在物联网边缘使用机器视觉和网格传感算法进行云IaaS优化
Nuruzzaman Faruqui1, Sandesh Achar2, Sandeepkumar Racherla3
1Department of Software Engineering, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka 1216, Bangladesh.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
本研究介绍了物联网边缘 (Mez) 的机器视觉和电网传感 (GRS) 算法,以降低安全摄像头网络的云成本. 综合方法显著减少了组织的带宽和存储需求.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 云计算 云计算 云计算
背景情况:
- 高清晰度 (HD) 物联网 (IoT) 摄像头越来越多地用于安全,但传输高清视频数据会给云基础设施带来压力.
- 大规模的安全网格在最小化云网络带宽和存储成本方面面临着挑战.
- 优化云资源分配对于具有成本效益的监控系统至关重要.
研究的目的:
- 介绍机器视觉在物联网边缘 (Mez) 技术的应用,结合一个新的网格传感 (GRS) 算法.
- 为大规模安全网格优化云基础设施即服务 (IaaS) 资源配置.
- 为了实现云带宽和存储的显著成本最小化.
主要方法:
- 机器视觉在物联网边缘 (Mez) 技术中的应用.
- 集成一个新的网络传感 (GRS) 算法用于物联网摄像头网络.
- 在 Mez 中使用网络延迟反模块进行视频转换.
- 通过GRS算法,通过不同的物联网节点对带宽要求进行自动排名.
主要成果:
- 实现了对带宽需求的31.29%的减少.
- 显示储存需求减少了22.43%.
- 通过优化资源配置,最大限度地降低整个电网的吞吐量.
结论:
- 拟议的系统有效地优化了物联网摄像头安全网的云基础设施即服务 (IaaS) 资源配置.
- Mez技术和GRS算法为减少带宽和存储成本提供了可行的解决方案.
- 集成带来了显著的云资源优化和企业的成本节省.
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