Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey
The concept of Cognitive Radio (CR) has emerged as a practical solution to solve the issue of the fixed spectrum and bandwidth scarcity in wireless communication. However, the nature of dynamic Mobile Cognitive Radio Networks (MCRNs) drives to the emergence of new challenges, especially concerning t...
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| Format: | Article |
| Language: | English |
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IEEE
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/28679/ http://umpir.ump.edu.my/id/eprint/28679/1/Smart%20Routing%20Management%20Framework.pdf |
| _version_ | 1848823112442839040 |
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| author | Salih, Qusay Medhat Rahman, Md. Arafatur Al-Turjman, Fadi Zafril Rizal, M Azmi |
| author_facet | Salih, Qusay Medhat Rahman, Md. Arafatur Al-Turjman, Fadi Zafril Rizal, M Azmi |
| author_sort | Salih, Qusay Medhat |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The concept of Cognitive Radio (CR) has emerged as a practical solution to solve the issue of the fixed spectrum and bandwidth scarcity in wireless communication. However, the nature of dynamic Mobile Cognitive Radio Networks (MCRNs) drives to the emergence of new challenges, especially concerning the routing protocol operations. Applying a cross-layer design is considered a sufficient remedy to overcome routing protocol challenges such (e.g. channel diversity, integration route discovery with spectrum decision, mobility, etc.). Consequently, the cross-layer design has a magic solution to overwhelm routing challenges in MCRNs due to the ability to be free from the strict boundary and share the information and services with other layers in a manner that contributes to enhancing routing performance. Thus, the scope of this survey is to review and taxonomy numerous routing protocols in MCRNs according to methods of design to highlight the strength and weakness points. Also, machine learning has acquired much interest in this literature. A cross-layer framework for smart routing protocol in MCRNs has been proposed by exploiting machine learning mechanisms. Finally, the open research issues of routing protocol in MCRNs are summed up. |
| first_indexed | 2025-11-15T02:51:57Z |
| format | Article |
| id | ump-28679 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:51:57Z |
| publishDate | 2020 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-286792020-07-07T03:31:11Z http://umpir.ump.edu.my/id/eprint/28679/ Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey Salih, Qusay Medhat Rahman, Md. Arafatur Al-Turjman, Fadi Zafril Rizal, M Azmi QA75 Electronic computers. Computer science The concept of Cognitive Radio (CR) has emerged as a practical solution to solve the issue of the fixed spectrum and bandwidth scarcity in wireless communication. However, the nature of dynamic Mobile Cognitive Radio Networks (MCRNs) drives to the emergence of new challenges, especially concerning the routing protocol operations. Applying a cross-layer design is considered a sufficient remedy to overcome routing protocol challenges such (e.g. channel diversity, integration route discovery with spectrum decision, mobility, etc.). Consequently, the cross-layer design has a magic solution to overwhelm routing challenges in MCRNs due to the ability to be free from the strict boundary and share the information and services with other layers in a manner that contributes to enhancing routing performance. Thus, the scope of this survey is to review and taxonomy numerous routing protocols in MCRNs according to methods of design to highlight the strength and weakness points. Also, machine learning has acquired much interest in this literature. A cross-layer framework for smart routing protocol in MCRNs has been proposed by exploiting machine learning mechanisms. Finally, the open research issues of routing protocol in MCRNs are summed up. IEEE 2020 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28679/1/Smart%20Routing%20Management%20Framework.pdf Salih, Qusay Medhat and Rahman, Md. Arafatur and Al-Turjman, Fadi and Zafril Rizal, M Azmi (2020) Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey. IEEE Access, 8. pp. 67835-67867. ISSN 2169-3536. (Published) https://doi.org/10.1109/ACCESS.2020.2986369 |
| spellingShingle | QA75 Electronic computers. Computer science Salih, Qusay Medhat Rahman, Md. Arafatur Al-Turjman, Fadi Zafril Rizal, M Azmi Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey |
| title | Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey |
| title_full | Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey |
| title_fullStr | Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey |
| title_full_unstemmed | Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey |
| title_short | Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey |
| title_sort | smart routing management framework exploiting dynamic data resources of cross-layer design and machine learning approaches for mobile cognitive radio networks: a survey |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/28679/ http://umpir.ump.edu.my/id/eprint/28679/ http://umpir.ump.edu.my/id/eprint/28679/1/Smart%20Routing%20Management%20Framework.pdf |