Design a D-Fog Scheme to Enhance Processing Performance of Real-Time IoT Applications in Fog Computing,ERICDATA高等教育知識庫
高等教育出版
熱門: Qiaoyun Zhang  Scopus AI  Helong Xia  美国 通识教育计划  Semi-trusted loader  Zhi-Wei Sun  
高等教育出版
首頁 臺灣期刊 學校系所 學協會 民間出版 大陸/海外期刊 政府機關 學校系所 學協會 民間出版 DOI查詢服務 DOI註冊服務
篇名
Design a D-Fog Scheme to Enhance Processing Performance of Real-Time IoT Applications in Fog Computing
並列篇名
Design a D-Fog Scheme to Enhance Processing Performance of Real-Time IoT Applications in Fog Computing
作者 Shin-Jer Yang、Wen-Hwa Liao、Wan-Lin Lu
英文摘要

The cloud computing has driven the fast development of the IoT services and applications. When a amount of real-time events in smart IoT applications are delivered to the cloud for processing, the loading on cloud computing becomes heavier, which can result in processing delays and exceeding their deadlines. The nodes in fog computing are closer to edge devices, which can process such these real-time events in this layer. With this fog process, the event processing efficiency can be enhanced and delays can also be solved in cloud computing.

This paper designs a set of fog computing framework called distributed fog computing scheme (D-FOG) that can improve the better processing performance of realtime IoT applications than conventional cloud computing. Through the simulation experiments, the final results indicate that the D-FOG yields the better performance in terms of Expired event rate, Event success rate, Average delay time, and Average processing time. Consequently, the proposed D-FOG is more effective and efficient in real-time events processing than conventional cloud computing.

起訖頁 1335-1345
關鍵詞 Cloud computing、Fog computing、D-FOG、Real-time IoT applications
刊名 網際網路技術學刊  
期數 202111 (22:6期)
出版單位 台灣學術網路管理委員會
DOI 10.53106/160792642021112206011   複製DOI
QR Code
該期刊
上一篇
A 4D Analysis Framework of Competitive Advantages and Development Strategies of Urban Science and Technology Parks: The Examples of Taipei Neihu Technology Park and Nankang Software Park
該期刊
下一篇
An Ensemble Machine Learning Botnet Detection Framework Based on Noise Filtering

高等教育知識庫  新書優惠  教育研究月刊  Ericdata高等教育知識庫