Intelligent reputation system for safety messages in VANET

Samara, Ghassan

doi:10.11591/ijai.v9.i3.pp439-447

Akses terbuka Jurnal SINTA 1

Intelligent reputation system for safety messages in VANET

Samara, Ghassan^{Zarqa University}

IAES International Journal of Artificial Intelligence · Vol. 9 No. 3 (2020) · hlm. 439–447

DOI: 10.11591/ijai.v9.i3.pp439-447
ISSN: 2252-8938, 2089-4872
Terbit: 2020-09-01
Penerbit: Institute of Advanced Engineering and Science
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-sa/4.0

Abstrak

Nowadays Vehicle Ad - hoc Nets (VANET) applications have become very important in our lives because VANET provides drivers with safety messages, warnings, and instructions to ensure drivers have a safe and enjoyable journey. VANET Security is one of the hottest topics in computer networks research, Falsifying VANET system information violates VANET safety objectives and may lead to hazardous situations and loss of life. In this paper, an Intelligent Reputation System (IRS) aims to identify attacking vehicles will be proposed; the proposed system will rely on opinion generation, trust value collection, traffic analysis, position based, data collection, and intelligent decision making by utilizing the multi-parameter Greedy Best First algorithm. The results of this research will enhance VANET's safety level and will facilitate the identification of misbehaving vehicles and their messages. The results of the proposed system have also proven to be superior to other reputational systems.

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8 penulis · 28 tautan kepenulisan bersama · 6 lintas organisasi

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Zarqa University
Applied Science Private University
Afiliasi tidak diketahui

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Organisasi sama
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Tampilkan sebagai tabel

Rekan penulis	Organisasi	Artikel bersama
Mohammad Aljaidi	Zarqa University	3
Nawaf Alshdaifat	Applied Science Private University	1
Suhaila Abuowaida	Afiliasi tidak diketahui	1
Hussain Mohammed Turki	Zarqa University	1
Essam Al-Daoud	Zarqa University	1
Raed Alazaidah	Zarqa University	1
Mais Haj Qasem	Zarqa University	1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">Intelligent reputation system for safety messages in VANET</dc:title>
	<dc:creator>Samara, Ghassan</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Reputation system, Safety messages, Trust, VANET security,</dc:subject>
	<dc:description xml:lang="en-US">Nowadays Vehicle Ad - hoc Nets (VANET) applications have become very important in our lives because VANET provides drivers with safety messages, warnings, and instructions to ensure drivers have a safe and enjoyable journey. VANET Security is one of the hottest topics in computer networks research, Falsifying VANET system information violates VANET safety objectives and may lead to hazardous situations and loss of life. In this paper, an Intelligent Reputation System (IRS) aims to identify attacking vehicles will be proposed; the proposed system will rely on opinion generation, trust value collection, traffic analysis, position based, data collection, and intelligent decision making by utilizing the multi-parameter Greedy Best First algorithm. The results of this research will enhance VANET's safety level and will facilitate the identification of misbehaving vehicles and their messages. The results of the proposed system have also proven to be superior to other reputational systems.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2020-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/20295</dc:identifier>
	<dc:identifier>10.11591/ijai.v9.i3.pp439-447</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 9, No 3: September 2020; 439-447</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v9.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20295/12977</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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