CN112104502A - Time-sensitive multitask edge computing and cache cooperation unloading strategy method - Google Patents

Time-sensitive multitask edge computing and cache cooperation unloading strategy method Download PDF

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CN112104502A
CN112104502A CN202010975197.6A CN202010975197A CN112104502A CN 112104502 A CN112104502 A CN 112104502A CN 202010975197 A CN202010975197 A CN 202010975197A CN 112104502 A CN112104502 A CN 112104502A
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赵明雄
包聆言
李文涛
余俊杰
罗佳
邓彪
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Shenzhen Tianlongteng Technology Co.,Ltd.
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Abstract

The invention relates to a time-sensitive multitask edge calculation and cache cooperation unloading strategy method, which comprises an unmanned aerial vehicle track optimization sub-strategy, an unmanned aerial vehicle resource allocation optimization sub-strategy and an Internet of things equipment unloading decision sub-strategy; and (4) performing iterative operation on the three sub-strategies by using a block coordinate descent method, and converging the final strategy to obtain a final value. The invention provides a computing and caching unloading strategy based on a time-sensitive multitask mobile edge computing network of an unmanned aerial vehicle, and the total energy consumption of IoT equipment is reduced to the greatest extent by comprehensively optimizing the flight trajectory of the unmanned aerial vehicle, the transmission bandwidth of the IoT equipment and the unmanned aerial vehicle, the computing resource allocation of the unmanned aerial vehicle, the unloading rate of the IoT equipment and the task type of the IoT equipment, so that the experience quality requirement of the IoT equipment on the time-sensitive tasks is met.

Description

Time-sensitive multitask edge computing and cache cooperation unloading strategy method
Technical field
The invention belongs to the field of unmanned aerial vehicle and edge computing combination networking, and discloses a time-sensitive multitask edge computing and cache cooperation unloading strategy based on an unmanned aerial vehicle under the background of a 5G Internet of things.
Background
The deployment of the mobile edge computing server is generally fixed, which limits the development of the mobile edge computing server towards being closer to the internet of things equipment, thereby further reducing the delay or energy consumption of the equipment. Unmanned aerial vehicles, as airborne mobile edge computing nodes, are widely used for service coverage in the fields of surveillance, data collection, disaster relief and public safety because of their excellent flexibility and mobility. Although drone-enabled mobile edge computing provides remote resources for internet of things devices, it still faces challenges in communication and computing design due to the limited number of embedded batteries for drones and internet of things devices. At present, various minimum energy consumption strategies are designed for the phenomena at home and abroad, the total unmanned aerial vehicle energy consumption meeting the ground node communication throughput is specifically considered, and the total weighted energy consumption of equipment and unmanned aerial vehicles is reduced. However, offloading energy-consuming workloads to the drones also causes additional delays, which can seriously affect the quality of experience of the internet of things devices and therefore is not negligible in policy design.
Disclosure of Invention
Aiming at the technical defects, the invention provides a time-sensitive multitask edge calculation and cache cooperation unloading strategy based on an unmanned aerial vehicle in the context of a 5G Internet of things. The strategy comprehensively considers and comprehensively optimizes the flight trajectory of the unmanned aerial vehicle, the transmission bandwidth of the Internet of things equipment and the unmanned aerial vehicle, the calculation resource allocation of the unmanned aerial vehicle, the unloading rate of the Internet of things equipment and the task type of the Internet of things equipment, and when a time-sensitive multi-task multi-Internet of things equipment is combined, a mathematical model is established, and the flight trajectory of the unmanned aerial vehicle, the resource allocation (transmission bandwidth and calculation resource allocation) of the unmanned aerial vehicle and the unloading decision sub-strategy (unloading proportion and task type) of the Internet of things equipment. Aiming at track optimization and resource allocation, an auxiliary variable is used for converting an original non-convex model into a convex model and then solving the convex model by using a CVX (composite variable X) method, a block coordinate descent method is continuously used for iteratively solving the task type and the unloading proportion aiming at the unloading decision, and a branch-and-bound algorithm is used for solving the integer variable of the task type.
The invention is realized by adopting the following technical scheme.
The invention discloses a time-sensitive multitask edge calculation and cache cooperation unloading strategy method, which comprises an unmanned aerial vehicle track optimization sub-strategy, an unmanned aerial vehicle resource allocation optimization sub-strategy and an Internet of things equipment unloading decision sub-strategy; iteratively operating the three sub-strategies by using a block coordinate reduction method, and obtaining a final value after a final strategy is converged;
the unmanned aerial vehicle trajectory optimization sub-strategy is used for optimizing the flight trajectory of the unmanned aerial vehicle under the condition that the unmanned aerial vehicle resource allocation optimization sub-strategy and the Internet of things equipment unloading decision sub-strategy are determined;
the unmanned aerial vehicle resource allocation optimization sub-strategy is used for solving the calculation resource allocation and bandwidth allocation of each time slice of the unmanned aerial vehicle for each Internet of things device by using a CVX technology through a conversion model under the condition that the unmanned aerial vehicle flight path and the Internet of things device unloading decision sub-strategy are fixed;
and the Internet of things equipment unloading decision sub-strategy is used for solving the task unloading proportion and the task type in each time slice of the Internet of things equipment under the condition of determining the flight path of the unmanned aerial vehicle and the resource allocation of the unmanned aerial vehicle, wherein the task type comprises a calculation task and a cache task.
