CN114268966B - Unmanned aerial vehicle auxiliary MEC network low-delay multi-task allocation method and system - Google Patents
Unmanned aerial vehicle auxiliary MEC network low-delay multi-task allocation method and system Download PDFInfo
- Publication number
- CN114268966B CN114268966B CN202111612235.2A CN202111612235A CN114268966B CN 114268966 B CN114268966 B CN 114268966B CN 202111612235 A CN202111612235 A CN 202111612235A CN 114268966 B CN114268966 B CN 114268966B
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- user
- aerial vehicle
- task
- optimization model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000005457 optimization Methods 0.000 claims abstract description 42
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 4
- 238000011084 recovery Methods 0.000 claims description 4
- 238000002922 simulated annealing Methods 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 abstract description 3
- 238000013468 resource allocation Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000010432 diamond Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Mobile Radio Communication Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a low-delay multi-task allocation method and system for an unmanned aerial vehicle auxiliary MEC network. The method comprises the following steps: s1, acquiring basic information of a plurality of users and a plurality of unmanned aerial vehicles, wherein the basic information of the users comprises user position information and task unloading information required to be unloaded, and the basic information of the unmanned aerial vehicles comprises unmanned aerial vehicle position information; s2, establishing a system optimization model taking minimum user task unloading time delay as a target and taking allocation decision and unmanned aerial vehicle capacity as constraints; s3, introducing a relaxation variable into the system optimization model to obtain a linear system optimization model; and S4, solving the linear system optimization model based on the basic information of the unmanned aerial vehicle and the basic information of the user to obtain a task allocation scheme. The invention optimizes the resource allocation, realizes the minimization of time delay, and the optimization target only depends on the mutual distance between the unmanned aerial vehicle and the user, and the parameter is available in the initialization stage, is easy to realize and has simple task allocation algorithm.
Description
Technical Field
The invention belongs to the technical field of information and communication engineering, and particularly relates to a low-delay multi-task allocation method and system for an unmanned aerial vehicle auxiliary MEC network.
Background
The advent of the internet of things has led to the widespread use of more and more mobile applications, but the computing power and limited battery life of the internet of things have greatly limited the quality of service of these applications. The Mobile Edge Computing (MEC) enables computing resources to be closer to the Internet of things, so that a mobile user can offload partial or complete computation-intensive tasks to an MEC server for computing, and the method has the characteristics of low time delay, large bandwidth, high reliability and the like. Compared with the traditional ground MEC network, the unmanned aerial vehicle carrying MEC can be flexibly deployed under most conditions, for example, in the complex terrain where the ground MEC network such as the wild, the desert and the like cannot be conveniently and reliably established, and the defect of the traditional MEC technology in flexibility is overcome.
Unmanned aerial vehicle assisted communication has been widely studied, such as using unmanned aerial vehicles as mobile edge servers to provide uplink/downlink information services and computing services for ground users. At present, aiming at the allocation processing of user tasks, a large number of students study single task allocation of multiple users under the coverage of an unmanned plane, the task sizes are the same, but in a real scene, the user tasks are usually different in size, a plurality of parallel tasks exist, and how to allocate computing resources between a plurality of MEC servers and the multiple user tasks becomes a problem to be solved urgently.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides the unmanned aerial vehicle auxiliary MEC network low-delay multi-task allocation method and system, the algorithm for realizing task allocation is simple and extensible, the optimization target only depends on the mutual distance between the unmanned aerial vehicle and the user, and the parameter is available in the initialization stage and is easy to realize.
The invention adopts the following technical scheme:
a low-latency multi-task allocation method for an unmanned aerial vehicle auxiliary MEC network comprises the following steps:
s1, acquiring basic information of a plurality of users and a plurality of unmanned aerial vehicles, wherein the basic information of the users comprises user position information and task unloading information required to be unloaded, and the basic information of the unmanned aerial vehicles comprises unmanned aerial vehicle position information;
s2, establishing a system optimization model taking minimum user task unloading time delay as a target and taking allocation decision and unmanned aerial vehicle capacity as constraints;
s3, introducing a relaxation variable into the system optimization model to obtain a linear system optimization model;
and S4, solving the linear system optimization model based on the basic information of the unmanned aerial vehicle and the basic information of the user to obtain a task allocation scheme.
