S1, initializing configuration information of the user terminal and the unmanned aerial vehicle;
s2, setting an iteration variable, and initializing distribution of calculation unloading task quantity, tracks of all unmanned aerial vehicles and a matching relation between multiple unmanned aerial vehicles and multiple user terminals;
s3, obtaining an initial objective function value according to the initialized condition, and entering iteration;
s4, optimizing and calculating the distribution of the unloading task amount;
s5, optimizing the track of each unmanned aerial vehicle;
s6, optimizing the matching relationship between multiple unmanned aerial vehicles and multiple user terminals;
s7, judging whether the difference between the objective function value of the ith iteration and the objective function value of the (i-1) th iteration is smaller than a preset threshold epsilon; if yes, executing step S8, otherwise, returning to step S4 if the iteration number i is i + 1;
s8, obtaining the optimal task amount of local calculation
Figure BDA0003097099330000101
Optimal computation of offloaded task volume
Figure BDA0003097099330000102
Optimal trajectory for drone m
Figure BDA0003097099330000103
Optimal matching relation between unmanned aerial vehicle m and user terminal k
Figure BDA0003097099330000104
And a minimum user terminal energy consumption value.
The configuration information of the user terminal and the drone in step S1 includes:
task duration T, time frame
Figure BDA0003097099330000105
Number of user terminals K, specific position z of user terminal Kk＝(xk，yk，0)，
Figure BDA0003097099330000106
Task volume L of user terminal kkComputing power f of user terminal kkComplexity of user terminal k task CkEffective switched capacitor gamma of a user terminal k processorkNumber M of unmanned aerial vehicles, flying height H of unmanned aerial vehicles, and instantaneous position q of unmanned aerial vehicle Mm[n]＝(xm[n]，ym[n]，H)，
Figure BDA0003097099330000111
Starting point position q of unmanned aerial vehicle mm，0And end position qm，fM speed of unmanned aerial vehicle
Figure BDA0003097099330000112
Optimal matching relation b between unmanned aerial vehicle m and user terminal kkm[n]Upper limit value v of unmanned aerial vehicle speedmaxSafe distance d between unmanned aerial vehiclesminMass M of unmanned aerial vehicle, and computing power f of unmanned aerial vehiclecEffective switch capacitor gamma of unmanned aerial vehiclecBandwidth B of wireless channel, average channel gain between user terminal k and drone m
Figure BDA0003097099330000113
The error threshold epsilon.
The specific method of step S2 is:
setting an iteration number variable i, wherein an initial value i is 0;
setting the allocation scheme of the initial calculation unloading task amount to be full unloading, namely, unloading the tasks of all users to the unmanned aerial vehicle for processing, so that the initial local calculation task amount and the initial calculation unloading task amount are respectively
Figure BDA0003097099330000114
And
Figure BDA0003097099330000115
wherein the variable in parentheses is an iteration variable i; setting the initial track of each unmanned aerial vehicle to be a straight line from the starting point to the end point, keeping the constant speed in the flight process, not starting iteration at the moment, and representing the initial track of the nth frame of unmanned aerial vehicle m as qm，n[0](ii) a Simultaneous, multi-drone-multi-user terminal matchingInitializing the relationship, constructing the relationship by using the nearby relationship as a principle to obtain an initial matching relationship bkm，n[0]。
The specific method of step S3 is:
by the formula
Figure BDA0003097099330000116
Acquiring communication energy consumption generated by calculation unloading of user terminal k in nth frame
Figure BDA0003097099330000117
Wherein N is0(dBm/Hz) is the power spectral density of Additive White Gaussian Noise (AWGN) with zero mean;
by the formula
Figure BDA0003097099330000118
Obtaining the energy consumption of the local calculation of the user terminal k in the nth frame
Figure BDA0003097099330000119
By the formula
Figure BDA0003097099330000121
Acquiring energy consumption generated by processing task unloaded by user terminal k at nth frame by unmanned aerial vehicle m