The method comprises the following steps:
the method comprises the following steps: an initialization stage: in the stage, the total task volume, the cache capacity and the computing capacity of each piece of internet-of-things equipment are obtained, an internet-of-things equipment unloading decision sub-strategy is initialized, the cache capacity and the computing capacity of the unmanned aerial vehicle are obtained, the initial track of the unmanned aerial vehicle is set, and the resources of the unmanned aerial vehicle are distributed;
step two: optimizing a total target, a time-sensitive multitask multi-Internet-of-things equipment environment, an Internet-of-things equipment computing capacity, cache capacity, unmanned aerial vehicle computing capacity and cache capacity constraint conditions to establish an optimization model, and bringing each variable initial value into the optimization model;
step three: fixing an unmanned aerial vehicle resource allocation optimization sub-strategy and an Internet of things equipment unloading decision sub-strategy, obtaining an optimized unmanned aerial vehicle track by using the unmanned aerial vehicle track optimization sub-strategy, and updating the unmanned aerial vehicle track;
step four: fixing the flight path of the unmanned aerial vehicle, unloading the decision sub-strategy by the Internet of things equipment, optimizing the resource allocation of the unmanned aerial vehicle by using the resource allocation optimization sub-strategy of the unmanned aerial vehicle, and updating the resource allocation of the unmanned aerial vehicle;
step five: the unmanned aerial vehicle track optimization sub-strategy is fixed, and the unmanned aerial vehicle resource allocation optimization sub-strategy is used for optimizing the internet of things equipment unloading decision sub-strategy; using the internet of things device offload decision sub-policy, wherein a bnb algorithm is used to optimize task types in the internet of things device offload decision sub-policy;
step six: checking whether each strategy value meets the tolerance precision, and if not, executing the step three; and if the optimal quality value of the strategy is met, obtaining the optimal quality value of the strategy.
In the fifth step, the bnb algorithm is replaced by any one of a simulated annealing method, a genetic algorithm and a hill climbing algorithm.
The method comprises the following steps that the Internet of things equipment is respectively represented as follows:
Figure RE-GDA0002767955200000031
the unmanned aerial vehicle serves as a mobile edge computing server to provide computing resources and data caching service for the Internet of things equipment;
setting the UAV navigation period as T;
the UAV flight height is H, and the UAV starts from the initial point and returns to the initial point at the last moment;
the distance between the UAV and the internet of things device is expressed as:
Figure RE-GDA0002767955200000032
(qx[n]，qy[n]) Representing unmanned aerial vehicle in a two-dimensional coordinate planeCoordinate (x)k，yk) Respectively representing the coordinates of the kth Internet of things device on a two-dimensional coordinate plane;
each piece of Internet of things equipment is completed within T time by energy consumption and experiment sensitive tasks;
dividing the UAV navigation period into N time slices, wherein the size of each time slice is tau to T/N;
the task usage tuple of the kth internet-of-things device is represented as: { sk[n]，ak[n]，θk，lk[n]，tk[n]}；
Wherein s isk[n]Represents the total number of tasks;
ak[n]for indicating the task type, wherein ak[n]1 denotes that the task is a computational task, ak[n]0 indicates that the task is a cache type task;
θkrepresents the CPU cycle required for processing 1bit input data;
lk[n]∈[0，1]representing the proportion of the total mission offloaded to UAV processing, (1-l)k[n]) Representing processing locally;
tk[n]represents the maximum tolerated delay for the task;
using fkRepresenting the computing power of the kth internet of things device, the delay in the local computing mode is represented as:
Figure RE-GDA0002767955200000041
considering delay under offload mode including offload task upload link delay
Figure RE-GDA0002767955200000042
And unmanned aerial vehicle computing delay
Figure RE-GDA0002767955200000043
Then the delay in unloaded mode is expressed as:
Figure RE-GDA0002767955200000044
considering the energy consumption of the internet of things devices, including the energy consumption calculated locally and the energy consumption uploaded to the unmanned aerial vehicle process, the energy consumption of the kth internet of things device and the nth time slice is expressed as follows:
Figure RE-GDA0002767955200000045
the drone needs to compute the tasks that are offloaded to the drone by the internet of things device, so the drone computes energy consumption as:
Figure RE-GDA0002767955200000046