Preferably, in step S2, the allocation decision constraint is: each task can only be allocated to one unmanned aerial vehicle, expressed as:
wherein x is knm ∈{0,1}，x knm =1 denotes the nth task f of the kth user nk Distributed to unmanned plane m, x knm =0 denotes the nth task f of the kth user nk At the local processing, n= {1,2,3,..once, N }, k= {1,2,3,..once, K }, N represents the total number of tasks for the respective user, K represents the total number of users, m= {1,2,3,..once, M }, M represents the total number of drones.
As a preferred solution, in step S2, the unmanned aerial vehicle capacity constraint includes: the task capacity allocated to the respective drone cannot exceed the maximum storage capacity of the respective drone, expressed as:
wherein Q is m Representing the maximum storage capacity of the unmanned aerial vehicle m;
unmanned aerial vehicle capacity constraints also include: maintaining the corresponding unmanned aerial vehicle receiving task amount at Q min And Q is equal to max Between Q min Minimum capacity limit representing total amount of received tasks for a single drone, Q max Maximum capacity limit representing total amount of received tasks for a single drone, and Q min <Q max ≤Q m 。
In a preferred embodiment, in step S2, the user task offloading delay is calculated as follows:
A. calculating channel gain between the user and the unmanned aerial vehicle based on the user position information and the unmanned aerial vehicle position information;
B. calculating a channel capacity between the user and the unmanned aerial vehicle based on the channel gain;
C. and calculating based on the channel capacity to obtain the user task unloading time delay.
In the preferred scheme, in the step a, the calculation formula of the channel gain g (m, k) between the unmanned aerial vehicle m and the user k is as follows:
wherein beta is 0 Represents the channel gain at a reference distance of 1m, d (m, k) represents the distance between the drone m and the user k, x m Represents the abscissa of the unmanned plane m, y m Represents the ordinate of the unmanned plane m, H represents the height of the unmanned plane m, and x k Representing the abscissa of user k, y k Representing the ordinate of user k;
in step B, the channel capacity R between user k and drone m km The calculation formula of (2) is as follows:
wherein B represents channel bandwidth, P m Representing the transmit power, σ, of user k 2 Representing gaussian white noise variance;
in step C, the nth task of user k is assigned to the task offloading delay t of unmanned plane m knm The calculation formula is as follows:
preferably, in step S2, the system optimization model is expressed as:
s.t.
x knm ∈{0,1}
in a preferred embodiment, in step S3, a 0-1 relaxation variable is introduced into the system optimization model to obtain a linear system optimization model, expressed as:
s.t.
preferably, step S4 includes the steps of:
s4.1, solving a linear system optimization model by adopting a simulated annealing method;
s4.2, performing discrete binary recovery based on a result obtained by solving, wherein the recovery rule is as follows:
preferably, the steps between the step S3 and the step S4 further include:
and creating virtual users with the same positions as the users according to the number of the user-required off-tasks, and distributing the user-required off-tasks to the virtual users so that each user distributes a required off-task.
The unmanned aerial vehicle auxiliary MEC network low-delay multi-task distribution system comprises an information acquisition module, a model building module and a solving module, wherein the model building module comprises a connected model building unit and a linear unit, and the information acquisition module, the solving module and the linear unit are sequentially connected;
the information acquisition module is used for acquiring basic information of a user and the unmanned aerial vehicle, wherein the basic information of the user comprises user position information and task unloading information required to be unloaded, and the basic information of the unmanned aerial vehicle comprises unmanned aerial vehicle position information;
the model building unit is used for building a system optimization model which aims at minimizing the user task unloading time delay and takes allocation decision and unmanned aerial vehicle capacity as constraints;
the linear unit is used for introducing a relaxation variable into the system optimization model to obtain a linear system optimization model;
and the solving module is used for solving the linear system optimization model based on the basic information of the unmanned aerial vehicle and the basic information of the user so as to obtain a task allocation scheme.
The beneficial effects of the invention are as follows:
the invention performs task allocation for the multi-task between the multi-server and the multi-user of the MEC system based on the unmanned plane, optimizes the resource allocation and realizes the minimization of time delay. And moreover, the task allocation algorithm is simple and extensible, the optimization target only depends on the mutual distance between the unmanned aerial vehicle and the user, and the parameter is available in the initialization stage and is easy to realize.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a low latency multi-task allocation method for an unmanned aerial vehicle assisted MEC network according to the present invention;
FIG. 2 is a schematic diagram of a two-tier network of multiple users and multiple drones;
FIG. 3 is a schematic diagram of a user's distribution with a drone;
FIG. 4 is a simulation diagram of task load and task load latency under different methods;
fig. 5 is a schematic structural diagram of a low latency multi-task distribution system for an unmanned aerial vehicle assisted MEC network according to the present invention.
