Unmanned aerial vehicle-assisted edge computing unloading algorithm distributed excitation method

Abstract

An unmanned aerial vehicle-assisted edge computing unloading algorithm distributed excitation method comprises the following steps: step 1, establishing an air-assisted ground network driven by digital twinning, the network being composed of four parts: a vehicle, a road side unit, an unmanned aerial vehicle and digital twinning; step 2, decomposing a target problem, i.e., maximizing the utility of each entity in the network, into two sub-problems for excitation; 3, solving the first problem to obtain the actual computing resource contribution condition of each road side unit in the network; and 4, solving the second problem to obtain a resource allocation scheme for maximizing the vehicle satisfaction and the energy utilization rate. Digital twins (DT) is introduced to create a real-timedigital simulation model for a physical entity in a network, and real-time collection of entity data and real-time updating of an entity state are realized, so that the resource requirements of high dynamic property and real-time variability of the network are better met.

Description

technical field [0001] The invention belongs to the technical field of smart power plants and smart vehicles, and in particular relates to a distributed excitation method for edge computing unloading algorithms assisted by drones. Background technique [0002] In the application of smart power plants and smart vehicles, it is necessary to face many problems such as data collection, information interaction, and task assignment. Real-time adjustments are made based on the situation and the operating status of the power plant; for the blind spots in the supervision of the power plant, the managers cannot monitor the data in real time, nor can they get timely information feedback when problems occur. There are similar problems in the process of realizing the interconnection and management of vehicles on the ground network. For example, due to the limited coverage of the existing ground network, fixed deployment location and capacity, it cannot be used in the case of high dynamic...

AI Technical Summary

Problems solved by technology

[0005] 1. In the air-ground network, the high dynamics and unpredictability of the network are not conducive to the effective allocation of resources

[0006] 2. For offloading computing tasks, most of the current research work assumes that vehicle movement and data exchange follow a definite pattern, which cannot meet the needs of actual application scenarios

[0007] 3. Both the incentive mechanism and the resource allocation mechanism may generate a large amount of computing work, which will burden the limited UAV resources

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