the drone needs to fly according to the flight path provided in the optimization strategy, using P0And PiRespectively representing the power of the fixed blade profile and the induced power in the hovering state, QtipRepresenting the tip speed, v, of the rotor blade0Representing the average rotor induction speed, d0Representing fuselage drag ratio, s representing rotor stability, ρ representing air density, a representing rotor disk area, and thus drone flight energy consumption is represented as:
Figure RE-GDA0002767955200000047
in the strategy of the invention, an unmanned aerial vehicle flight trajectory q, a bandwidth b, a computing capacity f, an Internet of things equipment task type a and an unloading proportion l are jointly optimized;
minimizing total energy consumption of the equipment of the Internet of things, and modeling a total optimization problem:
Figure RE-GDA0002767955200000051
Figure RE-GDA0002767955200000052
Figure RE-GDA0002767955200000053
Figure RE-GDA0002767955200000054
Figure RE-GDA0002767955200000055
Efly+Ecal≤E,
Figure RE-GDA0002767955200000056
q[0]＝q[N],
Figure RE-GDA0002767955200000057
Figure RE-GDA0002767955200000058
Figure RE-GDA0002767955200000059
Figure RE-GDA00027679552000000510
the trajectory optimization problem is solved after the resource allocation problem and the unloading decision problem are given:
Figure RE-GDA00027679552000000511
Efly+Ecal≤E,
Figure RE-GDA00027679552000000512
q[0]＝q[N],
Figure RE-GDA00027679552000000513
the track optimization sub-problem is a non-convex sub-problem, and auxiliary variables are introduced
Figure RE-GDA00027679552000000514
And
Figure RE-GDA0002767955200000061
solving the non-convex problem into a convex problem, wherein the simplified problem is as follows:
Figure RE-GDA0002767955200000062
q[0]＝q[N],
Figure RE-GDA0002767955200000063
Figure RE-GDA0002767955200000064
Figure RE-GDA0002767955200000065
Figure RE-GDA0002767955200000066
Figure RE-GDA0002767955200000067
problem reduction uses SCA technology to process the problem and obtain a variable solution;
the following algorithm was used to optimize the subproblems after transformation:
algorithm 1: trajectory optimization algorithm
Inputting: given an initial value qi,bi,fi,ai,liH, set i to 0
And (3) outputting: q. q.s*
Figure RE-GDA0002767955200000068
1: initialization:
Figure RE-GDA0002767955200000069
2: circulation of
3: value of a given variable
Figure RE-GDA0002767955200000071
Problem P is solvedT1 is optimized
Figure RE-GDA0002767955200000072
4 updating variable value qi+1[n]＝q*[n],oi+1[n]＝o*[n],vi+1[n]＝v*[n],γi+1[n]＝γ*[n]}
5: and exiting the loop when the variable value is converged to the tolerable precision.
The given UAV flight trajectory and resource allocation optimization sub-strategy after offloading decision solves the problem:
Figure RE-GDA0002767955200000073
Figure RE-GDA0002767955200000074
Figure RE-GDA0002767955200000075
Efly+Ecal≤E,
Figure RE-GDA0002767955200000076
Figure RE-GDA0002767955200000077
the resource allocation sub-problem is a non-convex sub-problem, and therefore an auxiliary variable is introduced
Figure RE-GDA0002767955200000078
Converting the above problem into an equivalent problem:
Figure RE-GDA0002767955200000081
Figure RE-GDA0002767955200000082
Figure RE-GDA0002767955200000083
Figure RE-GDA0002767955200000084
Figure RE-GDA0002767955200000085
Figure RE-GDA0002767955200000086
Figure RE-GDA0002767955200000087
Figure RE-GDA0002767955200000088
the above sub-problem is a convex problem, and the problem solution can be directly obtained using CVX.
And solving a resource optimization sub-problem by using a resource allocation optimization sub-strategy, wherein the resource optimization sub-problem is as follows:
Figure RE-GDA0002767955200000089
the above resource allocation sub-problem needs to be broken down into two sub-problems: fixing l to solve the integer programming subproblem of a, and fixing a to solve the linear programming subproblem of l; solving by using a branch-and-bound algorithm aiming at the integer programming problem; the algorithm flow for solving the problem is as follows:
and 2, algorithm: optimization strategy overall algorithm
Inputting: given an initial value qi,bi,fi,ai,liH, set i to 0
And (3) outputting: { q ] q*,b*,f*,a*,l*}
1: circulation of
2: given { qi,bi,fi,ai,liCalculating to obtain the unmanned aerial vehicle track q based on the algorithm 1i+1；
3: given an offload decision { ai,liAnd trace qi+1To solve P R1, calculating to obtain unmanned aerial vehicle resource allocation strategy { bi+1,fi+1}
Using the known { qi+1,bi+1,fi+1Solve POGet an offload decision { ai+1,li+1}
Updating variables: q. q.si＝qi+1,bi＝bi+1,fi＝fi+1,ai＝ai+1,li＝li+1
4: and exiting the loop when the variable value is converged to the tolerable precision.
The invention provides a computing and caching unloading strategy based on a time-sensitive multitask mobile edge computing network of an unmanned aerial vehicle, and the total energy consumption of IoT equipment is reduced to the greatest extent by comprehensively optimizing the flight trajectory of the unmanned aerial vehicle, the transmission bandwidth of the IoT equipment and the unmanned aerial vehicle, the computing resource allocation of the unmanned aerial vehicle, the unloading rate of the IoT equipment and the task type of the IoT equipment, so that the experience quality requirement of the IoT equipment on the time-sensitive tasks is met.
