Hossain, Mohammad Asif and Md Noor, Rafidah and Yau, Kok-Lim Alvin and Azzuhri, Saaidal Razalli and Z'aba, Muhammad Reza and Ahmedy, Ismail and Jabbarpour, Mohammad Reza (2021) Machine learning-based cooperative spectrum sensing in dynamic segmentation enabled cognitive radio vehicular network. Energies, 14 (4). ISSN 1996-1073, DOI https://doi.org/10.3390/en14041169.
Full text not available from this repository.Abstract
A vehicle ad hoc network (VANET) is a solution for road safety, congestion management, and infotainment services. Integration of cognitive radio (CR), known as CR-VANET, is needed to solve the spectrum scarcity problems of VANET. Several research efforts have addressed the concerns of CR-VANET. However, more reliable, robust, and faster spectrum sensing is still a challenge. A novel segment-based CR-VANET (Seg-CR-VANET) architecture is therefore proposed in this paper. Roads are divided equally into segments, and they are sub-segmented based on the probability value. Individual vehicles or secondary users produce local sensing results by choosing an optimal spectrum sensing (SS) technique using a hybrid machine learning algorithm that includes fuzzy and naive Bayes algorithms. We used dynamic threshold values for the sensing techniques. In this proposed cooperative SS, the segment spectrum agent (SSA) made the global decision using the tri-agent reinforcement learning (TA-RL) algorithm. Three environments (network, signal, and vehicle) are learned by this proposed algorithm to determine primary (licensed) users' activities. The simulation results indicate that, compared to current works, the proposed Seg-CR-VANET produces better results in spectrum sensing.
| Item Type: | Article |
|---|---|
| Funders: | Sunway University [CR-UM-SST-DCIS-2018-01] [RK004-2017], Universiti Malaya [CR-UM-SST-DCIS-2018-01] [RK004-2017] |
| Uncontrolled Keywords: | Spectrum sensing; Cognitive radio; VANET; Tri-agent reinforcement learning; Machine learning |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 20 Apr 2022 06:50 |
| Last Modified: | 20 Apr 2022 06:50 |
| URI: | http://eprints.um.edu.my/id/eprint/28745 |
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