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Artificial Intelligence-Driven and Secure 5G-VANET Architectures for Future Transportation Systems

Journal of Robotics and Control (JRC) · Vol. 6 No. 4 (2025) · hlm. 1833–1848

DOI
10.18196/jrc.v6i4.26295
ISSN
2715-5072, 2715-5056
Terbit
2025-07-13
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The advent of 5G has opened a new era of intelligent, adaptive and secure VANETs that is envisaged to serve as the backbone network architecture for next generation of vehicular transportation systems. In this work, we present a connected 5G VANETs-to-Edge Computing systems with Artificial Intelligence (AI) infrastructure to improve system adaptability, anomaly detection, trust management, and real-time decisionmaking. Crucial enabling technologies like Software-Defined Networking (SDN). Mobile Edge Computing (MEC), and millimeterwave communication are investigated in detail. We examine key security threats such as identity forgery, data interception, and denial-of-service attacks, and assess the AI-enhanced defense measures such as intrusion detection systems and blockchainbased trust models. Applications, like autonomous platooning, and collaborative vehicle authentication provide additional examples of AI technologies’ added value in the context of vehicular communications and safety. The paper concludes by providing open issues and future directions, including quantum-resistant protocols, lightweight AI models and cognitive networking in the context AI-driven 5G-VANET ecosystems.

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  • University of Basrah
  • Southern Technical University
  • Universiti Sains Malaysia
  • Shatt Al-Arab University College
  • University of Jordan
  • King Faisal University
  • University of Warith Al-Anbiyaa
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Rekan penulisOrganisasiArtikel bersama
Mohammed Amin AlmaiahUniversity of Jordan14
Zainab Marid AlzamiliAfiliasi tidak diketahui11
Jalal Mohammed Hachim AltmemiSouthern Technical University8
Abbas Jalal KaisheshUniversity of Kerbala7
Hussein Ahmed AliUniversity of Kirkuk6
Huda Mohammed AlsayednoorShatt Al-Arab University College5
Faris K. AL-ShammriUniversity of Warith Al-Anbiyaa5
Mansour ObeidatAfiliasi tidak diketahui5
Ali A. AlaidanyShatt Al-Arab University College5
Md Asri NgadiUniversity of Technology Malaysia4
Abdulaziz Zaid A. AljarwanUniversity of Ha'il4
Abdulnasser AbdulJabbar AbboodAfiliasi tidak diketahui3
Ali K. MattarShatt Al-Arab University College3
Ghazi Jalal KasheshUniversity of Kerbala3
May Adnan FalehSouthern Technical University3
Ali M. AbdulsadaUniversity of Warith Al-Anbiyaa3
Mustafa Moosa QasimUniversity of Basrah3
Akram Hussain Abd AliUniversity of Basrah3
Bushra Abdullah ShtaytSouthern Technical University3
Selvakumar ManickamUniversiti Sains Malaysia2
Shankar KaruppayahUniversiti Sains Malaysia2
Rommel Mahmoud AlAliKing Faisal University2
Marwa K. FarhanMinistry of Higher Education and Scientific Research2
Rommel Al AliAfiliasi tidak diketahui2
Saadaldeen Rashid AhmedAl-Ayen Iraqi University2
Nada Mohammed MuradMinistry of Higher Education and Scientific Research2
Ali Adil YassinUniversity of Basrah1
Devamekalai NagasundaramUniversiti Sains Malaysia1
Sababathy PalanisamyUniversiti Sains Malaysia1
Erum AshrafBahria University1
Mohammed Thakir MahmoodAfiliasi tidak diketahui1
Wassan Adnan HashimAl-Qalam University1
Saadaldeen Rashid AhmedBayan University1
Sari Ali SariUniversity of Basrah1
Seng Yue WongUniversity of Malaya1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Artificial Intelligence-Driven and Secure 5G-VANET Architectures for Future Transportation Systems</dc:title>
	<dc:creator>Saare, Murtaja Ali</dc:creator>
	<dc:creator>Abdulhamed, Mohamed Abdulrahman</dc:creator>
	<dc:creator>Al-Shareeda‬‏, ‪Mahmood A.</dc:creator>
	<dc:creator>Almaiah, Mohammed Amin</dc:creator>
	<dc:creator>Shehab,  Rami</dc:creator>
	<dc:subject xml:lang="en-US">Artificial Intelligence in VANETs</dc:subject>
	<dc:subject xml:lang="en-US">5G-Enabled Vehicular Networks</dc:subject>
	<dc:subject xml:lang="en-US">Secure VANET Architectures</dc:subject>
	<dc:subject xml:lang="en-US">Intelligent Transportation Systems (ITS)</dc:subject>
	<dc:subject xml:lang="en-US">Edge-Assisted AI Computing</dc:subject>
	<dc:subject xml:lang="en-US">Software-Defined Networking (SDN)</dc:subject>
	<dc:subject xml:lang="en-US">Blockchain-based Trust Management</dc:subject>
	<dc:description xml:lang="en-US">The advent of 5G has opened a new era of intelligent, adaptive and secure VANETs that is envisaged to serve as the backbone network architecture for next generation of vehicular transportation systems. In this work, we present a connected 5G VANETs-to-Edge Computing systems with Artificial Intelligence (AI) infrastructure to improve system adaptability, anomaly detection, trust management, and real-time decisionmaking. Crucial enabling technologies like Software-Defined Networking (SDN). Mobile Edge Computing (MEC), and millimeterwave communication are investigated in detail. We examine key security threats such as identity forgery, data interception, and denial-of-service attacks, and assess the AI-enhanced defense measures such as intrusion detection systems and blockchainbased trust models. Applications, like autonomous platooning, and collaborative vehicle authentication provide additional examples of AI technologies’ added value in the context of vehicular communications and safety. The paper concludes by providing open issues and future directions, including quantum-resistant protocols, lightweight AI models and cognitive networking in the context AI-driven 5G-VANET ecosystems.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2025-07-13</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/26295</dc:identifier>
	<dc:identifier>10.18196/jrc.v6i4.26295</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 6 No. 4 (2025); 1833-1848</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/26295/11724</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Murtaja Ali Saare, Mohamed Abdulrahman Abdulhamed, ‪Mahmood A. Al-Shareeda‬‏, Mohammed Amin Almaiah,  Rami Shehab</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>