Detailed Description
The following specific examples are presented to illustrate the present invention, and those skilled in the art will readily appreciate the additional advantages and capabilities of the present invention as disclosed herein. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
Embodiment one:
referring to fig. 1, the embodiment provides a low-latency multi-task allocation method for an unmanned aerial vehicle auxiliary MEC network, which includes the steps of:
s1, acquiring basic information of a plurality of users and a plurality of unmanned aerial vehicles, wherein the basic information of the users comprises user position information and task unloading information required to be unloaded, and the basic information of the unmanned aerial vehicles comprises unmanned aerial vehicle position information;
s2, establishing a system optimization model taking minimum user task unloading time delay as a target and taking allocation decision and unmanned aerial vehicle capacity as constraints;
s3, introducing a relaxation variable into the system optimization model to obtain a linear system optimization model;
and S4, solving the linear system optimization model based on the basic information of the unmanned aerial vehicle and the basic information of the user to obtain a task allocation scheme.
Specifically:
the present embodiment considers a two-layer network of multiple users and multiple unmanned aerial vehicles as shown with reference to fig. 2, with the unmanned aerial vehicle position set to [ x ] m ,y m ,H]Wherein M e M, m= {1,2,3, M } is the mth unmanned aerial vehicle, M is the total number of unmanned aerial vehicles, x m Is the abscissa of unmanned plane m, y m The vertical coordinate is H, and the height of the unmanned aerial vehicle is H; the user position is set to [ x ] k ,y k ,0]Where K e K, k= {1,2,3,... As shown in fig. 3, filled squares, diamonds and regular triangles respectively represent deployed positions of three unmanned aerial vehicles, the coverage area of the three unmanned aerial vehicles is shown as a large circle in the figure, small triangles represent users in the coverage area, the user distribution is subjected to poisson point distribution, and the unmanned aerial vehicles are deployed at the optimal positions. Each user has N tasks f of unequal sizes nk N= {1,2,3,.. k= {1,2,3, -, K, representing the nth task of user K.
In step S2, the allocation decision constraint is: each task can only be allocated to one unmanned aerial vehicle, expressed as:
wherein x is knm ∈{0,1}，x knm =1 denotes the nth task f of the kth user nk Distributed to unmanned plane m, x knm =0 denotes the nth task f of the kth user nk At the local processing, n= {1,2,3,..once, N }, k= {1,2,3,..once, K }, N represents the total number of tasks for the respective user, K represents the total number of users, m= {1,2,3,..once, M }, M represents the total number of drones.
In step S2, the unmanned aerial vehicle capacity constraint includes: the task capacity allocated to the respective drone cannot exceed the maximum storage capacity of the respective drone, expressed as:
wherein Q is m Representing the maximum storage capacity of the unmanned aerial vehicle m;
in order to guarantee unmanned aerial vehicle load balancing, unmanned aerial vehicle capacity constraint still includes: maintaining the corresponding unmanned aerial vehicle receiving task amount at Q min And Q is equal to max Between Q min Minimum capacity limit representing total amount of received tasks for a single drone, Q max Maximum capacity limit representing total amount of received tasks for a single drone, and Q min <Q max ≤Q m 。
In step S2, the user task offloading delay is calculated as follows:
A. calculating channel gain between the user and the unmanned aerial vehicle based on the user position information and the unmanned aerial vehicle position information;
B. calculating a channel capacity between the user and the unmanned aerial vehicle based on the channel gain;
C. and calculating based on the channel capacity to obtain the user task unloading time delay.
In step a, the channel between the user and the unmanned plane obeys a line-of-sight wireless transmission (LoS) channel model, so the calculation formula of the channel gain g (m, k) between the unmanned plane m and the user k is:
wherein beta is 0 Represents the channel gain at a reference distance of 1m, d (m, k) represents the distance between the drone m and the user k, x m Represents the abscissa of the unmanned plane m, y m Represents the ordinate of the unmanned plane m, H represents the height of the unmanned plane m, and x k Representing the abscissa of user k, y k Representing the ordinate of user k;
in step B, the channel capacity R between user k and drone m km The calculation formula of (2) is as follows:
wherein B represents channel bandwidth, P m Representing the transmit power, σ, of user k 2 Representing gaussian white noise variance;
in step C, the nth task of user k is assigned to the task offloading delay t of unmanned plane m knm The calculation formula is as follows:
in summary, in step S2, the system optimization model is expressed as:
s.t.
