DDPG-Based Edge Resource Management for Coal Mine Surveillance Video Analysis in Cloud-Edge Cooperation Framework

Intelligent video surveillance is important to ensure production safety in coal mines, while cloud-edge cooperation is an effective means to improve the performance of intelligent video monitoring. However, in edge layers, incorrect resource allocation of computing and network resources will result...

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Autores principales:	Zhi Xu, Jingzhao Li
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Edge resource allocation intelligent video surveillance deep deterministic policy gradient edge status perception network Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/63794fb91d7f401c8673d4590afecc02
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spelling	oai:doaj.org-article:63794fb91d7f401c8673d4590afecc022021-11-26T00:01:08ZDDPG-Based Edge Resource Management for Coal Mine Surveillance Video Analysis in Cloud-Edge Cooperation Framework2169-353610.1109/ACCESS.2021.3129465https://doaj.org/article/63794fb91d7f401c8673d4590afecc022021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9622265/https://doaj.org/toc/2169-3536Intelligent video surveillance is important to ensure production safety in coal mines, while cloud-edge cooperation is an effective means to improve the performance of intelligent video monitoring. However, in edge layers, incorrect resource allocation of computing and network resources will result in the waste of resources and low real-time performance. In this paper, a DDPG-Based (Deep deterministic policy gradient-based) edge resource allocation method for cloud-edge cooperation framework is proposed. Firstly, the cloud-edge cooperation framework is designed for different tasks. Secondly, the joint minimizing problem of latency and bandwidth usage caused by edge computing is modeled. To quickly solve the joint optimization problem, we convert it to MDP (Markov Decision Process). In addition, ESPN (Edge status perception network) is proposed, which enhances the ability of feature perception and action output of DDPG. Finally, DDPG-ESPN is proposed to solve the joint optimization problem. Simulation results show that compared with other methods, DDPG-ESPN improves the real-time performance and bandwidth usage by up to 18.88% and 42.81% respectively.Zhi XuJingzhao LiIEEEarticleEdge resource allocationintelligent video surveillancedeep deterministic policy gradientedge status perception networkElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155457-155471 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Edge resource allocation intelligent video surveillance deep deterministic policy gradient edge status perception network Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Edge resource allocation intelligent video surveillance deep deterministic policy gradient edge status perception network Electrical engineering. Electronics. Nuclear engineering TK1-9971 Zhi Xu Jingzhao Li DDPG-Based Edge Resource Management for Coal Mine Surveillance Video Analysis in Cloud-Edge Cooperation Framework
description	Intelligent video surveillance is important to ensure production safety in coal mines, while cloud-edge cooperation is an effective means to improve the performance of intelligent video monitoring. However, in edge layers, incorrect resource allocation of computing and network resources will result in the waste of resources and low real-time performance. In this paper, a DDPG-Based (Deep deterministic policy gradient-based) edge resource allocation method for cloud-edge cooperation framework is proposed. Firstly, the cloud-edge cooperation framework is designed for different tasks. Secondly, the joint minimizing problem of latency and bandwidth usage caused by edge computing is modeled. To quickly solve the joint optimization problem, we convert it to MDP (Markov Decision Process). In addition, ESPN (Edge status perception network) is proposed, which enhances the ability of feature perception and action output of DDPG. Finally, DDPG-ESPN is proposed to solve the joint optimization problem. Simulation results show that compared with other methods, DDPG-ESPN improves the real-time performance and bandwidth usage by up to 18.88% and 42.81% respectively.
format	article
author	Zhi Xu Jingzhao Li
author_facet	Zhi Xu Jingzhao Li
author_sort	Zhi Xu
title	DDPG-Based Edge Resource Management for Coal Mine Surveillance Video Analysis in Cloud-Edge Cooperation Framework
title_short	DDPG-Based Edge Resource Management for Coal Mine Surveillance Video Analysis in Cloud-Edge Cooperation Framework
title_full	DDPG-Based Edge Resource Management for Coal Mine Surveillance Video Analysis in Cloud-Edge Cooperation Framework
title_fullStr	DDPG-Based Edge Resource Management for Coal Mine Surveillance Video Analysis in Cloud-Edge Cooperation Framework
title_full_unstemmed	DDPG-Based Edge Resource Management for Coal Mine Surveillance Video Analysis in Cloud-Edge Cooperation Framework
title_sort	ddpg-based edge resource management for coal mine surveillance video analysis in cloud-edge cooperation framework
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/63794fb91d7f401c8673d4590afecc02
work_keys_str_mv	AT zhixu ddpgbasededgeresourcemanagementforcoalminesurveillancevideoanalysisincloudedgecooperationframework AT jingzhaoli ddpgbasededgeresourcemanagementforcoalminesurveillancevideoanalysisincloudedgecooperationframework
_version_	1718409972596867072

DDPG-Based Edge Resource Management for Coal Mine Surveillance Video Analysis in Cloud-Edge Cooperation Framework

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