SELF-SUPERVISED CLUSTERING IN VANETS USING GRAPH NEURAL NETWORKS / (Kayıt no. 292747)
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| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 02796nam a22002657a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20240923123517.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 240912d2024 cy ldj|| |||| 00| 0 eng d |
| 040 ## - KATALOGLAMA KAYNAĞI | |
| Özgün Kataloglama Kurumu | CY-NiCIU |
| Kataloglama Dili | eng |
| Çeviri Kurumu | CY-NiCIU |
| Açıklama Kuralları | rda |
| 041 ## - DİL KODU | |
| Metin ya da ses kaydının dil kodu | eng |
| 090 ## - Yerel Tasnif No | |
| tasnif no | YL 3432 |
| Cutter no | H37 2024 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Hassan, Israa Abdalla Ali |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | SELF-SUPERVISED CLUSTERING IN VANETS USING GRAPH NEURAL NETWORKS / |
| Sorumluluk Bildirimi | ISRAA ABDALLA ALİ HASSAN ; SUPERVISOR, ASST. PROF. DR. ZIYA DEREBOYLU |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024 |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | 64 sheets; |
| Birlikteki Materyal | + 1 CD ROM |
| Boyutları | 30 cm |
| 336 ## - CONTENT TYPE | |
| Source | rdacontent |
| Content type term | text |
| Content type code | txt |
| 337 ## - MEDIA TYPE | |
| Source | rdamedia |
| Media type term | unmediated |
| Media type code | n |
| 338 ## - CARRIER TYPE | |
| Source | rdacarrier |
| Carrier type term | volume |
| Carrier type code | nc |
| 502 ## - TEZ NOTU | |
| Tez Notu | Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Electrical-Electronic Engineering |
| 520 ## - ÖZET NOTU | |
| Özet notu | This thesis introduces a method to enhance the stability and efficiency of vehicular clusters in Vehicular Ad Hoc Networks (VANETs) by utilizing a clustering algorithm based on Graph Neural Networks (GNNs). As the number of vehicles on the road increases, problems such as traffic congestion, energy inefficiency, and air pollution have become more severe. This work tackles these issues by improving the stability of vehicle clusters, thereby boosting the efficiency of cooperative driving. Unlike conventional techniques that rely on periodic communication and the selection of cluster heads (CH), this approach uses a GNN model to create effective node representations, grouping vehicles with similar behaviours into stable clusters. The performance of the clustering methods was rigorously assessed using the open source highD dataset. The results demonstrated superior cluster longevity and efficiency compared to the K-means algorithm. The GNN model adeptly processes vehicular features, including speed, position, and acceleration, alongside graph data, using a force-directed algorithm to compute vehicle connectivity metrics. This innovative approach significantly reduces the overhead of control messages, thereby enhancing the overall system stability. The results of this research demonstrate that the GNN-based clustering algorithm, which incorporates both vehicular characteristics and graph structures, significantly surpasses traditional clustering methods. This positions it as a highly promising solution for advancing future intelligent transportation systems. By improving the management of vehicular clusters, this method contributes to more efficient and sustainable transportation networks, potentially leading to reduced traffic congestion, lower energy consumption, and diminished air pollution. |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Electrical-Electronic Engineering |
| Alt başlık biçimi | Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Dereboylu, Ziya |
| İlişkili Terim | Supervisor |
| 942 ## - EK GİRİŞ ÖGELERİ (KOHA) | |
| Sınıflama Kaynağı | Dewey Onlu Sınıflama Sistemi |
| Materyal Türü | Thesis |
| Geri Çekilme Durumu | Kayıp Durumu | Sınıflandırma Kaynağı | Kredi için değil | Koleksiyon Kodu | Kalıcı Konum | Mevcut Konum | Raf Yeri | Kayıt Tarih | Source of acquisition | Toplam Ödünçverme | Yer Numarası | Demirbaş Numarası | Son Görülme Tarihi | Kopya Bilgisi | Fatura Tarihi | Materyal Türü | Genel / Bağış Notu |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Depo | 12.09.2024 | Bağış | YL 3432 H37 2024 | T3849 | 12.09.2024 | C.1 | 12.09.2024 | Thesis | Electrical-Electronic Engineering | ||||
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 12.09.2024 | Bağış | YL 3432 H37 2024 | CDT3849 | 12.09.2024 | C.1 | 12.09.2024 | Suppl. CD | Electrical-Electronic Engineering |