x knm ∈{0,1}
further, for x knm After introducing the relaxation variables, e {0,1} the overall optimization problem is converted into a linear programming problem to obtain a linear system optimization model expressed as:
s.t.
further, solve for x knm Thereafter, the discrete binary x is restored using the following rule knm ：
According to the invention, a simulated annealing method (SAA) is adopted when solving, compared with a Hungary method (Hungary), the method aims at minimizing the total time delay of the system, and the task size and the unmanned aerial vehicle capacity are used as constraints, so that the total time delay of task transmission is minimized under the condition that the size of the unmanned aerial vehicle receiving task is within a certain range. When a new solution is generated, the SAA method receives a solution worse than the current solution with a certain probability, so that a local optimal solution may be skipped, thereby obtaining a global optimal solution, and the Hungary may be trapped in the local optimal solution. Fig. 4 simulates the relationship between the task unloading amount and the task unloading delay, and as can be seen from fig. 4, when the amount of the received tasks of the unmanned aerial vehicle increases, compared with the traditional huntary allocation method, the method (SAA) adopted by the invention has smaller task unloading delay, and both the task transmission delay minimization and the task load balancing are considered. Simulation results prove that the method (SAA) adopted by the invention is superior to a Hungary method for a multi-user multi-task allocation scheme.
In a specific embodiment, the steps between the step S3 and the step S4 may further include:
and creating virtual users with the same positions as the users according to the number of the user-required off-tasks, and distributing the user-required off-tasks to the virtual users so that each user distributes a required off-task. In order to solve the problem of matching between users and tasks, virtual users are created according to the number of user tasks, for a single user, if the single user has N tasks, N-1 virtual users are created, the positions of the virtual users are the same as the positions of the user, namely N tasks of the user are distributed to N users with the same positions. The problem of multiple tasks of one user is converted into the problem of corresponding one task of one user.
Embodiment two:
referring to fig. 5, the embodiment provides an unmanned aerial vehicle auxiliary MEC network low-latency multi-task allocation system, which is based on the unmanned aerial vehicle auxiliary MEC network low-latency multi-task allocation method described in the embodiment one, and includes an information acquisition module, a model building module and a solving module, wherein the model building module includes a connected model building unit and a linear unit, and the information acquisition module, the solving module and the linear unit are connected in sequence;
the information acquisition module is used for acquiring basic information of a user and the unmanned aerial vehicle, wherein the basic information of the user comprises user position information and task unloading information required to be unloaded, and the basic information of the unmanned aerial vehicle comprises unmanned aerial vehicle position information;
the model building unit is used for building a system optimization model which aims at minimizing the user task unloading time delay and takes allocation decision and unmanned aerial vehicle capacity as constraints;
the linear unit is used for introducing a relaxation variable into the system optimization model to obtain a linear system optimization model;
and the solving module is used for solving the linear system optimization model based on the basic information of the unmanned aerial vehicle and the basic information of the user so as to obtain a task allocation scheme.
It should be noted that, in the low-latency multi-task allocation system for an unmanned aerial vehicle auxiliary MEC network provided in this embodiment, similar to the embodiment, a detailed description is omitted here.
The above examples are merely illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the protection scope of the present invention without departing from the design spirit of the present invention.
