RLIS: Resource Limited Improved Security Beyond Fifth-Generation Networks Using Deep Learning Algorithms
This study explores the feasibility of allocating finite resources beyond fifth generation networks for extended reality applications through the implementation of enhanced security measures via offloading analysis (RLIS). The quantification of resources is facilitated through the utilization of par...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10261511/ |
| _version_ | 1797655621087002624 |
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| author | Shitharth Selvarajan Hariprasath Manoharan Alaa O. Khadidos Achyut Shankar M. S. Mekala Adil O. Khadidos |
| author_facet | Shitharth Selvarajan Hariprasath Manoharan Alaa O. Khadidos Achyut Shankar M. S. Mekala Adil O. Khadidos |
| author_sort | Shitharth Selvarajan |
| collection | DOAJ |
| description | This study explores the feasibility of allocating finite resources beyond fifth generation networks for extended reality applications through the implementation of enhanced security measures via offloading analysis (RLIS). The quantification of resources is facilitated through the utilization of parameters, namely energy, capacity, and power, which are equipped with proximity constraints. These constraints are then integrated with activation functions in both multilayer perceptron and long short term memory models. Furthermore, the system model has been developed using vision-based computing, which involves managing data queues in terms of waiting periods to minimize congestion for data transmission with limited resources. The major significance of the proposed method is to utilize allocated spectrums for future generation networks by allocating necessary resources and therefore high usage of resources by all users can be avoided. In addition the advantage of the proposed method is secure the networks that operate beyond 5G where more number of users will try to share the allocated resources that needs to be provided with high security conditions. |
| first_indexed | 2024-03-11T17:17:07Z |
| format | Article |
| id | doaj.art-06abe3c1db6840818719f9f60cd43890 |
| institution | Directory Open Access Journal |
| issn | 2644-125X |
| language | English |
| last_indexed | 2024-03-11T17:17:07Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj.art-06abe3c1db6840818719f9f60cd438902023-10-19T23:01:43ZengIEEEIEEE Open Journal of the Communications Society2644-125X2023-01-0142383239610.1109/OJCOMS.2023.331886010261511RLIS: Resource Limited Improved Security Beyond Fifth-Generation Networks Using Deep Learning AlgorithmsShitharth Selvarajan0https://orcid.org/0000-0002-4931-724XHariprasath Manoharan1https://orcid.org/0000-0001-5034-3034Alaa O. Khadidos2https://orcid.org/0000-0003-3297-3715Achyut Shankar3M. S. Mekala4Adil O. Khadidos5https://orcid.org/0000-0001-6854-6235Department of Computer Science, Kebri Dehar University, Kebri Dehar, EthiopiaDepartment of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, IndiaDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaSecure Cyber Systems Research Group (SCSRG), WMG, University of Warwick, Coventry, U.K.School of Computing, Robert Gordon University, Aberdeen, U.K.Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaThis study explores the feasibility of allocating finite resources beyond fifth generation networks for extended reality applications through the implementation of enhanced security measures via offloading analysis (RLIS). The quantification of resources is facilitated through the utilization of parameters, namely energy, capacity, and power, which are equipped with proximity constraints. These constraints are then integrated with activation functions in both multilayer perceptron and long short term memory models. Furthermore, the system model has been developed using vision-based computing, which involves managing data queues in terms of waiting periods to minimize congestion for data transmission with limited resources. The major significance of the proposed method is to utilize allocated spectrums for future generation networks by allocating necessary resources and therefore high usage of resources by all users can be avoided. In addition the advantage of the proposed method is secure the networks that operate beyond 5G where more number of users will try to share the allocated resources that needs to be provided with high security conditions.https://ieeexplore.ieee.org/document/10261511/Deep learning algorithmextended reality applicationsfifth generation networkslimited resourcevisual systems |
| spellingShingle | Shitharth Selvarajan Hariprasath Manoharan Alaa O. Khadidos Achyut Shankar M. S. Mekala Adil O. Khadidos RLIS: Resource Limited Improved Security Beyond Fifth-Generation Networks Using Deep Learning Algorithms IEEE Open Journal of the Communications Society Deep learning algorithm extended reality applications fifth generation networks limited resource visual systems |
| title | RLIS: Resource Limited Improved Security Beyond Fifth-Generation Networks Using Deep Learning Algorithms |
| title_full | RLIS: Resource Limited Improved Security Beyond Fifth-Generation Networks Using Deep Learning Algorithms |
| title_fullStr | RLIS: Resource Limited Improved Security Beyond Fifth-Generation Networks Using Deep Learning Algorithms |
| title_full_unstemmed | RLIS: Resource Limited Improved Security Beyond Fifth-Generation Networks Using Deep Learning Algorithms |
| title_short | RLIS: Resource Limited Improved Security Beyond Fifth-Generation Networks Using Deep Learning Algorithms |
| title_sort | rlis resource limited improved security beyond fifth generation networks using deep learning algorithms |
| topic | Deep learning algorithm extended reality applications fifth generation networks limited resource visual systems |
| url | https://ieeexplore.ieee.org/document/10261511/ |
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