CN113361881A - Overlapped organization cooperative control method based on vehicle fog computing architecture - Google Patents
Overlapped organization cooperative control method based on vehicle fog computing architecture Download PDFInfo
- Publication number
- CN113361881A CN113361881A CN202110576290.4A CN202110576290A CN113361881A CN 113361881 A CN113361881 A CN 113361881A CN 202110576290 A CN202110576290 A CN 202110576290A CN 113361881 A CN113361881 A CN 113361881A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- alliance
- calculation
- alliances
- vehicle node
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 26
- 230000008520 organization Effects 0.000 title claims description 10
- 238000004364 calculation method Methods 0.000 claims abstract description 51
- 230000002860 competitive effect Effects 0.000 claims abstract description 5
- 230000000875 corresponding effect Effects 0.000 claims description 18
- 230000007246 mechanism Effects 0.000 claims description 14
- 238000004891 communication Methods 0.000 claims description 13
- 238000009826 distribution Methods 0.000 claims description 11
- 238000004422 calculation algorithm Methods 0.000 claims description 10
- 238000005304 joining Methods 0.000 claims description 9
- 238000005457 optimization Methods 0.000 claims description 7
- 230000002596 correlated effect Effects 0.000 claims description 4
- 230000010365 information processing Effects 0.000 claims description 3
- 230000002452 interceptive effect Effects 0.000 claims description 3
- 238000007726 management method Methods 0.000 claims description 3
- 230000011664 signaling Effects 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 abstract description 6
- 230000008901 benefit Effects 0.000 abstract description 5
- 238000004088 simulation Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 7
- 238000012545 processing Methods 0.000 description 6
- 230000015572 biosynthetic process Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000009471 action Effects 0.000 description 3
- 230000001276 controlling effect Effects 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000003190 augmentative effect Effects 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/042—Backward inferencing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y10/00—Economic sectors
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- Computing Systems (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Accounting & Taxation (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to an overlapped organizational cooperative control method based on a vehicle fog computing architecture, which is characterized in that each vehicle node in a target area generates task information needing cooperative computing according to requirements, the task information is transmitted to roadside central units in corresponding service areas, the vehicle nodes in the same service area form alliances, each vehicle node is added with a plurality of alliances at the same time, the alliances are overlapped, each alliance realizes computing resource sharing under the control and scheduling of the roadside central units, the roadside central units generate optimal resource scheduling decisions according to computing resources and position information fed back by vehicle periods, and the alliances perform competitive computing according to the optimal resource scheduling decisions. Compared with the prior art, the method has the advantages of effectively improving the calculation efficiency of the local Internet of vehicles on the tasks, avoiding transmitting the tasks to the remote server, reducing service delay and transmission overhead and the like.
Description
Technical Field
The invention relates to the field of vehicle cooperative computing, in particular to a control method capable of realizing overlapped organization cooperation based on a vehicle fog computing architecture.
Background
In recent years, with the introduction and development of intelligent transportation systems and smart city concepts, the internet of vehicles has received more and more extensive attention and research. With the increasing number of smart vehicles, the demand for computing tasks by on-board units is increasing. The demands of mobile applications such as virtual reality, augmented reality, automatic driving and various in-vehicle entertainment projects on the calculation amount and the calculation real-time performance are further increased, the calculation capacity of the vehicle cannot meet the demands, and the transmission delay for unloading the calculation task to a remote server is too expensive to meet the demands of some services (such as automatic driving) with high real-time performance requirements. Therefore, to solve this problem, the concept of Vehicle Fog Calculation (VFC) arose.