Claims (5)
1. The unmanned aerial vehicle assisted MEC network low-latency multi-task allocation method is characterized by comprising the following steps of:
s1, acquiring basic information of a plurality of users and a plurality of unmanned aerial vehicles, wherein the basic information of the users comprises user position information and task unloading information required to be unloaded, and the basic information of the unmanned aerial vehicles comprises unmanned aerial vehicle position information;
s2, establishing a system optimization model taking minimum user task unloading time delay as a target and taking allocation decision and unmanned aerial vehicle capacity as constraints;
s3, introducing a relaxation variable into the system optimization model to obtain a linear system optimization model;
s4, solving a linear system optimization model based on the basic information of the unmanned aerial vehicle and the basic information of the user to obtain a task allocation scheme;
in step S2, the allocation decision constraint is: each task can only be allocated to one unmanned aerial vehicle, expressed as:
wherein x is knm ∈{0,1}，x knm =1 denotes the nth task f of the kth user nk Distributed to unmanned plane m, x knm =0 denotes the nth task f of the kth user nk In the local processing, n= {1,2,3, & gt, N }, k= {1,2,3, & gt, K }, N representing the total number of tasks for the respective user, K representing the total number of users, m= {1,2,3, & gt, M }, M representing the total number of unmanned aerial vehicles;
in step S2, the unmanned aerial vehicle capacity constraint includes: the task capacity allocated to the respective drone cannot exceed the maximum storage capacity of the respective drone, expressed as:
wherein Q is m Representing the maximum storage capacity of the unmanned aerial vehicle m;
unmanned aerial vehicle capacity constraints also include: maintaining the corresponding unmanned aerial vehicle receiving task amount at Q min And Q is equal to max Between Q min Minimum capacity limit representing total amount of received tasks for a single drone, Q max Maximum capacity limit representing total amount of received tasks for a single drone, and Q min ＜Q max ≤Q m ；
In step S2, the user task offloading delay is calculated as follows:
A. calculating channel gain between the user and the unmanned aerial vehicle based on the user position information and the unmanned aerial vehicle position information;
B. calculating a channel capacity between the user and the unmanned aerial vehicle based on the channel gain;
C. calculating based on the channel capacity to obtain user task unloading time delay;
in the step a, the calculation formula of the channel gain g (m, k) between the unmanned plane m and the user k is as follows:
wherein beta is 0 Represents the channel gain at a reference distance of 1m, d (m, k) represents the distance between the drone m and the user k, x m Represents the abscissa of the unmanned plane m, y m Represents the ordinate of the unmanned plane m, H represents the height of the unmanned plane m, and x k Representing the abscissa of user k, y k Representing the ordinate of user k;
in step B, the channel capacity R between user k and drone m km The calculation formula of (2) is as follows:
wherein B represents channel bandwidth, P m Representing the transmit power, σ, of user k 2 Representing gaussian white noise variance;
in step C, the nth task of user k is assigned to the task offloading delay t of unmanned plane m knm Calculation ofThe formula is:
in step S2, the system optimization model is expressed as:
s.t.
x knm ∈{0,1}
2. the unmanned aerial vehicle assisted MEC network low latency multitasking distribution method of claim 1, wherein in step S3, a 0-1 relaxation variable is introduced into the system optimization model to obtain a linear system optimization model expressed as:
s.t.
3. the method for low latency multitasking assignment of an unmanned aerial vehicle assisted MEC network according to claim 2, wherein step S4 comprises the steps of:
s4.1, solving a linear system optimization model by adopting a simulated annealing method;
s4.2, performing discrete binary recovery based on a result obtained by solving, wherein the recovery rule is as follows:
4. the method for low latency multiplexing allocation of an unmanned aerial vehicle assisted MEC network according to claim 3, further comprising the steps of:
and creating virtual users with the same positions as the users according to the number of the user-required off-tasks, and distributing the user-required off-tasks to the virtual users so that each user distributes a required off-task.
5. An unmanned aerial vehicle assisted MEC network low-latency multi-task distribution system based on the distribution method of any one of claims 1-4, characterized by comprising an information acquisition module, a model building module and a solving module, wherein the model building module comprises a connected model building unit and a linear unit, and the information acquisition module, the solving module and the linear unit are connected in sequence;
the information acquisition module is used for acquiring basic information of a user and the unmanned aerial vehicle, wherein the basic information of the user comprises user position information and task unloading information required to be unloaded, and the basic information of the unmanned aerial vehicle comprises unmanned aerial vehicle position information;
the model building unit is used for building a system optimization model which aims at minimizing the user task unloading time delay and takes allocation decision and unmanned aerial vehicle capacity as constraints;
