Multi-unmanned aerial vehicle auxiliary edge computing resource allocation method based on task prediction

Abstract

The invention discloses a multi-unmanned aerial vehicle auxiliary edge computing resource allocation method based on task prediction. The method comprises the following steps of: firstly, modeling a communication model, a computing model and an energy loss model in an unmanned aerial vehicle auxiliary edge computing unloading scene; modeling a system total energy consumption minimization problem of the unmanned aerial vehicle auxiliary edge computing unloading network into task predictable process of terminal devices; obtaining prediction model parameters of different terminal devices by adopting centralized training through accessing historical data of the terminal devices; obtaining a prediction task set of the next time slot by utilizing the prediction model based on the task information of the current access terminal devices; and based on the prediction task set, decomposing an original problem into an unmanned aerial vehicle deployment problem and a task scheduling problem for joint optimization. The response time delay and completion time delay of the task can be effectively reduced through the deep learning algorithm, so that the calculation energy consumption is reduced; anevolutionary algorithm is introduced to solve the problem of joint unmanned aerial vehicle deployment and task scheduling optimization, the hovering energy consumption of the unmanned aerial vehicleis greatly reduced, and the utilization rate of computing resources is increased.

Description

technical field [0001] The present invention relates to the technical field of wireless communication, in particular to a multi-UAV-assisted edge computing resource allocation method based on task prediction. Background technique [0002] The rapid development of networking technology has led to the explosive growth of IoT devices, and the massive data generated at the edge of the network will bring enormous pressure to the transmission network and cloud computing center. In order to solve these problems, mobile edge computing is proposed, which effectively breaks the bottleneck of cloud computing development, releases the pressure of terminals, and realizes edge intelligence, low latency and large bandwidth processing data. The location of the server in the existing research on mobile edge computing is fixed and cannot be flexibly changed according to the needs of mobile users. Due to the characteristics of controllability, easy deployment and low cost of unmanned aerial v...

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Problems solved by technology

[0005] In order to overcome the deficiencies of the above-mentioned prior art, the present invention proposes a multi-UAV-assisted edge computing resource allocation method based on task prediction, uses the task prediction model to solve the problem of dynamic change of business data streams in different time slots, and deploys UAVs and Joint optimization of task scheduling, based on the predicted task set, the optimal UAV deployment plan and task scheduling plan for the next time slot are obtained, so as to reduce the total energy consumption of the system

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