Energy consumption optimization method for unmanned aerial vehicle auxiliary edge calculation based on genetic algorithm

Abstract

The invention discloses an energy consumption optimization method for unmanned aerial vehicle auxiliary edge calculation based on a genetic algorithm. The method comprises the steps of carrying out the parallel processing and calculation of an Internet of Things device, an unmanned aerial vehicle and a base station of a system, and carrying out the adjustment of the task execution sequence of the Internet of Things device, the unmanned aerial vehicle and the base station of the system through the genetic algorithm on the basis, reducing the energy consumption of the unmanned aerial vehicle and the equipment by combining the system unloading proportion, the CPU frequency and the unloading equipment sequence, reducing the time slot interval among the Internet of Things equipment, the unmanned aerial vehicle and the base station of the system, and reducing the total completion time of the system. Compared with a traditional single-assembly-line method and an unoptimized three-assembly-line method, the time consumption is less, and the hovering energy consumption of the unmanned aerial vehicle is lower.

Description

technical field [0001] The invention relates to the technical field of UAV-assisted edge computing, in particular to a method for optimizing energy consumption of UAV-assisted edge computing based on a genetic algorithm. Background technique [0002] With the deepening of informatization, mobile devices and the amount of data are increasing rapidly, and computing-intensive applications (such as online games, autonomous driving, etc.) are becoming more and more popular. Since these applications are computing-intensive and delay-sensitive, exceeding the computing frequency of ordinary smart devices, the contradiction between computing-intensive requirements and limited computing frequency reduces the quality of service (Quality of Service, QoS) of the device. In order to meet the above challenges, the concept of Mobile Edge Computing (MEC) is proposed. UAVs are deployed at the edge of the network, and the device offloads some or all of the devices to the UAV. Compared with cl...

AI Technical Summary

Problems solved by technology

Although the advantage of drones lies in flexibility and portability, the disadvantages are also obvious. Compared with ground base stations, using drones as carriers to provide computing offloading services has two major disadvantages: (1) limited battery life, drones Limited by energy constraints, it is impossible to fly for a long time; (2) UAVs, especially rotor UAVs, have limited load, which limits computing power and storage, and cannot handle large-scale equipment

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