The present application is further explained below with reference to the drawings and the detailed description.
Drawings
FIG. 1 is a schematic flow chart of the corresponding steps of the present invention;
FIG. 2 is a schematic diagram of the model relationships of the present invention;
FIG. 3 is a schematic diagram illustrating different requirements of the trajectory of the UAV on the experience quality of the time-sensitive task device under different unloading schemes;
FIG. 4 is a schematic diagram illustrating total energy consumption of different numbers of Internet of things devices for simulating different offloading decisions;
fig. 5 is a schematic diagram illustrating the influence of the device service quality requirement of the time-sensitive task of the device of the internet of things on the total energy consumption of different quantities of devices of the internet of things.
Detailed Description
The invention discloses a time-sensitive multitask edge calculation and cache cooperation unloading strategy method, which comprises an unmanned aerial vehicle track optimization sub-strategy, an unmanned aerial vehicle resource allocation optimization sub-strategy and an Internet of things equipment unloading decision sub-strategy; iteratively operating the three sub-strategies by using a block coordinate reduction method, and obtaining a final value after a final strategy is converged;
the unmanned aerial vehicle trajectory optimization sub-strategy is used for optimizing the flight trajectory of the unmanned aerial vehicle under the condition that the unmanned aerial vehicle resource allocation optimization sub-strategy and the Internet of things equipment unloading decision sub-strategy are determined;
the unmanned aerial vehicle resource allocation optimization sub-strategy is used for solving the calculation resource allocation and bandwidth allocation of each time slice of the unmanned aerial vehicle for each Internet of things device by using a CVX technology through a conversion model under the condition that the unmanned aerial vehicle flight path and the Internet of things device unloading decision sub-strategy are fixed;
and the Internet of things equipment unloading decision sub-strategy is used for solving the task unloading proportion and the task type in each time slice of the Internet of things equipment under the condition of determining the flight path of the unmanned aerial vehicle and the resource allocation of the unmanned aerial vehicle, wherein the task type comprises a calculation task and a cache task.
The method comprises the following steps:
the method comprises the following steps: an initialization stage: in the stage, the total task volume, the cache capacity and the computing capacity of each piece of internet-of-things equipment are obtained, an internet-of-things equipment unloading decision sub-strategy is initialized, the cache capacity and the computing capacity of the unmanned aerial vehicle are obtained, the initial track of the unmanned aerial vehicle is set, and the resources of the unmanned aerial vehicle are distributed;
step two: optimizing a total target, a time-sensitive multitask multi-Internet-of-things equipment environment, an Internet-of-things equipment computing capacity, cache capacity, unmanned aerial vehicle computing capacity and cache capacity constraint conditions to establish an optimization model, and bringing each variable initial value into the optimization model;
step three: fixing an unmanned aerial vehicle resource allocation optimization sub-strategy and an Internet of things equipment unloading decision sub-strategy, obtaining an optimized unmanned aerial vehicle track by using the unmanned aerial vehicle track optimization sub-strategy, and updating the unmanned aerial vehicle track;
step four: fixing the flight path of the unmanned aerial vehicle, unloading the decision sub-strategy by the Internet of things equipment, optimizing the resource allocation of the unmanned aerial vehicle by using the resource allocation optimization sub-strategy of the unmanned aerial vehicle, and updating the resource allocation of the unmanned aerial vehicle;
step five: the unmanned aerial vehicle track optimization sub-strategy is fixed, and the unmanned aerial vehicle resource allocation optimization sub-strategy is used for optimizing the internet of things equipment unloading decision sub-strategy; using the internet of things device offload decision sub-policy, wherein a bnb algorithm is used to optimize task types in the internet of things device offload decision sub-policy;
step six: checking whether each strategy value meets the tolerance precision, and if not, executing the step three; and if the optimal quality value of the strategy is met, obtaining the optimal quality value of the strategy.
In the fifth step, the bnb algorithm is replaced by any one of a simulated annealing method, a genetic algorithm and a hill climbing algorithm.
