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Machine learning-based cooperative spectrum sensing in dynamic segmentation enabled cognitive radio vehicular network

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. Howe...

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Main Authors: Hossain, Mohammad Asif, Md Noor, Rafidah, Yau, Kok-Lim Alvin, Azzuhri, Saaidal Razalli, Z'aba, Muhammad Reza, Ahmedy, Ismail, Jabbarpour, Mohammad Reza
Format: Article
Published: MDPI 2021
Subjects:
T Technology (General)
Online Access:http://eprints.um.edu.my/28745/
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spelling my.um.eprints.287452022-04-20T06:50:51Z http://eprints.um.edu.my/28745/ Machine learning-based cooperative spectrum sensing in dynamic segmentation enabled cognitive radio vehicular network Hossain, Mohammad Asif Md Noor, Rafidah Yau, Kok-Lim Alvin Azzuhri, Saaidal Razalli Z'aba, Muhammad Reza Ahmedy, Ismail Jabbarpour, Mohammad Reza T Technology (General) 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. MDPI 2021-02 Article PeerReviewed 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 <https://doi.org/10.3390/en14041169>. 10.3390/en14041169
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic T Technology (General)
spellingShingle T Technology (General)
Hossain, Mohammad Asif
Md Noor, Rafidah
Yau, Kok-Lim Alvin
Azzuhri, Saaidal Razalli
Z'aba, Muhammad Reza
Ahmedy, Ismail
Jabbarpour, Mohammad Reza
Machine learning-based cooperative spectrum sensing in dynamic segmentation enabled cognitive radio vehicular network
description 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.
format Article
author Hossain, Mohammad Asif
Md Noor, Rafidah
Yau, Kok-Lim Alvin
Azzuhri, Saaidal Razalli
Z'aba, Muhammad Reza
Ahmedy, Ismail
Jabbarpour, Mohammad Reza
author_facet Hossain, Mohammad Asif
Md Noor, Rafidah
Yau, Kok-Lim Alvin
Azzuhri, Saaidal Razalli
Z'aba, Muhammad Reza
Ahmedy, Ismail
Jabbarpour, Mohammad Reza
author_sort Hossain, Mohammad Asif
title Machine learning-based cooperative spectrum sensing in dynamic segmentation enabled cognitive radio vehicular network
title_short Machine learning-based cooperative spectrum sensing in dynamic segmentation enabled cognitive radio vehicular network
title_full Machine learning-based cooperative spectrum sensing in dynamic segmentation enabled cognitive radio vehicular network
title_fullStr Machine learning-based cooperative spectrum sensing in dynamic segmentation enabled cognitive radio vehicular network
title_full_unstemmed Machine learning-based cooperative spectrum sensing in dynamic segmentation enabled cognitive radio vehicular network
title_sort machine learning-based cooperative spectrum sensing in dynamic segmentation enabled cognitive radio vehicular network
publisher MDPI
publishDate 2021
url http://eprints.um.edu.my/28745/
_version_ 1735409574492504064
score 13.211869

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