The goal of fog computing is to bring the computing storage capacity of the remote to the edge of the system. The architecture of VFC mainly includes three levels: the cloud layer of the remote server and the data center, the small cloud layer of the local fog server and the fog layer of the vehicle. The local fog server is used for dispatching the calculation tasks generated by the vehicles and deciding to transmit the calculation tasks to the remote server or solve the calculation tasks in the fog layer. The local fog servers are widely distributed in geographical locations, such as roadside central units, base stations and the like, the effective coverage area of the local fog servers can be regarded as a service area, and a plurality of service areas can cover one city. Under the VFC framework, vehicles are also service providers while they are tasked, and are considered a computing and communication infrastructure. Wherein the communication between the vehicle and the vehicle (V2V) and between the vehicle and the facility (V2I) is performed by a dedicated short-range communication technology. The most critical issue during the whole process of computing tasks is "who is to compute the task". After the vehicle generates the task, the calculation task is scheduled by the LFS, and the calculation task is unloaded to a remote server, is delivered to the vehicle in a local service area for resolution, or is transmitted to a local fog server in a neighboring service area for processing. At present, two main ideas exist for processing the task scheduling problem: firstly, the whole scheduling task is regarded as an optimization problem, the general aim is to minimize the time delay or energy consumption of the task and allocate the task to a specified vehicle on the premise of ensuring the service experience and the service quality of a user; and secondly, incentive measures are carried out on the vehicle users, including a contract mechanism, a market currency mechanism and the like, so that the vehicles are encouraged to share own calculation and storage resources, and the overall resource amount of the system is increased.
However, in the cooperative computing scheme under the existing VFC architecture, from the perspective of overall optimization or an excitation mechanism, most vehicles are regarded as a schedulable resource rather than a selfish node with interest appeal, and the potential of the vehicle autonomous adjustment structure to improve the system efficiency is ignored. Since the optimization problem of task scheduling is NP problem, the complexity of the scheduling optimization problem will continue to increase as the number of smart vehicles further increases. Therefore, considering the cooperative computing problem from the perspective of vehicle behavior strategy will become an effective idea. The league game is a game theory method for cooperative computing, and players participating in the game cooperate with each other through league formation, so that greater benefits are obtained. The league game has been widely applied in the field of wireless communication. In the traditional league game, each player can only join one league, so the leagues are separated from each other. This non-overlapping league partitioning strategy ignores the potential revenue that a player may have from joining multiple leagues at the same time. Each player can join a plurality of alliances simultaneously for the benefit of the player to distribute the limited resource of the player so as to obtain more benefits, and the game is formed by overlapping alliances.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides the control method of the overlapped organization cooperation based on the vehicle fog computing architecture, so that the computing efficiency of the local vehicle networking on tasks is effectively improved, the tasks are prevented from being transmitted to a remote server side, and the service delay and the transmission overhead are reduced.
The purpose of the invention can be realized by the following technical scheme:
a method for controlling overlapped organization cooperation based on a vehicle fog computing architecture is characterized in that each vehicle node in a target area generates task information needing cooperative computing according to requirements, the task information is transmitted to roadside center units in corresponding service areas, the vehicle nodes in the same service area form an alliance, each vehicle node is added with a plurality of alliances at the same time, the alliances are overlapped, each alliance realizes computing resource sharing under the control and the scheduling of the roadside center units, the roadside center units generate optimal resource scheduling decisions according to computing resources and position information fed back by the vehicle periods, and the alliances perform competitive computing according to the optimal resource scheduling decisions.
The overlapped alliance forming algorithm does not need to detect the surrounding environment in real time and then adjust the alliance structure according to the feedback information, but runs periodically, and realizes an alliance forming scheme with low communication cost.
And the vehicle nodes spontaneously select to join or leave a plurality of alliances under certain constraint conditions, when one alliance is joined, the vehicle nodes donate certain resources, and after one alliance is left, the contributed resources are redistributed.
Each alliance is provided with a leader vehicle node, and the leader vehicle node is communicated with the corresponding roadside central unit.
Further, the optimal resource scheduling decision is sent to the leader vehicle node by the roadside central unit, and the leader vehicle node periodically reports the status attributes of the alliance to the corresponding roadside central unit, wherein the status attributes comprise the number of the overall computing resources of the alliance and the distribution positions of the member vehicle nodes.
Furthermore, the other vehicle nodes except the leader vehicle node in the alliance are used as member vehicle nodes, the member vehicle nodes can only communicate with the leader vehicle node, the member vehicles receive the dispatching of the leader vehicle node, the communication information is interactive signaling, and the resources of the member vehicle nodes cannot be directly received by the roadside central unit dispatching.
Further, the choice of leader vehicle node is chosen by member vehicle nodes and is not changed in a stable federation structure.