The method comprises the following steps that the Internet of things equipment is respectively represented as follows:
Figure RE-GDA0002767955200000111
the unmanned aerial vehicle serves as a mobile edge computing server to provide computing resources and data caching service for the Internet of things equipment;
setting the UAV navigation period as T;
the UAV flight height is H, and the UAV starts from the initial point and returns to the initial point at the last moment;
the distance between the UAV and the internet of things device is expressed as:
Figure RE-GDA0002767955200000112
(qx[n]，qy[n]) Representing the coordinates of the unmanned plane in a two-dimensional coordinate plane, (x)k，yk) Respectively representing the coordinates of the kth Internet of things device on a two-dimensional coordinate plane;
each piece of Internet of things equipment is completed within T time by energy consumption and experiment sensitive tasks;
dividing the UAV navigation period into N time slices, wherein the size of each time slice is tau to T/N;
the task usage tuple of the kth internet-of-things device is represented as: { sk[n]，ak[n]，θk，lk[n]，tk[n]}；
Wherein s isk[n]Represents the total number of tasks;
ak[n]for indicating the task type, wherein ak[n]1 denotes that the task is a computational task, ak[n]0 indicates that the task is a cache type task;
θkrepresents the CPU cycle required for processing 1bit input data;
lk[n]∈[0，1]representing the proportion of the total mission offloaded to UAV processing, (1-l)k[n]) Representing processing locally;
tk[n]represents the maximum tolerated delay for the task;
using fkRepresenting the computing power of the kth internet of things device, the delay in the local computing mode is represented as:
Figure RE-GDA0002767955200000121
considering delay under offload mode including offload task upload link delay
Figure RE-GDA0002767955200000122
And unmanned aerial vehicle computing delay
Figure RE-GDA0002767955200000123
Then the delay in unloaded mode is expressed as:
Figure RE-GDA0002767955200000124
considering the energy consumption of the internet of things devices, including the energy consumption calculated locally and the energy consumption uploaded to the unmanned aerial vehicle process, the energy consumption of the kth internet of things device and the nth time slice is expressed as follows:
Figure RE-GDA0002767955200000125
the drone needs to compute the tasks that are offloaded to the drone by the internet of things device, so the drone computes energy consumption as:
Figure RE-GDA0002767955200000126
the drone needs to fly according to the flight path provided in the optimization strategy, using P0And PiRespectively representing the power of the fixed blade profile and the induced power in the hovering state, QtipRepresenting the tip speed, v, of the rotor blade0Representing the average rotor induction speed, d0Representing fuselage drag ratio, s representing rotor stability, ρ representing air density, a representing rotor disk area, and thus drone flight energy consumption is represented as:
Figure RE-GDA0002767955200000131
in the strategy of the invention, an unmanned aerial vehicle flight trajectory q, a bandwidth b, a computing capacity f, an Internet of things equipment task type a and an unloading proportion l are jointly optimized;
minimizing total energy consumption of the equipment of the Internet of things, and modeling a total optimization problem:
Figure RE-GDA0002767955200000132
Figure RE-GDA0002767955200000133
Figure RE-GDA0002767955200000134
Figure RE-GDA0002767955200000135
Figure RE-GDA0002767955200000136
Efly+Ecal≤E,
Figure RE-GDA0002767955200000137
q[0]＝q[N],
Figure RE-GDA0002767955200000138
Figure RE-GDA0002767955200000139
Figure RE-GDA00027679552000001310
Figure RE-GDA00027679552000001311
the trajectory optimization problem is solved after the resource allocation problem and the unloading decision problem are given:
Figure RE-GDA00027679552000001312
Efly+Ecal≤E,
Figure RE-GDA00027679552000001313
q[0]＝q[N],
Figure RE-GDA00027679552000001314
the track optimization sub-problem is a non-convex sub-problem, and auxiliary variables are introduced
Figure RE-GDA0002767955200000141
And
Figure RE-GDA0002767955200000142
solving the non-convex problem into a convex problem, wherein the simplified problem is as follows:
Figure RE-GDA0002767955200000143
q[0]＝q[N],
Figure RE-GDA0002767955200000144
Figure RE-GDA0002767955200000145
Figure RE-GDA0002767955200000146
Figure RE-GDA0002767955200000147
Figure RE-GDA0002767955200000148
problem reduction uses SCA technology to process the problem and obtain a variable solution;
the following algorithm was used to optimize the subproblems after transformation:
algorithm 1: trajectory optimization algorithm
Inputting: given an initial value qi,bi,fi,ai,liH, set i to 0
And (3) outputting: p is a radical of*
1: initialization:
Figure RE-GDA0002767955200000151
Figure RE-GDA0002767955200000152
2: circulation of
3: value of a given variable
Figure RE-GDA0002767955200000159
Problem P is solvedT1 is optimized
Figure RE-GDA0002767955200000153
4 updating variable value qi+1[n]＝q*[n],oi+1[n]＝o*[n],vi+1[n]＝v*[n],γi+1[n]＝γ*[n]}
5: and exiting the loop when the variable value is converged to the tolerable precision.
The given UAV flight trajectory and resource allocation optimization sub-strategy after offloading decision solves the problem:
Figure RE-GDA0002767955200000154
Figure RE-GDA0002767955200000155
Figure RE-GDA0002767955200000156
Efly+Ecal≤E,
Figure RE-GDA0002767955200000157
Figure RE-GDA0002767955200000158
the resource allocation sub-problem is a non-convex sub-problem, and therefore an auxiliary variable is introduced
Figure RE-GDA0002767955200000161
Converting the above problem into an equivalent problem:
Figure RE-GDA0002767955200000162
Figure RE-GDA0002767955200000163
Figure RE-GDA0002767955200000164
Figure RE-GDA0002767955200000165
Figure RE-GDA0002767955200000166
Figure RE-GDA0002767955200000167
Figure RE-GDA0002767955200000168
Figure RE-GDA0002767955200000169
the above sub-problem is a convex problem, and the problem solution can be directly obtained using CVX.