Figure BDA0003097099330000122
By the formula
Figure BDA0003097099330000123
Obtaining the propulsive energy consumption generated by the flight of the unmanned aerial vehicle m at the nth frame
Figure BDA0003097099330000124
Wherein κ is 0.5MuavΔ，MuavIs the mass of each drone, vm[n]The instantaneous speed of the unmanned plane m at the nth frame is obtained;
by the formula
Figure BDA0003097099330000125
Substituted into step S2
Figure BDA0003097099330000126
qm，n[0]And bkm，n[0]And further obtain the energy consumption F [0 ] of the initial user terminal]；
Setting an iteration variable i to 1 and a fixed variable qm,n[1]＝qm,n[0]、bkm,n[1]＝bkm,n[0]。
The specific method of step S4 is:
trajectory q of fixed unmanned aerial vehicle mm，n[i]And the initial relationship b of multiple UAVs-multiple user terminalskm，n[i]Converting the question P1 into a task amount calculated locally by the user terminal
Figure BDA0003097099330000127
And calculating the amount of offloaded tasks as an optimization variable
Figure BDA0003097099330000128
Minimizing the problem of energy consumption of the user terminal. Therefore, the problem P1 can be rewritten as follows:
Figure BDA0003097099330000129
due to f0And f1Both are convex functions, and the objective function of the problem P1.1 can be regarded as the sum of two convex functions and still has convex properties. For the problem P1.1, the optimal local calculation task amount of the nth frame user terminal k can be obtained by utilizing a CVX tool of math software Matlab and solving through a standard convex optimization technology
Figure BDA00030970993300001210
And optimal computation of offloaded task volume
Figure BDA00030970993300001211
The specific method of step S5 is:
local computation of the task volume optimized by the nth frame of user terminal k solved in problem P1.1
Figure BDA0003097099330000131
And optimal computation of offloaded task volume
Figure BDA0003097099330000132
Based on the initial relation b of a plurality of unmanned aerial vehicles and a plurality of user terminals is fixed simultaneouslykm，n[i]The problem P1.1 can be converted into a trajectory q with a dronem[n]In order to optimize variables, the energy consumption of the user terminal is minimized. Therefore, problem P1 can be further rewritten as problem P1.2:
Figure BDA0003097099330000133
the objective function of the problem P1.2 is with respect to qm[n]And the constraint is also convex, the problem P1.2 is therefore a convex problem. For the problem P1.2, the CVX tool of mathematic software Matlab can be used for solving through the standard convex optimization technology to obtain the optimal track m of the unmanned aerial vehicle of the nth frame
Figure BDA0003097099330000134
The specific method of step S6 is:
local computation of the optimal task volume with the nth frame of user terminals k solved in problems P1.1 and P1.2
Figure BDA0003097099330000135
Optimal computation of offloaded task volume
Figure BDA0003097099330000136
And the optimal track of the unmanned plane m
Figure BDA0003097099330000137
On the basis, the problem P1 can be converted into the corresponding relation b of multiple unmanned aerial vehicles and multiple user terminalskm[n]For optimizingAnd (4) minimizing the problem of energy consumption of the user terminal. Therefore, problem P1 can be further rewritten as problem P1.3:
Figure BDA0003097099330000138
the objective function of the problem P1.3 is related to bkm[n]The convex problem of (2). For the problem P1.3, a CVX tool of mathematic software Matlab is utilized, the solution is carried out through a standard convex optimization technology, and the optimal corresponding relation between the nth frame unmanned aerial vehicle m and the user terminal k is obtained
Figure BDA0003097099330000139
The specific method of step S7 is:
comparing the objective function value obtained in the ith iteration with the objective function value obtained in the (i-1) th iteration, and if the difference value is in the range of [ -epsilon, epsilon]If so, the iteration ends and step S8 is executed. Otherwise, the variable i of the iteration number is i +1, and the variable i is obtained in step S5 and step S6
Figure BDA0003097099330000141
And
Figure BDA0003097099330000142
unmanned aerial vehicle trajectory q as next iterationm，n[i+1]And bkm，n[i+1]Input is made and execution returns to step S4.