Furthermore, the vehicle nodes in the affiliated alliances can help relay information, and the vehicle communication service range is expanded.
The vehicle node needs to perform data delivery and information processing after leaving the range of the service area of the roadside center unit.
The roadside center unit is communicated with the leader vehicle node of the alliance, resource management is carried out on the alliance level, a logic topological structure is constructed according to the calculation resource information and the position information fed back by the vehicle node, and an optimal resource scheduling decision is generated through a preset algorithm and a specific optimization target according to the logic topological structure.
The vehicle nodes are provided with a candidate alliance list and a current alliance list, the candidate alliance list records alliances meeting constraint conditions of the corresponding vehicle nodes, the current alliance list records alliances to which the corresponding vehicle nodes belong currently, and each vehicle node joining the alliance needs to contribute certain computing resources.
Further, the number of alliances that the vehicle node can join has a certain upper limit, and the upper limit is adjusted according to actual conditions and communication channel conditions.
The alliance is provided with an incentive mechanism, and the vehicle nodes obtain money corresponding to the optimal resource scheduling decision through completing the incentive mechanism.
Further, the goal of the vehicle node to take action is to maximize its own revenue, which comes from the incentive mechanism, and which can be used to improve the user experience.
Further, the distribution of the money is positively correlated with the calculation characteristics of the calculation task, and the types of the calculation characteristics comprise the uploading data volume, the downloading data volume and the number of calculation rounds required to be consumed.
Compared with the prior art, the invention has the following beneficial effects:
the invention forms alliances among different vehicles for carrying out cooperative calculation and information transmission, carries out task delivery and information transmission between the alliances and the roadside central unit, the roadside central unit makes a task allocation strategy according to the calculation resource information and the position information fed back by the vehicle period, the vehicles form a stable alliance structure according to an incentive mechanism and the premise of maximizing self income, and a vehicle system can form a stable alliance structure under different road conditions with lower complexity, thereby effectively improving the task completion rate and the calculation capacity of the edge fog node, simultaneously improving the calculation efficiency of the local internet of vehicles for tasks, avoiding transmitting tasks to a remote server end, and reducing service delay and transmission overhead.
Drawings
FIG. 1 is a schematic diagram of the vehicle fog calculation of the present invention;
FIG. 2 is a diagram illustrating federating under the overlapping federating algorithm of the present invention;
fig. 3 is a schematic structural diagram of a union formed by road conditions at a certain time in the embodiment of the present invention;
FIG. 4 is a simulation graph of the time it takes to complete the number of computing tasks and the time it takes to complete given the total number of tasks of the present invention is 1000;
FIG. 5 is a comparison diagram of calculating the average service delay of a task according to an embodiment of the present invention;
FIG. 6 is a graph comparing task completion rates in embodiments of the present invention;
FIG. 7 is a diagram illustrating the variation of the total profit within the system under simulation in the embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
As shown in fig. 1 and 2, in the method for controlling organization collaboration which can be overlapped based on a vehicle fog computing architecture, each vehicle node in a target area generates task information which needs to be cooperatively computed according to requirements, the task information is transmitted to roadside central units in corresponding service areas, the vehicle nodes in the same service area form alliances, each vehicle node is added with a plurality of alliances at the same time, the alliances are overlapped, each alliance realizes computing resource sharing under the control and scheduling of the roadside central units, the roadside central units generate optimal resource scheduling decisions according to computing resources and position information fed back by vehicle periods, and the alliances perform competitive computation according to the optimal resource scheduling decisions.
Vehicle nodes in the same service area and between different service areas carry out internal task distribution and cross-service area task switching under the control of a roadside central unit in each service area, and process calculation tasks under the scheduling of the vehicle nodes.
The overlapped alliance forming algorithm does not need to detect the surrounding environment in real time and then adjust the alliance structure according to the feedback information, but runs periodically, and an alliance forming scheme with low communication cost is achieved.
The vehicle nodes spontaneously select to join or leave a plurality of alliances under certain constraint conditions, when the vehicle nodes join one alliance, the vehicle nodes donate certain resources, and after the vehicle nodes leave one alliance, the contributed resources are redistributed.
Each alliance is provided with a leader vehicle node, and the leader vehicle node is communicated with the corresponding roadside central unit.
And the optimal resource scheduling decision is sent to the leader vehicle node by the roadside central unit, the leader vehicle node reports the state attributes of the alliances to the corresponding roadside central unit regularly, and the state attributes comprise the quantity of the overall calculation resources of the alliances and the distribution positions of all member vehicle nodes.
And other vehicle nodes except the leading vehicle node in the alliance are used as member vehicle nodes, the member vehicle nodes can only communicate with the leading vehicle node, the member vehicles receive the dispatching of the leading vehicle node, the communication information is interactive signaling, and the resources of the member vehicle nodes cannot be directly received by the roadside central unit dispatching.
The choice of leader vehicle node is chosen by member vehicle nodes and is not changed in a stable federation structure.
The vehicle node needs to perform data delivery and information processing after leaving the range of the service area of the roadside center unit.
The roadside center unit communicates with a leader vehicle node of the alliance, performs resource management on the alliance level, constructs a logic topological structure according to calculation resource information and position information fed back by the vehicle node, and generates an optimal resource scheduling decision through a preset algorithm and a specific optimization target according to the logic topological structure.
The vehicle nodes are provided with a candidate alliance list and a current alliance list, the candidate alliance list records alliances meeting constraint conditions of the corresponding vehicle nodes, the current alliance list records alliances to which the corresponding vehicle nodes belong currently, and each vehicle node joining the alliance needs to contribute certain computing resources.
The number of alliances that a vehicle node can join has a certain upper limit, which is adjusted according to actual conditions and communication channel conditions.
The alliance is provided with an incentive mechanism, and the vehicle nodes obtain money corresponding to the optimal resource scheduling decision under the incentive mechanism.
The goal of the vehicle node to take behavioral actions is to maximize its own revenue, which comes from the incentive mechanism, and which can be used to improve the user experience.
The distribution of the money is positively correlated with the calculation characteristics of the calculation task, and the types of the calculation characteristics comprise the uploading data volume, the downloading data volume and the number of calculation rounds required to be consumed.
In this embodiment, each alliance has a certain utility value according to the capability of its internal vehicle node. The utility value is related to the incentive scheme, and the higher the utility value, the better the ability of the federation to complete the task. Each vehicle node estimates the rewards that can be obtained by joining each coalition based on an incentive scheme according to a certain algorithm. The reward is proportional to the utility of the league, i.e. the league with greater utility can bring more rewards. In addition, the reward needs to take into account the actual consumption of joining the federation and the minimum communication requirements to prevent a large federation from forming. And adopting the action of joining the alliance or exiting the existing alliance to maximize the reward obtained by the user. Inside the federation is irrevocable utility, so each vehicle node will be considered a greedy and selfish user. Finally, under the constraint conditions of speed direction and distance, through continuous switching alliance, the alliance structure inside the service area tends to be stable, and the alliance under the structure also has the maximum total utility.
Incentive mechanisms are mechanisms for encouraging vehicles to share computing resources. Monetary awards are one form of money awards. When currency is used for task distribution, the task is competitive compared with other tasks, and the consumed service delay is lower. The distribution of the money is positively correlated with the calculation characteristics of the task, namely, the larger the calculation difficulty and the more time consumption, the larger the amount of money required to be guaranteed to be completed on time. Monetary accounting for each vehicle node is taken care of by roadside central units within the service area.
Fig. 3 shows a vehicle union formed by road conditions at a certain time, wherein the vehicles on the upper two roads run from left to right, and the vehicles on the lower two roads run from right to left. Set of vehicle nodes { V6,V9}、{V14,V15Form two leagues because of their closer speed, same direction, and closer distance. Each vehicle node ViBoth maintain two lists by themselves, a candidate federation list and a current federation list. The candidate alliance list stores all nearby alliances meeting the constraint condition, and each time the vehicle selects whether to join a new alliance from the candidate alliance list. The current alliance list stores the alliances that the vehicle currently joins. Each time the vehicle selects from the list of current alliances whether to exit the current alliance. For practical reasons, there should be a limit to the number of alliances that a vehicle can join at the same time, and the upper limit of this number is set to M. Vehicle ViAll the joining and leaving operations are to maximize the own profit pi, while the own overall profit pi (CS) is determined by the current alliance structure (CS) since each vehicle can join multiple alliances simultaneously. This local federation structure is considered stable when actions cannot be taken to increase its revenue.
In this embodiment, the overlap join forming algorithm is specifically as follows:
(1) constructing a topological structure diagram based on the alliance information obtained by the vehicle node feedback and the position of the leader vehicle node, and initializing each vehicle node in the diagram:
a) each node creates a alliance only containing the node;
b) adding a qualified candidate alliance according to the constraint condition;
c) exiting the non-compliant current alliance according to the constraint condition;
(2) and (3) iterative convergence:
a) slave node ViSelect federation C in the candidate federation list ofk：
If pi (CS { [ C ]k∪{Vi}) pi (CS), then ViJoining federation CkOtherwise, the current alliance list is not changed;
b) slave node ViSelects federation C in the current list of federationsk：
If pi (CS \ C)k}∪{Ck\{Vi}) pi (CS), then ViLeaving federation CkOtherwise, the current alliance list is not changed;
c) updating the candidate alliance list and the current alliance list;
d) until the federation structure stabilizes;
(3) and (3) completing the task:
a) the vehicle generates a calculation task and sends a task request to the roadside central unit;
b) the roadside central unit designates the alliance to complete the calculation task, and the alliance leader vehicle dispatches and selects the alliance internal vehicle to complete the calculation task;
c) and the task generating vehicle transmits the calculation task to the vehicle appointed to complete the task, the vehicle completes the calculation task, and the result is returned to the generating vehicle after the calculation task is completed.
FIG. 7 is a graph of the system's overall yield versus the number of iterative rounds for a given fixed position vehicle. It can be seen that both the overlapping coalition formation algorithm and the non-overlapping coalition formation algorithm can achieve nash equilibrium within five iterations in a pseudo-static environment.
The following comparison shows the local task completion rate under different environmental parameters under the Vehicle Fog Computing (VFC) architecture. In the simulation, the generation process of the calculation task and the process of the vehicle arriving at the service area are both regarded as being subject to poisson distribution, the processing capacity of the service area to the task can be measured by utilizing the overall task completion rate, and the specific simulation parameters are shown in table 1:
TABLE 1 simulation parameters
As shown in fig. 5 and fig. 6, the variation trend of the task completion rate and the average task delay under different vehicle arrival rates is given. The larger the poisson distribution parameter of the vehicle arrival is, the larger the number of vehicles in the service area is, and the more the available computing resources are. And as can be seen from the simulation results, the overlapping federation formation strategy is superior to the traditional federation formation strategy. Finally, the generation of the simulation data has randomness, so that the data has a certain deviation compared with the real situation, but it is reasonable to believe that the game strategy formed by the overlapping alliances can effectively improve the task processing capacity of the local Internet of vehicles under the VFC architecture, and especially can effectively improve the task processing capacity of the network under the condition of large network load. This also illustrates the great application potential of the coordinated computation scheme based on the OCF under the VFC architecture proposed in the present invention.
In order to investigate the influence of the cooperative computing scheme on the system task throughput, as shown in fig. 4, given 1000 computing tasks, the variation trend of the ratio of the number of completed tasks to the simulation time is given. Because each task is issued only after the current task is completed, the ratio of the number of completed tasks and the simulation time are in a nearly linear relationship. The method has the advantages that the overlapping alliance forming game strategy can effectively improve the throughput of a system to the computing task and increase the speed of processing the computing task compared with the non-overlapping alliance forming game strategy and the non-cooperation strategy.
In addition, it should be noted that the specific embodiments described in the present specification may have different names, and the above descriptions in the present specification are only illustrations of the structures of the present invention. All equivalent or simple changes in the structure, characteristics and principles of the invention are included in the protection scope of the invention. Various modifications or additions may be made to the described embodiments or methods may be similarly employed by those skilled in the art without departing from the scope of the invention as defined in the appending claims.
Claims (10)
1. A vehicle fog calculation architecture-based overlapped organization cooperative control method is characterized in that each vehicle node in a target area generates task information needing cooperative calculation according to requirements, the task information is transmitted to roadside center units in corresponding service areas, the vehicle nodes in the same service area form alliances, each vehicle node is added with a plurality of alliances at the same time, the alliances are overlapped, each alliance achieves calculation resource sharing under the control and scheduling of the roadside center units, the roadside center units generate optimal resource scheduling decisions according to calculation resources and position information fed back by the vehicle periods, and the alliances conduct competitive calculation according to the optimal resource scheduling decisions.
2. The method as claimed in claim 1, wherein the vehicle nodes spontaneously choose to join or leave a plurality of the federations under certain constraint conditions, joining one of the federations means that the vehicle node contributes a certain resource, and leaving one of the federations means that the contributed resource is reallocated.
3. The vehicle fog calculation architecture-based stackable organizational collaboration control method according to claim 1, wherein each of the unions is provided with a leader vehicle node through which to communicate with the corresponding roadside center unit.
4. The method according to claim 3, wherein the optimal resource scheduling decision is sent to a leader vehicle node by a roadside center unit, and the leader vehicle node periodically reports status attributes of the alliance to the corresponding roadside center unit, wherein the status attributes comprise the number of alliance overall computing resources and the distribution positions of the member vehicle nodes.
5. The vehicle fog calculation architecture-based overlapped organization cooperative control method according to claim 3, wherein the vehicle nodes except the leader vehicle node in the alliance are used as member vehicle nodes, the member vehicle nodes can only communicate with the leader vehicle node, the member vehicles receive the dispatching of the leader vehicle node, and the communication information is interactive signaling.
6. The vehicle fog calculation architecture based stackable organization coordination control method according to claim 1, wherein said vehicle node needs data delivery and information processing after leaving the range of the service area of said roadside center unit.
7. The method as claimed in claim 1, wherein the roadside center unit communicates with a leader vehicle node of the alliance, performs resource management at the alliance level, constructs a logical topology structure according to the computing resource information and the location information fed back by the vehicle node, and generates an optimal resource scheduling decision through a preset algorithm and a specific optimization objective according to the logical topology structure.
8. The vehicle fog computing architecture-based overlappable organizational collaboration control method according to claim 1, wherein the vehicle node is provided with a candidate alliance list and a current alliance list, the candidate alliance list records alliances meeting the constraint conditions of the corresponding vehicle node, and the current alliance list records alliances to which the corresponding vehicle node currently belongs.
9. The method as claimed in claim 1, wherein the alliance is provided with an incentive mechanism, and the vehicle nodes obtain money corresponding to the optimal resource scheduling decision through the incentive mechanism.
10. The vehicle fog calculation architecture based stackable organization cooperation control method according to claim 9, wherein the distribution of the money is positively correlated with the calculation characteristics of the calculation task, and the types of the calculation characteristics comprise an upload data amount, a download data amount and a number of calculation rounds required to be consumed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110576290.4A CN113361881A (en) | 2021-05-26 | 2021-05-26 | Overlapped organization cooperative control method based on vehicle fog computing architecture |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110576290.4A CN113361881A (en) | 2021-05-26 | 2021-05-26 | Overlapped organization cooperative control method based on vehicle fog computing architecture |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113361881A true CN113361881A (en) | 2021-09-07 |
Family
ID=77527617
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110576290.4A Pending CN113361881A (en) | 2021-05-26 | 2021-05-26 | Overlapped organization cooperative control method based on vehicle fog computing architecture |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113361881A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113709698A (en) * | 2021-09-08 | 2021-11-26 | 同济大学 | Internet of vehicles fog computing cooperation task unloading control method based on task block |
| CN114401037A (en) * | 2022-03-24 | 2022-04-26 | 武汉大学 | Unmanned aerial vehicle communication network flow unloading method and system based on alliance formed game |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107708169A (en) * | 2017-08-07 | 2018-02-16 | 国家计算机网络与信息安全管理中心 | Obtain the method and system of distributed overlay Stable coalitions structure |
| CN108777852A (en) * | 2018-05-16 | 2018-11-09 | 国网吉林省电力有限公司信息通信公司 | A kind of car networking content edge discharging method, mobile resources distribution system |
| US20190312779A1 (en) * | 2018-04-05 | 2019-10-10 | Phantom Auto Inc. | Platform for vehicle cooperation and coordination of services |
| CN111405527A (en) * | 2020-03-17 | 2020-07-10 | 中南大学 | Vehicle-mounted edge computing method, device and system based on volunteer cooperative processing |
-
2021
- 2021-05-26 CN CN202110576290.4A patent/CN113361881A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107708169A (en) * | 2017-08-07 | 2018-02-16 | 国家计算机网络与信息安全管理中心 | Obtain the method and system of distributed overlay Stable coalitions structure |
| US20190312779A1 (en) * | 2018-04-05 | 2019-10-10 | Phantom Auto Inc. | Platform for vehicle cooperation and coordination of services |
| CN108777852A (en) * | 2018-05-16 | 2018-11-09 | 国网吉林省电力有限公司信息通信公司 | A kind of car networking content edge discharging method, mobile resources distribution system |
| CN111405527A (en) * | 2020-03-17 | 2020-07-10 | 中南大学 | Vehicle-mounted edge computing method, device and system based on volunteer cooperative processing |
Non-Patent Citations (1)
| Title |
|---|
| XIANJING WU.ELT: "Mobility Prediction-Based Joint Task Assignment and Resource Allocation in Vehicular Fog Computing", 《IEEE》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113709698A (en) * | 2021-09-08 | 2021-11-26 | 同济大学 | Internet of vehicles fog computing cooperation task unloading control method based on task block |
| CN114401037A (en) * | 2022-03-24 | 2022-04-26 | 武汉大学 | Unmanned aerial vehicle communication network flow unloading method and system based on alliance formed game |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11607971B2 (en) | Method for scheduling multi agent and unmanned electric vehicle battery swap based on internet of vehicles | |
| CN109379727B (en) | MEC-based task distributed unloading and cooperative execution scheme in Internet of vehicles | |
| Li et al. | An online incentive mechanism for collaborative task offloading in mobile edge computing | |
| Zhou et al. | Incentive-driven deep reinforcement learning for content caching and D2D offloading | |
| Hammoud et al. | On demand fog federations for horizontal federated learning in IoV | |
| Li et al. | Joint offloading decision and resource allocation for vehicular fog-edge computing networks: A contract-stackelberg approach | |
| Saad et al. | A selfish approach to coalition formation among unmanned air vehicles in wireless networks | |
| CN113361881A (en) | Overlapped organization cooperative control method based on vehicle fog computing architecture | |
| Hui et al. | Reservation service: Trusted relay selection for edge computing services in vehicular networks | |
| Wei et al. | Many-to-many task offloading in vehicular fog computing: A multi-agent deep reinforcement learning approach | |
| CN113709698A (en) | Internet of vehicles fog computing cooperation task unloading control method based on task block | |
| Su et al. | Engineering a game theoretic access for urban vehicular networks | |
| Wu et al. | A profit-aware coalition game for cooperative content caching at the network edge | |
| CN105142195A (en) | Method for communication strategy selection and resource allocation of wireless terminal based on Stackelberg game | |
| Wei et al. | OCVC: An overlapping-enabled cooperative vehicular fog computing protocol | |
| Balasubramanian et al. | FedCo: A federated learning controller for content management in multi-party edge systems | |
| Wei et al. | Dynamic many-to-many task offloading in vehicular fog computing: A multi-agent drl approach | |
| Liwang et al. | Unifying futures and spot market: Overbooking-enabled resource trading in mobile edge networks | |
| Bernal et al. | Management of EV charging stations under advance reservations schemes in electricity markets | |
| Nguyen et al. | EdgePV: collaborative edge computing framework for task offloading | |
| Liwang et al. | Overbooking-empowered computing resource provisioning in cloud-aided mobile edge networks | |
| Yang et al. | Incorporating user willingness in contract-based incentive mechanism for D2D cooperative data forwarding | |
| Rebecchi et al. | Should I seed or should I not: On the remuneration of seeders in D2D offloading | |
| Kong et al. | Cloud-based charging management of electric vehicles in a network of charging stations | |
| Yang et al. | A self-adaptive method of task allocation in clustering-based MANETs |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210907 |