And solving a resource optimization sub-problem by using a resource allocation optimization sub-strategy, wherein the resource optimization sub-problem is as follows:
Figure RE-GDA00027679552000001610
the above resource allocation sub-problem needs to be broken down into two sub-problems: fixing l to solve the integer programming subproblem of a, and fixing a to solve the linear programming subproblem of l; solving by using a branch-and-bound algorithm aiming at the integer programming problem; the algorithm flow for solving the problem is as follows:
and 2, algorithm: optimization strategy overall algorithm
Inputting: given an initial value qi,bi,fi,ai,liH, set i to 0
And (3) outputting: { q ] q*,b*,f*,a*,l*}
1: circulation of
2: given { qi,bi,fi,ai,liCalculating to obtain the unmanned aerial vehicle track q based on the algorithm 1i+1；
3: given an offload decision { ai,liAnd trace qi+1To solve P R1, calculating to obtain unmanned aerial vehicle resource allocation strategy { bi+1,fi+1}
Using the known { qi+1,bi+1,fi+1Solve POGet an offload decision { ai+1,li+1}
Updating variables: q. q.si＝qi+1,bi＝bi+1,fi＝fi+1,ai＝ai+1,li＝li+1
4: and exiting the loop when the variable value is converged to the tolerable precision.
The model of the invention is shown in figure 2: unmanned aerial vehicle energy multi-internet-of-things based on frequency division multiple access, wherein internet-of-things equipment is respectively represented as:
Figure RE-GDA0002767955200000171
the unmanned aerial vehicle serves as a mobile edge computing server to provide computing resources and data caching service for the Internet of things equipment. And setting the UAV navigation period as T. And the UAV flight height is H, and the UAV starts from the initial point and returns to the initial point at the last moment.
Fig. 3 simulates different requirements of the trajectory of the drone for the quality of experience of the time-sensitive task device under different offloading schemes. When t is 0.5s, the navigation distance of unmanned aerial vehicle under the scheme of cache uninstallation is relatively nearer to thing networking device, in the scheme of uninstallation in coordination, along with the gradual reduction of quality of experience requirement of equipment, unmanned aerial vehicle flies to the distance of thing networking device more and more closely. Because the experiment requirement is lower, therefore unmanned aerial vehicle can provide more services for thing networking equipment through approaching thing networking equipment, allows thing networking equipment to upload more calculation and cache tasks. While once the UAV finds the appropriate location shown by the green line (gray cross lines between X-axes 0-20), the UAV will hover at that location for multiple timeslices.
Fig. 4 simulates total energy consumption of different numbers of internet of things devices for different offloading decisions. Under any strategy, the consumption of the internet of things equipment can rise as the number of the internet of things equipment rises. Due to the fact that energy consumption and operation of computing and caching tasks are different, compared with a cooperative unloading and caching unloading scheme, the internet of things equipment in the computing unloading mode consumes more energy, and all tasks are designed for computing and consume more energy than caching. At the same time, the cache offload mode is the most energy efficient because all tasks are for caching and therefore consume less energy than other modes. Note that the cooperative offloading scheme not only meets the requirements of different types of tasks, but also obtains a more satisfactory result
Fig. 5 simulates the impact of the quality of service requirements of the internet of things device time sensitive tasks on the total energy consumption of different quantities of internet of things devices. In fig. 5 it is reflected that the total energy consumption decreases with decreasing service quality of the time sensitive task equipment. Because of the low quality of service requirements, the UAV has enough time to get close to the internet of things device and thus reduces latency and provides a better communication interface and thus reduces overall energy consumption.
Figure RE-GDA0002767955200000181
The above description is only a part of specific embodiments of the present invention (since the technical solution of the present invention relates to the numerical range, the embodiments are not exhaustive, the protection scope described in the present invention is subject to the numerical range and other technical essential ranges), and the specific contents or common general knowledge in the technical solution are not described too much here. It should be noted that the above-mentioned embodiments do not limit the present invention in any way, and all technical solutions obtained by means of equivalent substitution or equivalent transformation for those skilled in the art are within the protection scope of the present invention. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.

Claims (7)

1. The method is characterized by comprising an unmanned aerial vehicle track optimization sub-strategy, an unmanned aerial vehicle resource allocation optimization sub-strategy and an Internet of things equipment unloading decision sub-strategy; iteratively operating the three sub-strategies by using a block coordinate reduction method, and obtaining a final value after a final strategy is converged;
the unmanned aerial vehicle trajectory optimization sub-strategy is used for optimizing the flight trajectory of the unmanned aerial vehicle under the condition that the unmanned aerial vehicle resource allocation optimization sub-strategy and the Internet of things equipment unloading decision sub-strategy are determined;
the unmanned aerial vehicle resource allocation optimization sub-strategy is used for solving the calculation resource allocation and bandwidth allocation of each time slice of the unmanned aerial vehicle for each Internet of things device by using a CVX technology through a conversion model under the condition that the unmanned aerial vehicle flight path and the Internet of things device unloading decision sub-strategy are fixed;
and the Internet of things equipment unloading decision sub-strategy is used for solving the task unloading proportion and the task type in each time slice of the Internet of things equipment under the condition of determining the flight path of the unmanned aerial vehicle and the resource allocation of the unmanned aerial vehicle, wherein the task type comprises a calculation task and a cache task.