the linear unit is used for introducing a relaxation variable into the system optimization model to obtain a linear system optimization model;
and the solving module is used for solving the linear system optimization model based on the basic information of the unmanned aerial vehicle and the basic information of the user so as to obtain a task allocation scheme.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111612235.2A CN114268966B (en) | 2021-12-27 | 2021-12-27 | Unmanned aerial vehicle auxiliary MEC network low-delay multi-task allocation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111612235.2A CN114268966B (en) | 2021-12-27 | 2021-12-27 | Unmanned aerial vehicle auxiliary MEC network low-delay multi-task allocation method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114268966A CN114268966A (en) | 2022-04-01 |
| CN114268966B true CN114268966B (en) | 2024-04-05 |
Family
ID=80830355
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111612235.2A Active CN114268966B (en) | 2021-12-27 | 2021-12-27 | Unmanned aerial vehicle auxiliary MEC network low-delay multi-task allocation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114268966B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116249143B (en) * | 2023-05-11 | 2023-09-01 | 南京邮电大学 | Time delay optimization method and system for unmanned aerial vehicle auxiliary movement edge calculation |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112351503A (en) * | 2020-11-05 | 2021-02-09 | 大连理工大学 | Task prediction-based multi-unmanned-aerial-vehicle-assisted edge computing resource allocation method |
| CN112512063A (en) * | 2020-11-25 | 2021-03-16 | 福州大学 | Resource allocation method for unmanned aerial vehicle assisted edge computing based on radio frequency energy collection |
| CN112737842A (en) * | 2020-12-29 | 2021-04-30 | 西北工业大学深圳研究院 | Task safety unloading method based on minimized time delay in air-ground integrated Internet of vehicles |
-
2021
- 2021-12-27 CN CN202111612235.2A patent/CN114268966B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112351503A (en) * | 2020-11-05 | 2021-02-09 | 大连理工大学 | Task prediction-based multi-unmanned-aerial-vehicle-assisted edge computing resource allocation method |
| CN112512063A (en) * | 2020-11-25 | 2021-03-16 | 福州大学 | Resource allocation method for unmanned aerial vehicle assisted edge computing based on radio frequency energy collection |
| CN112737842A (en) * | 2020-12-29 | 2021-04-30 | 西北工业大学深圳研究院 | Task safety unloading method based on minimized time delay in air-ground integrated Internet of vehicles |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114268966A (en) | 2022-04-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111240701B (en) | Task unloading optimization method for end-side-cloud collaborative computing | |
| CN109951869B (en) | Internet of vehicles resource allocation method based on cloud and mist mixed calculation | |
| CN111372314A (en) | Task unloading method and task unloading device based on mobile edge computing scene | |
| CN109756912B (en) | Multi-user multi-base station joint task unloading and resource allocation method | |
| CN113359480B (en) | Multi-unmanned aerial vehicle and user cooperative communication optimization method based on MAPPO algorithm | |
| CN108924254B (en) | User-centered distributed multi-user computing task unloading method | |
| CN111757361B (en) | Task unloading method based on unmanned aerial vehicle assistance in fog network | |
| CN112040512B (en) | Mist computing task unloading method and system based on fairness | |
| CN114363803B (en) | Energy-saving multitasking distribution method and system for mobile edge computing network | |
| CN116546559B (en) | Distributed multi-target space-ground combined track planning and unloading scheduling method and system | |
| CN115659803A (en) | Intelligent unloading method for computing tasks under unmanned aerial vehicle twin network mapping error condition | |
| CN114268966B (en) | Unmanned aerial vehicle auxiliary MEC network low-delay multi-task allocation method and system | |
| CN113971461A (en) | Distributed federal learning method and system for unmanned aerial vehicle ad hoc network | |
| CN114866979A (en) | Unmanned aerial vehicle-assisted edge calculation time delay minimization method | |
| CN114268967B (en) | Unmanned aerial vehicle auxiliary mobile edge network user matching method and device | |
| CN115150781A (en) | Unmanned aerial vehicle assisted edge computing resource allocation method based on task priority | |
| CN113709728B (en) | NOMA and unmanned aerial vehicle assisted two-stage mobile edge computing communication method and system | |
| CN113627013A (en) | System throughput maximization method based on unmanned aerial vehicle binary unloading edge calculation | |
| Zheng et al. | Service caching based task offloading and resource allocation in multi-UAV assisted MEC networks | |
| CN116737391A (en) | Edge computing cooperation method based on mixing strategy in federal mode | |
| CN115348558A (en) | Unmanned aerial vehicle deployment and calculation unloading joint optimization method based on convex optimization | |
| CN116056220A (en) | Resource unit assignment for selective fading | |
| CN118102337A (en) | MEC network security energy efficiency maximization method based on user fairness | |
| CN117750435A (en) | Spectrum resource allocation and power cooperative regulation and control method for unmanned aerial vehicle power calculation network | |
| CN115118724A (en) | Ant colony algorithm-based multi-unmanned aerial vehicle-assisted edge computing system deployment optimization method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |