Internet of Vehicles mobile edge computing task unloading method and system based on learning pruning

Abstract

The invention provides an Internet of Vehicles mobile edge computing task unloading method and system based on learning pruning, belongs to the technical field of Internet of Vehicles communication, and the method comprising the following steps: calculating vehicle parameters in an Internet of Vehicles mobile edge computing scene, calculating unloading parameters according to the vehicle parameters; constructing a task unloading utility model according to the unloading parameters; and solving an optimal solution of the task unloading utility model by using a branch and bound algorithm in combination with an imitation learning method, thereby selecting a task unloading mode in a utility optimization manner and determining computing resources obtained by bidding. On the premise of considering the mobility of the vehicle, a vehicle utility function in an Internet of Vehicles scene is established, so that the selection of the unloading mode by the vehicle and the calculation resource obtained by bidding are carried out in a utility optimization mode; when the task is selected to be unloaded to the service type vehicle, the vehicle running in different directions is selected, so that the transmission rate is increased; a branch and bound method is utilized, a pruning strategy based on learning is combined to accelerate the branch pruning process, and the complexity is reduced.

Description

technical field [0001] The present invention relates to the technical field of Internet of Vehicles communication, in particular to a method and system for offloading mobile edge computing tasks of Internet of Vehicles based on learning pruning. Background technique [0002] With the advent of the Internet of Things (IoT) era, all things are interconnected, and the Internet of Vehicles, as a key branch of the Internet of Things, has become an indispensable part of modern transportation. With the rise and development of various applications, people's requirements for vehicle performance have gradually increased, and the demand for diversified services with low latency and low energy consumption has become increasingly prominent. The program requirements of in-vehicle terminals pose greater challenges to vehicles with limited resources. . [0003] The limited computing resources of the vehicle itself cannot meet the service requirements of users and the requirements of some c...

AI Technical Summary

Problems solved by technology

However, the limited computing resources of the vehicle are difficult to fully meet the offloading requirements of the vehicle, and the mobility of the vehicle brings many challenges to the task offloading of the vehicle, such as the increased possibility of communication link interruption, unreliable result feedback, etc.

[0005] For the above-mentioned existing vehicle computing task offloading technology, in the scenario of edge car networking task offloading, it is considered to leave the task in the local computing or offload to the edge server. Vehicles traveling in different directions are not considered. When performing V2V communication, the data transmission rate will increase as the vehicle distance decreases, which will have a certain gain effect on the transmission offload of tasks, and the computational complexity will increase exponentially with the increase of variables, which will take a long time

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