CN113592327A - Online vehicle selection method, system and terminal for task distribution in Internet of vehicles - Google Patents

Online vehicle selection method, system and terminal for task distribution in Internet of vehicles Download PDF

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CN113592327A
CN113592327A CN202110898408.5A CN202110898408A CN113592327A CN 113592327 A CN113592327 A CN 113592327A CN 202110898408 A CN202110898408 A CN 202110898408A CN 113592327 A CN113592327 A CN 113592327A
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钱永峰
左洲同
郝义学
宋军
杨帆
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China University of Geosciences
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Abstract

The invention belongs to the technical field of vehicle networking, and discloses an online vehicle selection method, a system and a terminal aiming at task distribution in the vehicle networking, wherein an online quality sensing vehicle selection scheme and an online vehicle selection scheme based on cost sensing and quality sensing are constructed; when the task vehicles in the internet of vehicles do not know all information of the service vehicles, such as cost and task completion quality, an online vehicle selection scheme based on cost perception and quality perception is selected for vehicle selection. The invention comprehensively considers the cost of the service vehicle, the learning reward of the task vehicle and the cost to select the vehicle. The invention can effectively select the service vehicle on the basis of ensuring the efficient completion of the task, and can reduce the processing cost and improve the processing efficiency.

Description

Online vehicle selection method, system and terminal for task distribution in Internet of vehicles
Technical Field
The invention belongs to the technical field of Internet of vehicles, and particularly relates to an online vehicle selection method, system and terminal aiming at task distribution in the Internet of vehicles.
Background
Currently, with the development of automated driving, the demand for computing resources has increased dramatically. And because of the differences between vehicles, it is a challenging problem how to make vehicles cooperate to accomplish delay sensitive tasks. To address this problem, many solutions have been proposed so far, such as vehicle temporary networks and vehicle edge cloud systems proposed based on vehicle-to-vehicle communication technology. In vehicle edge clouds, vehicles are divided into two categories: a task vehicle, having tasks to process, and a server vehicle having remaining resources and wanting to process tasks of others. In this case, the task vehicle needs to replicate the task and then replicate it to the server vehicle.
Wherein how to select a service vehicle is always a problem that needs to be constantly optimized. Existing vehicle options can be divided into two types: centralized models and decentralized models. In the centralized model, the task vehicle knows all the information of the service vehicles, e.g. channel information, and can select the best service vehicle to replicate the task. In the decentralized approach, since the vehicle moves at a fast speed, the network is dynamic, and the information of the service vehicle is an unknown a priori knowledge, the service vehicle is selected depending on the learning method.
Through the above analysis, the problems and defects of the prior art are as follows: the existing vehicle selection method cannot adapt to actual requirements, occupies a large amount of resources, and is not optimal in a selection scheme. In the prior art, all information of the service vehicle is mostly assumed to be completely known, but the information is difficult to realize because tasks sent by the task vehicle are different and are influenced by real environment factors, and the effect of the service vehicle for completing the tasks is changed.
The difficulty in solving the above problems and defects is: the task vehicle does not know the network condition and processing cost of the service vehicle in advance, so that the optimal solution of task allocation cannot be obtained
The significance of solving the problems and the defects is as follows: after the problems are solved, the optimal solution of task allocation can be obtained under the condition of no prior knowledge, the assumed condition is more consistent with the actual condition, the optimal solution allocation of the task can be completed with the least cost, and the maximum benefit is obtained.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an online vehicle selection method, system and terminal aiming at task distribution in the Internet of vehicles.
The invention is realized in this way, an online vehicle selection method for task distribution in the Internet of vehicles, comprising:
and establishing an online quality perception vehicle selection scheme and an online vehicle selection scheme based on cost perception and quality perception, and determining different online vehicle selection schemes and selecting vehicles based on information whether task vehicles in the internet of vehicles definitely serve the vehicles.
Further, the information of the service vehicle includes a cost for completing the task.
Further, the determining different online vehicle selection schemes and performing vehicle selection based on information whether the task vehicle explicitly serves the vehicle in the internet of vehicles comprises:
when the task vehicles in the Internet of vehicles know the information of part of service vehicles, selecting an online quality sensing vehicle selection scheme for vehicle selection;
and when the task vehicle in the Internet of vehicles does not know the information of the service vehicle, selecting an online vehicle selection scheme based on cost perception and quality perception to select the vehicle.
Further, the online vehicle selection method for task distribution in the internet of vehicles comprises the following steps:
judging whether a task vehicle knows the information of part of service vehicles or not; if so, executing the step two; if not, executing the step four;
distributing the tasks to all service vehicles, returning task results after the service vehicles finish the tasks, and determining task information of the service vehicles; calculating based on the acquired task information to obtain an optimal task expectation, and turning to the third step;
step three, based on the acquired task expectation data, selecting, and continuously selecting the best service vehicle according to the index when the budget is not exhausted or the task is not completed;
and step four, selecting the vehicle according to an online vehicle selection scheme based on cost perception and quality perception.
Further, in step two, the task information of the service vehicle includes: the task completion quality of the service vehicle, and the task profit.
Further, in step two, the optimal task expectation is that the optimal task is most suitable for the selected service vehicle index under the comprehensive condition.
Further, the third step further includes: the previously obtained optimal mission expectations are updated each time a vehicle is selected.
Further, the online vehicle selection method for task distribution in the internet of vehicles further comprises the following steps:
the vehicle selection problem is modeled as a new multiple arm slot machine problem, where each vehicle is considered an arm and its processing completion time is considered a corresponding reward, i.e., vehicle selection is virtualized as a pull arm.
Further, the online vehicle selection method for task distribution in the internet of vehicles further comprises the following steps: vehicle selection is made taking into account the cost of the service vehicle, the learning reward of the mission vehicle, and the cost.
Further, the vehicle selection comprises: a plurality of vehicles are selected at a time for ensuring a task processing result.
Another object of the present invention is to provide an online vehicle selection control system for task distribution in a vehicle networking, comprising:
the service vehicle task analysis module is used for judging whether the task vehicle knows the information of part of service vehicles or not;
the system comprises an optimal task expectation acquisition module, a task processing module and a task processing module, wherein the optimal task expectation acquisition module is used for distributing tasks to all service vehicles, returning task results after the service vehicles complete the tasks and determining task information of the service vehicles; calculating based on the acquired task information to obtain an optimal task expectation;
the optimal service vehicle selection module is used for selecting based on the acquired task expectation data, and continuously selecting the optimal service vehicle according to the index when the budget is not exhausted or the task is not completed;
and the online vehicle selection scheme selection module is used for selecting vehicles according to the online vehicle selection scheme based on cost perception and quality perception.
It is a further object of the present invention to provide a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform an online vehicle selection method for task distribution in the internet of vehicles.
Another object of the present invention is to provide an information data processing terminal including a memory storing a computer program and a processor, the computer program, when executed by the processor, causing the processor to execute an online vehicle selection method for task distribution in the internet of vehicles.
By combining all the technical schemes, the invention has the advantages and positive effects that: the invention can effectively select the service vehicle on the basis of ensuring the efficient completion of the task, and can reduce the processing cost and improve the processing efficiency. We introduce the vehicle selection problem of task replication in a vehicle network at an unknown cost of service vehicles and model it as an optimization problem with the aim of maximizing the task replication quality under the limited budget of the task vehicles. Based on dobby piracy theory, and considering whether the cost of service vehicles is fixed or dynamic, we developed QVS algorithm and QCVS algorithm, respectively. In addition, we also comparatively analyzed the regrettable bounds of the two algorithms. We performed extensive experiments to prove the validity of the proposed algorithm. Experimental results show that compared with the traditional vehicle selection algorithm, the algorithm can improve the task replication quality. For example, our proposed QVS algorithm is 10% better in quality than other algorithms.
We propose a vehicle selection scheme and service vehicle cost for mission replication under unknown network conditions to maximize mission replication quality with a limited budget. We formulated this problem as a budgeted multiple armed bander problem. Specifically, considering that the service vehicle cost is fixed and after selecting the service vehicle number once, we first propose a good vehicle selection scheme. We then consider a service vehicle with an unknown variable cost and propose a quality-aware and cost-aware vehicle selection scheme. Finally, experiments show the effectiveness of our project.
The experimental results are shown in fig. 3-8, the scheme of the present invention is compared with several schemes which are common at present, and the experimental result figures show that the scheme of the present invention performs better in all aspects.
Drawings
Fig. 1 is a schematic diagram of an online vehicle selection method for task distribution in the internet of vehicles according to an embodiment of the present invention.
Fig. 2 is a flowchart of an online vehicle selection method for task distribution in the internet of vehicles according to an embodiment of the present invention.
Fig. 3-8 are graphs comparing experimental results of the present invention with those of the prior art, provided by the embodiment of the present invention. Fig. 3 shows the quality of task replication in different task vehicle selection modes, and fig. 4 shows the difference between the optimal solution and the actual solution in different task vehicle selection situations. Fig. 5,6 and 7 respectively demonstrate the effect of different budgets, different task vehicles and different service vehicles on the task quality. Figure 8 shows the performance of the algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides an online vehicle selection method for task distribution in an internet of vehicles, and the present invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the online vehicle selection method for task distribution in the internet of vehicles according to the embodiment of the present invention includes:
and establishing an online quality perception vehicle selection scheme and an online vehicle selection scheme based on cost perception and quality perception, and determining different online vehicle selection schemes and selecting vehicles based on information whether task vehicles in the internet of vehicles definitely serve the vehicles.
The information provided by the embodiment of the invention for servicing the vehicle includes the cost for completing the task.
The method for determining different online vehicle selection schemes and selecting vehicles based on the information whether the task vehicles definitely serve the vehicles in the Internet of vehicles comprises the following steps:
when the task vehicles in the Internet of vehicles know the information of part of service vehicles, selecting an online quality sensing vehicle selection scheme for vehicle selection;
and when the task vehicle in the Internet of vehicles does not know the information of the service vehicle, selecting an online vehicle selection scheme based on cost perception and quality perception to select the vehicle.
As shown in fig. 2, the online vehicle selection method for task distribution in the internet of vehicles according to the embodiment of the present invention includes the following steps:
s101, judging whether the task vehicle knows the information of part of service vehicles; if so, executing step S102; if not, executing step S104;
s102, distributing the tasks to all service vehicles, returning task results after the service vehicles finish the tasks, and determining task information of the service vehicles; calculating based on the acquired task information to obtain an optimal task expectation, and turning to step S103;
s103, selecting based on the acquired task expectation data, and continuously selecting the best service vehicle according to the index when the budget is not exhausted or the task is not completed;
and S104, selecting the vehicles according to an online vehicle selection scheme based on cost perception and quality perception.
The task information of the service vehicle provided by the embodiment of the invention comprises the following steps: the task completion quality of the service vehicle, and the task profit.
The best task expectation provided by the embodiment of the invention is that the best task expectation is the most suitable service vehicle index selected under the comprehensive condition.
Step S103 provided in the embodiment of the present invention further includes: the previously obtained optimal mission expectations are updated each time a vehicle is selected.
The online vehicle selection method for task distribution in the Internet of vehicles provided by the embodiment of the invention further comprises the following steps:
the vehicle selection problem is modeled as a new multiple arm slot machine problem, where each vehicle is considered an arm and its processing completion time is considered a corresponding reward, i.e., vehicle selection is virtualized as a pull arm.
The online vehicle selection method for task distribution in the Internet of vehicles further comprises the following steps: vehicle selection is made taking into account the cost of the service vehicle, the learning reward of the mission vehicle, and the cost.
The vehicle selection provided by the embodiment of the invention comprises the following steps: a plurality of vehicles are selected at a time for ensuring a task processing result.
The technical solution of the present invention is further described with reference to the following specific embodiments.
Example 1:
the scheme of the invention is mainly divided into two specific models under the conditions, and firstly, the scheme of online quality perception vehicle selection is as follows:
in the first case, the mission vehicle knows the information of the partial service vehicle, such as the cost of completing the mission, and divides the whole process into two parts, an exploration part and a practice part, wherein the exploration part comprises the following steps: distributing the tasks to all service vehicles, and returning task results after the service vehicles complete the tasks, so that the task completion quality, the task benefits and the like of the service vehicles can be known, and the optimal task expectation, namely the index which is most suitable for the selected service vehicles under the comprehensive condition, can be obtained by calculating the obtained information; the practical part is as follows: after the exploration part is finished, the obtained task expectation data is selected, when the budget is not exhausted or the task is not finished, the best service vehicle is selected continuously according to the index, and the obtained best task expectation is updated after each selection.
In the second case, conventionally, the task vehicle is not aware of any information of the service vehicle in advance, so the present invention proposes an online vehicle selection scheme based on cost perception and quality perception for this case, and the principle is basically similar to the first case in the exploration and practice phase. The specific model structure is as follows.
The technical effects of the present invention will be further explained in conjunction with simulation experiments.
The GPS data of a one-month flow trajectory of 500 taxis in san francisco, usa was selected as a data set for the experiment. For vehicle network communication, we set up a wireless channel and consider the distance between vehicles. Further, we set the channel bandwidth of the vehicle to 30MHz and the transmission power P to 0.2W, while also taking the noise component into account therein. For the task vehicle, we assume that the task vehicle has the same input data size and task result size (i.e., input data size is S)t2Mbits and task result size λt0.5 mbits); the CPU cycle required for a task is ωt200M. Furthermore, to describe the heterogeneity of service vehicles, service vehicles randomly selected numbers from a uniform distribution to assign [2,8 ]]GHz is the CPU frequency assigned to the task. For the cost parameter of the service vehicle, we randomly generate from (0, 1).