2. Method according to claim 1, characterized in that the method comprises the steps of:
the method comprises the following steps: an initialization stage: in the stage, the total task volume, the cache capacity and the computing capacity of each piece of internet-of-things equipment are obtained, an internet-of-things equipment unloading decision sub-strategy is initialized, the cache capacity and the computing capacity of the unmanned aerial vehicle are obtained, the initial track of the unmanned aerial vehicle is set, and the resources of the unmanned aerial vehicle are distributed;
step two: optimizing a total target, a time-sensitive multitask multi-Internet-of-things equipment environment, an Internet-of-things equipment computing capacity, cache capacity, unmanned aerial vehicle computing capacity and cache capacity constraint conditions to establish an optimization model, and bringing each variable initial value into the optimization model;
step three: fixing an unmanned aerial vehicle resource allocation optimization sub-strategy and an Internet of things equipment unloading decision sub-strategy, obtaining an optimized unmanned aerial vehicle track by using the unmanned aerial vehicle track optimization sub-strategy, and updating the unmanned aerial vehicle track;
step four: fixing the flight path of the unmanned aerial vehicle, unloading the decision sub-strategy by the Internet of things equipment, optimizing the resource allocation of the unmanned aerial vehicle by using the resource allocation optimization sub-strategy of the unmanned aerial vehicle, and updating the resource allocation of the unmanned aerial vehicle;
step five: the unmanned aerial vehicle track optimization sub-strategy is fixed, and the unmanned aerial vehicle resource allocation optimization sub-strategy is used for optimizing the internet of things equipment unloading decision sub-strategy; using the internet of things device offload decision sub-policy, wherein a bnb algorithm is used to optimize task types in the internet of things device offload decision sub-policy;
step six: checking whether each strategy value meets the tolerance precision, and if not, executing the step three; and if the optimal value of the strategy is met, obtaining the optimal value of the strategy.
3. The method of claim 1, wherein in the fifth step, the bnb algorithm is replaced by any one of a simulated annealing method, a genetic algorithm and a hill climbing method algorithm.
4. The method of claim 2, wherein the step of removing the metal oxide layer comprises removing the metal oxide layer from the metal oxide layerIn the method, the internet of things devices are respectively represented as follows:
Figure RE-FDA0002767955190000021
the unmanned aerial vehicle serves as a mobile edge computing server to provide computing resources and data caching service for the Internet of things equipment;
setting the UAV navigation period as T;
the UAV flight height is H, and the UAV starts from the initial point and returns to the initial point at the last moment;
the distance between the UAV and the internet of things device is expressed as:
Figure RE-FDA0002767955190000022
(qx[n]，qy[n]) Representing the coordinates of the unmanned plane in a two-dimensional coordinate plane, (x)k，yk) Respectively representing the coordinates of the kth Internet of things device on a two-dimensional coordinate plane;
each piece of Internet of things equipment is completed within T time by energy consumption and experiment sensitive tasks;
dividing the UAV navigation period into N time slices, wherein the size of each time slice is tau to T/N;
the task usage tuple of the kth internet-of-things device is represented as: { sk[n]，ak[n]，θk，lk[n]，tk[n]}；
Wherein s isk[n]Represents the total number of tasks;
ak[n]for indicating the task type, wherein ak[n]1 denotes that the task is a computational task, ak[n]0 indicates that the task is a cache type task;
θkrepresents the CPU cycle required for processing 1bit input data;
lk[n]∈[0，1]representing the proportion of the total mission offloaded to UAV processing, (1-l)k[n]) Representing processing locally;
tk[n]indicating the maximum tolerance of the taskDelaying;
using fkRepresenting the computing power of the kth internet of things device, the delay in the local computing mode is represented as:
Figure RE-FDA0002767955190000031
considering delay under offload mode including offload task upload link delay
Figure RE-FDA0002767955190000032
And unmanned aerial vehicle computing delay
Figure RE-FDA0002767955190000033
Then the delay in unloaded mode is expressed as:
Figure RE-FDA0002767955190000034
considering the energy consumption of the internet of things devices, including the energy consumption calculated locally and the energy consumption uploaded to the unmanned aerial vehicle process, the energy consumption of the kth internet of things device and the nth time slice is expressed as follows:
Figure RE-FDA0002767955190000035
the drone needs to compute the tasks that are offloaded to the drone by the internet of things device, so the drone computes energy consumption as:
Figure RE-FDA0002767955190000036
the drone needs to fly according to the flight path provided in the optimization strategy, using P0And PiRespectively representing the power of the fixed blade profile and the induced power in the hovering state, QtipRepresenting tip speed of rotor blade，v0Representing the average rotor induction speed, d0Representing fuselage drag ratio, s representing rotor stability, ρ representing air density, a representing rotor disk area, and thus drone flight energy consumption is represented as:
Figure RE-FDA0002767955190000037
in the strategy of the invention, an unmanned aerial vehicle flight trajectory q, a bandwidth b, a computing capacity f, an Internet of things equipment task type a and an unloading proportion l are jointly optimized;
minimizing total energy consumption of the equipment of the Internet of things, and modeling a total optimization problem:
Figure RE-FDA0002767955190000041
Figure RE-FDA0002767955190000042
Figure RE-FDA0002767955190000043
Figure RE-FDA0002767955190000044
Figure RE-FDA0002767955190000045
Efly+Ecal≤E,
Figure RE-FDA0002767955190000046
q[0]＝q[N],
Figure RE-FDA0002767955190000047
Figure RE-FDA0002767955190000048
Figure RE-FDA0002767955190000049
Figure RE-FDA00027679551900000410
5. the method of claim 4, wherein the method is specifically configured to solve the trajectory optimization problem given the resource allocation problem and the offload decision problem:
Figure RE-FDA00027679551900000411
Efly+Ecal≤E,
Figure RE-FDA00027679551900000412
q[0]＝q[N],
Figure RE-FDA00027679551900000413
the track optimization sub-problem is a non-convex sub-problem, and auxiliary variables are introduced
Figure RE-FDA0002767955190000051
And
Figure RE-FDA0002767955190000052
solving the non-convex problem into a convex problem, wherein the simplified problem is as follows:
Figure RE-FDA0002767955190000053
q[0]＝q[N],
Figure RE-FDA0002767955190000054
Figure RE-FDA0002767955190000055
Figure RE-FDA0002767955190000056
Figure RE-FDA0002767955190000057
Figure RE-FDA0002767955190000058
problem reduction uses SCA technology to process the problem and obtain a variable solution;
the following algorithm was used to optimize the subproblems after transformation:
algorithm 1: trajectory optimization algorithm
Inputting: given an initial value qi,bi,fi,ai,liH, set i to 0
And (3) outputting: q. q.s*
1: initialization:
Figure RE-FDA0002767955190000061
Figure RE-FDA0002767955190000062
2: circulation of
3: value of a given variable
Figure RE-FDA0002767955190000063
Problem P is solvedT1 is optimized
Figure RE-FDA0002767955190000064
4 updating variable value qi+1[n]＝q*[n],oi+1[n]＝o*[n],vi+1[n]＝v*[n],γi+1[n]＝γ*[n]}
5: and exiting the loop when the variable value is converged to the tolerable precision.
6. The method of claim 5, wherein the method is specific to the given UAV flight trajectory and post-offload decision resource allocation optimization sub-strategy solving the problem:
Figure RE-FDA0002767955190000065
Figure RE-FDA0002767955190000066
Figure RE-FDA0002767955190000067
Efly+Ecal≤E,
Figure RE-FDA0002767955190000068
Figure RE-FDA0002767955190000069
the resource allocation sub-problem is a non-convex sub-problem, and therefore an auxiliary variable is introduced
Figure RE-FDA0002767955190000071
Converting the above problem into an equivalent problem:
Figure RE-FDA0002767955190000072
Figure RE-FDA0002767955190000073
Figure RE-FDA0002767955190000074
Figure RE-FDA0002767955190000075
Figure RE-FDA0002767955190000076
Figure RE-FDA0002767955190000077
Figure RE-FDA0002767955190000078
Figure RE-FDA0002767955190000079
the above sub-problem is a convex problem, and the problem solution can be directly obtained using CVX.
7. The method according to claim 6, wherein the method is specifically configured to solve the resource optimization sub-problem using a resource allocation optimization sub-policy, the resource optimization sub-problem being:
Figure RE-FDA0002767955190000081
the above resource allocation sub-problem needs to be broken down into two sub-problems: fixing l to solve the integer programming subproblem of a, and fixing a to solve the linear programming subproblem of l; solving by using a branch-and-bound algorithm aiming at the integer programming problem; the algorithm flow for solving the problem is as follows:
and 2, algorithm: optimization strategy overall algorithm
Inputting: given an initial value qi,bi,fi,ai,liH, set i to 0
And (3) outputting: { q ] q*,b*,f*,a*,l*}
1: circulation of
2: given { qi,bi,fi,ai,liCalculating to obtain the unmanned aerial vehicle track q based on the algorithm 1i+1；
3: given an offload decision { ai,liAnd trace qi+1To solve PR1, calculating to obtain unmanned aerial vehicle resource allocation strategy { bi+1,fi+1}
Using the known { qi+1,bi+1,fi+1Solve POGet an offload decision { ai+1,li+1}
Updating variables: q. q.si＝qi+1,bi＝bi+1,fi＝fi+1,ai＝ai+1,li＝li+1
4: and exiting the loop when the variable value is converged to the tolerable precision.
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