In conclusion, the invention considers the problem of energy consumption of the user terminal under the multi-unmanned-aerial-vehicle-assisted multi-user edge computing system. Aiming at the characteristics of multiple unmanned aerial vehicles, the time delay of local calculation of a user terminal, the unloading matching problem of the multiple unmanned aerial vehicles and the multiple user terminals and the safety distance problem among the multiple unmanned aerial vehicles are particularly considered. Aiming at solving the problem, a three-step iterative optimization algorithm based on block coordinate reduction is provided, and energy consumption minimization of the user terminal is realized.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, but equivalent modifications or changes made by those skilled in the art according to the present disclosure should be included in the scope of the present invention as set forth in the appended claims.

Claims (8)

1. Energy-saving unloading method based on multi-unmanned aerial vehicle collaborative auxiliary edge calculation is characterized in that: the method comprises the following steps:
s1, initializing configuration information of the user terminal and the unmanned aerial vehicle;
s2, setting an iteration variable, and initializing distribution of calculation unloading task quantity, tracks of all unmanned aerial vehicles and a matching relation between multiple unmanned aerial vehicles and multiple user terminals;
s3, obtaining an initial objective function value according to the initialized condition, and entering iteration;
s4, optimizing and calculating the distribution of the unloading task amount;
s5, optimizing the track of each unmanned aerial vehicle;
s6, optimizing the matching relationship between multiple unmanned aerial vehicles and multiple user terminals;
s7, judging whether the difference between the objective function value of the ith iteration and the objective function value of the (i-1) th iteration is smaller than a preset threshold epsilon; if yes, executing step S8, otherwise, returning to step S4 if the iteration number i is i + 1;
s8, obtaining the optimal task amount of local calculation
Figure FDA0003097099320000011
Optimal computation of offloaded task volume
Figure FDA0003097099320000012
Optimal trajectory for drone m
Figure FDA0003097099320000013
Optimal matching relation between unmanned aerial vehicle m and user terminal k
Figure FDA0003097099320000014
And a minimum user terminal energy consumption value.
2. The energy-saving unloading method based on multi-unmanned-aerial-vehicle cooperative auxiliary edge computing according to claim 1, characterized in that: the configuration information of the user terminal and the drone in step S1 includes:
task duration T, time frame
Figure FDA0003097099320000015
Number of user terminals K, specific location of user terminal K
Figure FDA0003097099320000016
Task volume L of user terminal kkComputing power f of user terminal kkComplexity of user terminal k task CkEffective switched capacitor gamma of a user terminal k processorkNumber M of unmanned aerial vehicles, flying height H of unmanned aerial vehicles, and instantaneous position of unmanned aerial vehicle M
Figure FDA0003097099320000021
Starting point position q of unmanned aerial vehicle mm，0And end position qm，fM speed of unmanned aerial vehicle
Figure FDA0003097099320000022
Optimal matching relation b between unmanned aerial vehicle m and user terminal kkm[n]Upper limit value v of unmanned aerial vehicle speedmaxSafe distance d between unmanned aerial vehiclesminMass M of unmanned aerial vehicle, and computing power f of unmanned aerial vehiclecEffective switch capacitor gamma of unmanned aerial vehiclecBandwidth B of wireless channel, average channel gain between user terminal k and drone m
Figure FDA0003097099320000023
The error threshold epsilon.
