Task unloading method based on mobile edge computing in Internet of Vehicles

Abstract

The invention belongs to the technical field of mobile communication, and particularly relates to a task unloading method based on mobile edge computing in the Internet of Vehicles, which comprises the following steps: constructing a multi-edge server joint unloading model; obtaining an unloading task of the vehicle, and constructing an unloading base station selection vector according to the unloading task of the vehicle; calculating load and energy consumption for task unloading according to the unloading base station selection vector by adopting a resource allocation method based on equivalent maximum tolerant time delay; taking the energy consumption as an adaptive function, and performing iterative optimization on an unloading task of the vehicle by adopting a genetic algorithm to obtain an unloading base station selection scheme; optimizing a task unloading strategy by using reinforcement learning according to an unloading base station selection scheme to obtain a task unloading ratio and unloading power; task unloading is completed according to the selected unloading base station, the formulated unloading ratio and the formulated unloading power; according to the method, the total energy consumption overhead of the system is effectively reduced, and the effectiveness of task unloading and resource allocation of the Internet of Vehicles is realized.

Description

technical field [0001] The invention belongs to the technical field of mobile communication, and in particular relates to a task offloading method based on mobile edge computing in the Internet of Vehicles. Background technique [0002] New mobile IoT applications such as autonomous driving and virtual reality require the execution of computationally intensive tasks with stringent latency requirements, which are challenging to complete in a timely manner given the limited processing power of mobile devices. Cloud computing can reduce the resource constraints of mobile devices through computational offloading, but its main challenge is the high latency caused by the continuous close communication between vehicles and cloud servers. In order to meet the requirements of low latency and high performance, mobile edge computing (MEC) has emerged as a practical solution, which can greatly alleviate the The resource limit of the vehicle. Due to the diversification of task types, m...

AI Technical Summary

Problems solved by technology

The power of the edge server increases quadratically with the CPU speed. When the load of the edge server is too high, the energy consumption will be very large.

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