Multi-unmanned aerial vehicle task unloading and resource allocation method for edge computing system

Abstract

The invention discloses a multi-unmanned aerial vehicle task unloading and resource allocation method for an edge computing system. The method comprises the following steps: combining the current position of an unmanned aerial vehicle, the relative distance of the unmanned aerial vehicle, the relative distance between the unmanned aerial vehicle and intelligent equipment of the Internet of Things, and the service times of the intelligent equipment of the Internet of Things into a system state; constructing a depth deterministic strategy gradient optimization neural network; inputting the system state into the depth deterministic strategy gradient optimization neural network to obtain a system action; enabling the edge computing system to execute the system action and obtain the reward value of the system action according to the instant reward function; and continuously training parameters of the depth deterministic strategy gradient optimization neural network according to the obtained reward value until the reward value tends to be stable, and training to obtain an optimal strategy [pi]. According to the method, the trajectory, the unloading strategy and the computing resource allocation strategy of the unmanned aerial vehicle are optimized through the depth deterministic strategy gradient optimization neural network, and the energy consumption of the system is minimized on the premise of ensuring the service fairness of the intelligent equipment of the Internet of Things.

Description

technical field [0001] The present invention relates to the technical field of computing offloading and resource allocation of the Internet of Things, and more specifically, to a method for multi-UAV task offloading and resource allocation of an edge computing system. Background technique [0002] With the development of the Internet of Things, emerging diversified mobile applications such as augmented reality, face recognition, mobile online games, virtual reality and other emerging technologies continue to develop, and the requirements for delay processing and computing offload are also increasing. However, mobile devices have insufficient computing and processing capabilities and limited battery capacity, making it difficult to meet the requirements of low latency and low energy consumption. Modern industrial IoT smart devices are often limited in terms of computing power due to their small form factors and strict production cost constraints. However, smart devices often ...

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Problems solved by technology

[0006] However, in some remote areas or disaster relief scenarios, due to the high cost and limited number of drones, it is not realistic to have

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