Analysis of Physical Expansion Training Based on Edge Computing and Artificial Intelligence

Jianhui Lv,Zhongle Liu

doi:10.1155/2021/9145952

Jianhui Lv, Zhongle Liu

Open Access

https://doi.org/10.1155/2021/9145952

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Abstract

The effective development of physical expansion training benefits from the rapid development of computer technology, especially the integration of Edge Computing (EC) and Artificial Intelligence (AI) technology. Physical expansion training is mainly based on the collective form, and how to improve the quality of training to achieve results has become the content of everyone’s attention. As a representative technology in the field of AI, deep learning and EC evolving from traditional cloud computing technology are all well applied to physical expansion training. Traditional EC methods have problems such as high computing cost and long computing time. In this paper, deep learning technology is introduced to optimize EC methods. The EC cycle is set through the Internet of Things (IoT) topology to obtain the data upload speed. The CNN (Convolutional Neural Network) model introduces deep reinforcement learning technology, implements convolution calculations, and completes the resource allocation of EC for each trainer’s wearable sensor device, which realizes the optimization of EC based on deep reinforcement learning. The experiment results show that the proposed method can effectively control the server’s occupancy time, the energy cost of the edge server, and the computing cost. The proposed method in this paper can also improve the resource allocation ability of EC, ensure the uniform speed of the computing process, and improve the efficiency of EC.

Highlights

Physical training generally refers to all physical activities that maintain and develop proper physical expansion and improve physical health through exercise
For deep learning technology, because it requires high-density calculations, current intelligent algorithms based on deep learning usually run in cloud computing data centers with powerful computing capabilities
With the rapid development of the mobile Internet and the IoT industry, as a novel combination of Artificial Intelligence (AI) application and Edge Computing (EC), intelligent edge system is promising by researchers in the field of AI and network computing. e research on intelligent edge system based on EC architecture is becoming more and more important

Summary

Introduction

Physical training generally refers to all physical activities that maintain and develop proper physical expansion and improve physical health through exercise. For deep learning technology, because it requires high-density calculations, current intelligent algorithms based on deep learning usually run in cloud computing data centers with powerful computing capabilities. For deep learning, mobile devices running deep learning applications offload part of model inference tasks to adjacent EC nodes for calculations, thereby cooperating with terminal devices and edge servers to integrate the local computing capabilities and strong computing capabilities of the two complementary advantages In this way, because a large number of calculations are executed on EC nodes with strong computing power adjacent to the mobile device, the resource and energy consumption of the mobile device itself and the delay of task inference can be significantly reduced, thereby ensuring good user experience.

Related Work

EC Drives Real-Time Deep Reinforcement Learning Method

Analysis of Experimental Results

Conclusions