3. The energy-saving unloading method based on multi-unmanned-aerial-vehicle cooperative auxiliary edge computing according to claim 2, characterized in that: the specific method of step S2 is as follows:
setting an iteration number variable i, wherein an initial value i is 0;
setting the allocation scheme of the initial calculation unloading task amount to be full unloading, namely, unloading the tasks of all users to the unmanned aerial vehicle for processing, so that the initial local calculation task amount and the initial calculation unloading task amount are respectively
Figure FDA0003097099320000024
And
Figure FDA0003097099320000025
wherein the variable in parentheses is an iteration variable i; setting the initial track of each unmanned aerial vehicle to be a straight line from the starting point to the end point, keeping the constant speed in the flight process, not starting iteration at the moment, and representing the initial track of the nth frame of unmanned aerial vehicle m as qm，n[0](ii) a Meanwhile, initializing the matching relationship of the multiple unmanned aerial vehicles and the multiple user terminals, and constructing the matching relationship by taking the nearby relationship as a principle to obtain an initial matching relationship bkm，n[0]。
4. The energy-saving unloading method based on multi-unmanned-aerial-vehicle cooperative auxiliary edge computing according to claim 3, characterized in that: the specific method of step S3 is as follows:
by the formula
Figure FDA0003097099320000026
Acquiring communication energy consumption generated by calculation unloading of user terminal k in nth frame
Figure FDA0003097099320000027
Wherein N is0(dBm/Hz) is the power spectral density of Additive White Gaussian Noise (AWGN) with zero mean;
by the formula
Figure FDA0003097099320000031
Obtaining the energy consumption of the local calculation of the user terminal k in the nth frame
Figure FDA0003097099320000032
By the formula
Figure FDA0003097099320000033
Acquiring energy consumption generated by processing task unloaded by user terminal k at nth frame by unmanned aerial vehicle m
Figure FDA0003097099320000034
By the formula
Figure FDA0003097099320000035
Obtaining the propulsive energy consumption generated by the flight of the unmanned aerial vehicle m at the nth frame
Figure FDA0003097099320000036
Wherein κ is 0.5MuavΔ，MuavIs the mass of each drone, vm[n]The instantaneous speed of the unmanned plane m at the nth frame is obtained;
by the formula
Figure FDA0003097099320000037
Substituted into step S2
Figure FDA0003097099320000038
qm，n[0]And bkm，n[0]And further obtain the energy consumption F [0 ] of the initial user terminal]；
Setting an iteration variable i to 1 and a fixed variable qm,n[1]＝qm,n[0]、bkm,n[1]＝bkm,n[0]。
5. The energy-saving unloading method based on multi-unmanned-aerial-vehicle cooperative auxiliary edge computing according to claim 4, characterized in that: the specific method of step S4 is as follows:
trajectory q of fixed unmanned aerial vehicle mm，n[i]Matching relation b between multiple unmanned aerial vehicles and multiple user terminalskm，n[i]Calculating the optimal local calculation task at the ith iteration by using a CVX tool of mathematic software MatlabThe traffic and the optimal calculation-offloaded task volume are respectively denoted as
Figure FDA0003097099320000039
And
Figure FDA00030970993200000310
6. the energy-saving unloading method based on multi-unmanned-aerial-vehicle cooperative auxiliary edge computing according to claim 5, characterized in that: the specific method of step S5 is as follows:
fixing the task volume of local computations
Figure FDA00030970993200000311
Computing offloaded task volumes
Figure FDA00030970993200000312
Matching relation b between multiple unmanned aerial vehicles and multiple user terminalskm，n[i]Calculating the optimal track of the unmanned aerial vehicle m during the ith iteration by using a CVX tool of mathematic software Matlab
Figure FDA00030970993200000313
7. The energy-saving unloading method based on multi-unmanned-aerial-vehicle cooperative auxiliary edge computing according to claim 6, characterized in that: the specific method of step S6 is as follows:
fixing the task volume of local computations
Figure FDA0003097099320000041
Computing offloaded task volumes
Figure FDA0003097099320000042
And the trajectory of unmanned plane m
Figure FDA0003097099320000043
Calculating the optimal matching relation between the unmanned aerial vehicle m and the user terminal k during the ith iteration by using a CVX tool of mathematic software Matlab
Figure FDA0003097099320000044
8. The energy-saving unloading method based on multi-unmanned-aerial-vehicle cooperative auxiliary edge computing according to claim 7, characterized in that: the specific method of step S7 is as follows:
comparing the objective function value obtained in the ith iteration with the objective function value obtained in the (i-1) th iteration, and if the difference value is in the range of [ -epsilon, epsilon]If yes, the iteration is ended, and step S8 is executed; otherwise, the variable i of the iteration number is i +1, and the variable i is obtained in step S5 and step S6
Figure FDA0003097099320000045
And
Figure FDA0003097099320000046
unmanned aerial vehicle trajectory q as next iterationm，n[i+1]And bkm，n[i+1]Input is made and execution returns to step S4.
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