Edge Computing and Cloud Computing for Internet of Things: A Review
The rapid expansion of the Internet of Things ecosystem has created an urgent need for efficient data processing and analysis technologies. This review aims to systematically examine and compare edge computing, cloud computing, and hybrid architectures, focusing on their applications within IoT envi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Informatics |
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| Online Access: | https://www.mdpi.com/2227-9709/11/4/71 |
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| _version_ | 1846104378808729600 |
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| author | Francesco Cosimo Andriulo Marco Fiore Marina Mongiello Emanuele Traversa Vera Zizzo |
| author_facet | Francesco Cosimo Andriulo Marco Fiore Marina Mongiello Emanuele Traversa Vera Zizzo |
| author_sort | Francesco Cosimo Andriulo |
| collection | DOAJ |
| description | The rapid expansion of the Internet of Things ecosystem has created an urgent need for efficient data processing and analysis technologies. This review aims to systematically examine and compare edge computing, cloud computing, and hybrid architectures, focusing on their applications within IoT environments. The methodology involved a comprehensive search and analysis of peer-reviewed journals, conference proceedings, and industry reports, highlighting recent advancements in computing technologies for IoT. Key findings reveal that edge computing excels in reducing latency and enhancing data privacy through localized processing, while cloud computing offers superior scalability and flexibility. Hybrid approaches, such as fog and mist computing, present a promising solution by combining the strengths of both edge and cloud systems. These hybrid models optimize bandwidth use and support low-latency, privacy-sensitive applications in IoT ecosystems. Hybrid architectures are identified as particularly effective for scenarios requiring efficient bandwidth management and low-latency processing. These models represent a significant step forward in addressing the limitations of both edge and cloud computing for IoT, offering a balanced approach to data analysis and resource management. |
| format | Article |
| id | doaj-art-acce31daffd2435e96e826f3cfc43c61 |
| institution | Kabale University |
| issn | 2227-9709 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Informatics |
| spelling | doaj-art-acce31daffd2435e96e826f3cfc43c612024-12-27T14:30:36ZengMDPI AGInformatics2227-97092024-09-011147110.3390/informatics11040071Edge Computing and Cloud Computing for Internet of Things: A ReviewFrancesco Cosimo Andriulo0Marco Fiore1Marina Mongiello2Emanuele Traversa3Vera Zizzo4Department of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, ItalyThe rapid expansion of the Internet of Things ecosystem has created an urgent need for efficient data processing and analysis technologies. This review aims to systematically examine and compare edge computing, cloud computing, and hybrid architectures, focusing on their applications within IoT environments. The methodology involved a comprehensive search and analysis of peer-reviewed journals, conference proceedings, and industry reports, highlighting recent advancements in computing technologies for IoT. Key findings reveal that edge computing excels in reducing latency and enhancing data privacy through localized processing, while cloud computing offers superior scalability and flexibility. Hybrid approaches, such as fog and mist computing, present a promising solution by combining the strengths of both edge and cloud systems. These hybrid models optimize bandwidth use and support low-latency, privacy-sensitive applications in IoT ecosystems. Hybrid architectures are identified as particularly effective for scenarios requiring efficient bandwidth management and low-latency processing. These models represent a significant step forward in addressing the limitations of both edge and cloud computing for IoT, offering a balanced approach to data analysis and resource management.https://www.mdpi.com/2227-9709/11/4/71cloud computingedge computingfog computingInternet of Thingsprivacy |
| spellingShingle | Francesco Cosimo Andriulo Marco Fiore Marina Mongiello Emanuele Traversa Vera Zizzo Edge Computing and Cloud Computing for Internet of Things: A Review Informatics cloud computing edge computing fog computing Internet of Things privacy |
| title | Edge Computing and Cloud Computing for Internet of Things: A Review |
| title_full | Edge Computing and Cloud Computing for Internet of Things: A Review |
| title_fullStr | Edge Computing and Cloud Computing for Internet of Things: A Review |
| title_full_unstemmed | Edge Computing and Cloud Computing for Internet of Things: A Review |
| title_short | Edge Computing and Cloud Computing for Internet of Things: A Review |
| title_sort | edge computing and cloud computing for internet of things a review |
| topic | cloud computing edge computing fog computing Internet of Things privacy |
| url | https://www.mdpi.com/2227-9709/11/4/71 |
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