A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems
Different fields have been thriving with the advents in mobile communication systems in recent years. These fields reap benefits of data collected by Internet of Things (IoT) in next generation (5G and 5BG) mobile networks. The IoT concept transforms different fields by providing large amount of dat...
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| Format: | Article |
| Language: | English |
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Elsevier B.V.
2023
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| Online Access: | http://eprints.utm.my/106806/1/JafriDin2023_ASurveyOfApplicationsOfArtificialIntelligence.pdf |
| _version_ | 1811132415830130688 |
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| author | Yazici, İbrahim Shayea, Ibraheem Din, Jafri |
| author_facet | Yazici, İbrahim Shayea, Ibraheem Din, Jafri |
| author_sort | Yazici, İbrahim |
| collection | ePrints |
| description | Different fields have been thriving with the advents in mobile communication systems in recent years. These fields reap benefits of data collected by Internet of Things (IoT) in next generation (5G and 5BG) mobile networks. The IoT concept transforms different fields by providing large amount of data to be used in their operations. This is achieved by massively utilized sensors and mobile devices that acquire data from internet connected devices to keep track of physical systems. Hence, different use cases benefit from the data generated thanks to future mobile network systems. Intelligent Transportation Systems, Smart Energy, Digital Twins, Unmanned Aerial Vehicles (UAVs), Smart Health, Cyber Security are of significant use cases that big data plays an important role for them. Large amount of data entails more intelligent systems with respect to conventional methods, and it also entails highly reduced response time for use cases. Artificial intelligence and machine learning models are adept in satisfying the requirements of this big data situations for different use cases. In this sense, this paper provides a survey of machine learning and artificial intelligence applications for different use cases enabled by future mobile communication systems. An overview of machine learning types and artificial intelligence is presented to provide insights into the intelligent method concepts. Available studies are extensively summarized, and they are also grouped to provide a complete overview of the study. Discussions on the reviewed papers based on artificial intelligence and machine learning concepts are made, and some descriptive figures about the results of the discussions are also given in the paper. Finally, research challenges for artificial intelligence and machine learning applications in the use cases are introduced, future research directions and concluding remarks are presented accordingly. |
| first_indexed | 2024-09-24T00:03:42Z |
| format | Article |
| id | utm.eprints-106806 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-09-24T00:03:42Z |
| publishDate | 2023 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | utm.eprints-1068062024-07-30T08:27:06Z http://eprints.utm.my/106806/ A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems Yazici, İbrahim Shayea, Ibraheem Din, Jafri TK Electrical engineering. Electronics Nuclear engineering Different fields have been thriving with the advents in mobile communication systems in recent years. These fields reap benefits of data collected by Internet of Things (IoT) in next generation (5G and 5BG) mobile networks. The IoT concept transforms different fields by providing large amount of data to be used in their operations. This is achieved by massively utilized sensors and mobile devices that acquire data from internet connected devices to keep track of physical systems. Hence, different use cases benefit from the data generated thanks to future mobile network systems. Intelligent Transportation Systems, Smart Energy, Digital Twins, Unmanned Aerial Vehicles (UAVs), Smart Health, Cyber Security are of significant use cases that big data plays an important role for them. Large amount of data entails more intelligent systems with respect to conventional methods, and it also entails highly reduced response time for use cases. Artificial intelligence and machine learning models are adept in satisfying the requirements of this big data situations for different use cases. In this sense, this paper provides a survey of machine learning and artificial intelligence applications for different use cases enabled by future mobile communication systems. An overview of machine learning types and artificial intelligence is presented to provide insights into the intelligent method concepts. Available studies are extensively summarized, and they are also grouped to provide a complete overview of the study. Discussions on the reviewed papers based on artificial intelligence and machine learning concepts are made, and some descriptive figures about the results of the discussions are also given in the paper. Finally, research challenges for artificial intelligence and machine learning applications in the use cases are introduced, future research directions and concluding remarks are presented accordingly. Elsevier B.V. 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/106806/1/JafriDin2023_ASurveyOfApplicationsOfArtificialIntelligence.pdf Yazici, İbrahim and Shayea, Ibraheem and Din, Jafri (2023) A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems. Engineering Science and Technology, an International Journal, 44 (NA). pp. 1-40. ISSN 2215-0986 http://dx.doi.org/10.1016/j.jestch.2023.101455 DOI : 10.1016/j.jestch.2023.101455 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Yazici, İbrahim Shayea, Ibraheem Din, Jafri A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems |
| title | A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems |
| title_full | A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems |
| title_fullStr | A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems |
| title_full_unstemmed | A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems |
| title_short | A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems |
| title_sort | survey of applications of artificial intelligence and machine learning in future mobile networks enabled systems |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/106806/1/JafriDin2023_ASurveyOfApplicationsOfArtificialIntelligence.pdf |
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