A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT Applications
The emergence of new technologies and the era of IoT which will be based on compute-intensive applications. These applications will increase the traffic volume of today’s network infrastructure and will impact more on emerging Fifth Generation (5G) system. Research is going in many details, such as...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/24/11585 |
| _version_ | 1797506775797202944 |
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| author | Muhammad Muneeb Kwang-Man Ko Young-Hoon Park |
| author_facet | Muhammad Muneeb Kwang-Man Ko Young-Hoon Park |
| author_sort | Muhammad Muneeb |
| collection | DOAJ |
| description | The emergence of new technologies and the era of IoT which will be based on compute-intensive applications. These applications will increase the traffic volume of today’s network infrastructure and will impact more on emerging Fifth Generation (5G) system. Research is going in many details, such as how to provide automation in managing and configuring data analysis tasks over cloud and edges, and to achieve minimum latency and bandwidth consumption with optimizing task allocation. The major challenge for researchers is to push the artificial intelligence to the edge to fully discover the potential of the fog computing paradigm. There are existing intelligence-based fog computing frameworks for IoT based applications, but research on Edge-Artificial Intelligence (Edge-AI) is still in its initial stage. Therefore, we chose to focus on data analytics and offloading in our proposed architecture. To address these problems, we have proposed a prototype of our architecture, which is a multi-layered architecture for data analysis between cloud and fog computing layers to perform latency- sensitive analysis with low latency. The main goal of this research is to use this multi-layer fog computing platform for enhancement of data analysis system based on IoT devices in real-time. Our research based on the policy of the OpenFog Consortium which will offer the good outcomes, but also surveillance and data analysis functionalities. We presented through case studies that our proposed prototype architecture outperformed the cloud-only environment in delay-time, network usage, and energy consumption. |
| first_indexed | 2024-03-10T04:38:15Z |
| format | Article |
| id | doaj.art-10af7bb232934f47ad32028af32af135 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:38:15Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-10af7bb232934f47ad32028af32af1352023-11-23T03:35:12ZengMDPI AGApplied Sciences2076-34172021-12-0111241158510.3390/app112411585A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT ApplicationsMuhammad Muneeb0Kwang-Man Ko1Young-Hoon Park2Department of Computer Engineering, Sang-Ji University, Wonju 26339, KoreaDepartment of Computer Engineering, Sang-Ji University, Wonju 26339, KoreaDivision of Computer Science, Sookmyung Women’s University, Seoul 04310, KoreaThe emergence of new technologies and the era of IoT which will be based on compute-intensive applications. These applications will increase the traffic volume of today’s network infrastructure and will impact more on emerging Fifth Generation (5G) system. Research is going in many details, such as how to provide automation in managing and configuring data analysis tasks over cloud and edges, and to achieve minimum latency and bandwidth consumption with optimizing task allocation. The major challenge for researchers is to push the artificial intelligence to the edge to fully discover the potential of the fog computing paradigm. There are existing intelligence-based fog computing frameworks for IoT based applications, but research on Edge-Artificial Intelligence (Edge-AI) is still in its initial stage. Therefore, we chose to focus on data analytics and offloading in our proposed architecture. To address these problems, we have proposed a prototype of our architecture, which is a multi-layered architecture for data analysis between cloud and fog computing layers to perform latency- sensitive analysis with low latency. The main goal of this research is to use this multi-layer fog computing platform for enhancement of data analysis system based on IoT devices in real-time. Our research based on the policy of the OpenFog Consortium which will offer the good outcomes, but also surveillance and data analysis functionalities. We presented through case studies that our proposed prototype architecture outperformed the cloud-only environment in delay-time, network usage, and energy consumption.https://www.mdpi.com/2076-3417/11/24/11585IoTdata analysisoffloadingedge computingfog computing |
| spellingShingle | Muhammad Muneeb Kwang-Man Ko Young-Hoon Park A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT Applications Applied Sciences IoT data analysis offloading edge computing fog computing |
| title | A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT Applications |
| title_full | A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT Applications |
| title_fullStr | A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT Applications |
| title_full_unstemmed | A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT Applications |
| title_short | A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT Applications |
| title_sort | fog computing architecture with multi layer for computing intensive iot applications |
| topic | IoT data analysis offloading edge computing fog computing |
| url | https://www.mdpi.com/2076-3417/11/24/11585 |
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