The main experimental results are shown in fig. 3-8, the scheme of the present invention is compared with several schemes which are common at present, and the experimental result figures show that the scheme provided by the present invention performs better in all aspects.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. An online vehicle selection method for task distribution in the Internet of vehicles is characterized by comprising the following steps:
and establishing an online quality perception vehicle selection scheme and an online vehicle selection scheme based on cost perception and quality perception, and determining different online vehicle selection schemes and selecting vehicles based on information whether task vehicles in the internet of vehicles definitely serve the vehicles.
2. The method of online vehicle selection for task distribution in an internet of vehicles as recited in claim 1, wherein the information of the service vehicle includes a cost to complete the task.
3. The method of claim 1, wherein the determining and performing vehicle selection for different online vehicle selection schemes based on information about whether a task vehicle explicitly serves a vehicle comprises:
when the task vehicles in the Internet of vehicles know the information of part of service vehicles, selecting an online quality sensing vehicle selection scheme for vehicle selection;
and when the task vehicle in the Internet of vehicles does not know the information of the service vehicle, selecting an online vehicle selection scheme based on cost perception and quality perception to select the vehicle.
4. The method for selecting the online vehicles for the task distribution in the internet of vehicles as claimed in claim 1, wherein the method for selecting the online vehicles for the task distribution in the internet of vehicles comprises the following steps:
judging whether a task vehicle knows the information of part of service vehicles or not; if so, executing the step two; if not, executing the step four;
distributing the tasks to all service vehicles, returning task results after the service vehicles finish the tasks, and determining task information of the service vehicles; calculating based on the acquired task information to obtain an optimal task expectation, and turning to the third step;
step three, based on the acquired task expectation data, selecting, and continuously selecting the best service vehicle according to the index when the budget is not exhausted or the task is not completed;
and step four, selecting the vehicle according to an online vehicle selection scheme based on cost perception and quality perception.
5. The method for selecting on-line vehicles for task distribution in the internet of vehicles as claimed in claim 4, wherein in step two, the task information of the service vehicle comprises: the task completion quality of the service vehicle, and the task profit.
6. The method for selecting the on-line vehicles for task distribution in the internet of vehicles according to claim 4, wherein in the second step, the best task expectation is the index of the service vehicle which is most suitable for being selected under the comprehensive condition;
the third step further comprises: the previously obtained optimal mission expectations are updated each time a vehicle is selected.
7. The method for selecting the online vehicles for the task distribution in the internet of vehicles as claimed in claim 1, wherein the method for selecting the online vehicles for the task distribution in the internet of vehicles further comprises:
modeling the vehicle selection problem as a new multi-armed slot machine problem, wherein each vehicle is considered an arm and its processing completion time is considered a corresponding reward, i.e., virtualizing the vehicle selection as a pull arm;
the online vehicle selection method for task distribution in the Internet of vehicles further comprises the following steps: comprehensively considering the cost of the service vehicle, the learning reward of the task vehicle and the cost to select the vehicle;
the vehicle selection includes: a plurality of vehicles are selected at a time for ensuring a task processing result.
8. An online vehicle selection control system for task distribution in an internet of vehicles, characterized in that the online vehicle selection control system for task distribution in the internet of vehicles comprises:
the service vehicle task analysis module is used for judging whether the task vehicle knows the information of part of service vehicles or not;
the system comprises an optimal task expectation acquisition module, a task processing module and a task processing module, wherein the optimal task expectation acquisition module is used for distributing tasks to all service vehicles, returning task results after the service vehicles complete the tasks and determining task information of the service vehicles; calculating based on the acquired task information to obtain an optimal task expectation;
the optimal service vehicle selection module is used for selecting based on the acquired task expectation data, and continuously selecting the optimal service vehicle according to the index when the budget is not exhausted or the task is not completed;
and the online vehicle selection scheme selection module is used for selecting vehicles according to the online vehicle selection scheme based on cost perception and quality perception.
9. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the method of online vehicle selection for task distribution in a network of vehicles as claimed in any one of claims 1 to 7.
10. An information data processing terminal, characterized in that the information data processing terminal comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to execute the online vehicle selection method for task distribution in the internet of vehicles according to any one of claims 1 to 7.
CN202110898408.5A 2021-08-05 2021-08-05 Online vehicle selection method, system and terminal for task distribution in Internet of vehicles Pending CN113592327A (en)

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CN112799823A (en) * 2021-03-31 2021-05-14 中国人民解放军国防科技大学 Online dispatching and scheduling method and system for edge computing tasks

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108364190A (en) * 2018-01-08 2018-08-03 东南大学 In conjunction with the online motivational techniques of mobile intelligent perception of reputation updating
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
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