A Review on Machine Learning Techniques Used in VANET and FANET Networks

Abstract

The widespread use of the Internet and the increase in the number and variety of devices connected to the internet have led to the emergence of new methods in wireless communication. Dynamic and temporary Ad-Hoc networks, which do not require a fixed infrastructure as in traditional wireless network communication, are one of these new methods. The fact that Ad-Hoc networks do not need a fixed infrastructure has revealed a network structure with a lower cost and less configuration. Mobile Ad-Hoc networks play an important role, especially in the communication of nodes on the move. FANET (Flying Ad-Hoc Networks) networks, which are called flying ad hoc networks, are mobile Ad-Hoc networks used for communication of unmanned aerial vehicles (UAV), and VANET (Vehicular Ad-Hoc Networks) networks, which are called vehicular ad hoc networks, are mobile Ad-Hoc networks used for communication of road vehicles. The development and dissemination of these networks make a significant contribution to the development of autonomous vehicles and UAVs. The increase in the use of FANET and VANET networks, which are specialized subnets of mobile Ad-Hoc networks, and the increase in the number of nodes in these networks have caused problems related to security, efficiency, and sustainability in these networks. Machine learning methods, one of today' s effective and common approaches, are one of the ways that are frequently used in solving the problems specified in FANET and VANET networks. The rapid topology change, which is one of the most important features of these networks, makes it difficult to provide traffic management, trust management, routing, and data transmission. In this direction, machine learning approaches play an active role. In this study, it is presented by examining which machine learning techniques are used in the literature to perform important tasks such as traffic management, trust management, routing, and data transfer. Thus, it is aimed for those who will work in these fields to acquire information about machine learning approaches that can be used. Since the FANET network type is a new approach, it has been observed that there are few studies using machine learning. In VANET systems, studies using machine learning methods are especially intense in 2021. This study was carried out to give the reader an idea about which machine learning methods can be used in which problems in FANET and VANET networks.

Keywords

• Ad Hoc
• Wireless Communication
• VANET
• FANET
• Machine Learning

VANET ve FANET Ağlarda Kullanılan Makine Öğrenimi Teknikleri Üzerine İnceleme

Abstract

İnternetin yaygınlaşması ve internete bağlı cihaz sayısı ve çeşitliliğinin artması kablosuz iletişimde yeni yöntemlerin ortaya çıkmasını sağlamıştır. Geleneksel kablosuz ağ iletişiminde olduğu gibi sabit bir alt yapı gereksinimi olmayan dinamik ve geçici Ad-Hoc ağlar bu yeni yöntemlerden bir tanesidir. Ad-Hoc ağların sabit bir altyapıya ihtiyaç duymamaları daha düşük maliyetli ve daha az konfigürasyona ihtiyaç duyan bir ağ yapısını ortaya koymuştur. Özellikle hareket halindeki düğümlerin haberleşmesinde mobil Ad-Hoc ağlar önemli rol oynamaktadır. Uçan tasarsız ağlar olarak adlandırılan FANET (Flying Ad-Hoc Networks) ağlar insansız hava araçlarının (İHA) haberleşmesini, araçsal tasarsız ağlar olarak adlandırılan VANET (Vehicular Ad-Hoc Networks) ağlar ise karayolu araçlarının haberleşmesini sağlamada kullanılan mobil Ad-Hoc ağlardır. Bu ağların gelişimi ve yaygınlaşması otonom araçların ve İHA’ ların gelişimine önemli katkı sağlamaktadır. Mobil Ad-Hoc ağların özelleşmiş alt ağları olan FANET ve VANET ağlarının kullanımının artması ve bu ağlar içerisinde yer alan düğüm sayılarındaki artış bu ağlarda güvenlik, verimlilik ve sürdürülebilirlik ile ilgili problemlerin ortaya çıkmasına neden olmuştur. Günümüzün etkin ve yaygın yaklaşımlarından biri olan makine öğrenmesi yöntemleri FANET ve VANET ağlarda belirtilen problemlerin çözümünde sıklıkla başvurulan yollardan bir tanesidir. Bu ağların en önemli özelliklerinin başında gelen hızlı topoloji değişimi trafik yönetiminin, güven yönetiminin, yönlendirmelerin ve veri iletiminin sağlanmasını zorlaştırmaktadır. Bu doğrultuda makine öğrenmesi yaklaşımları etkin rol oynamaktadır. Bu çalışmada, literatürde trafik yönetimi, güven yönetimi, yönlendirme ve veri transferi gibi önemli görevleri gerçekleştirmede hangi makine öğrenmesi tekniklerinin kullanıldığı incelenerek sunulmuştur. Böylelikle bu alanlarda çalışacakların kullanılabilecek makine öğrenimi yaklaşımları ile ilgili bilgileri edinmeleri hedeflenmiştir. FANET ağ türünün yeni bir yaklaşım olması nedeniyle makine öğrenimi yaklaşımlarının kullanıldığı az sayıda çalışma olduğu gözlemlenmiştir. VANET sistemlerde ise makine öğrenmesi yöntemlerinin kullanıldığı çalışmalar özellikle 2021 yılında yoğunluk göstermektedir. Bu çalışma, FANET ve VANET ağlarda hangi problemlerde hangi makine öğrenmesi yöntemlerinin kullanılabileceği hakkında okuyucuya fikir vermek amacıyla gerçekleştirilmiştir.

Keywords

• Tasarsız Ağlar
• Kablosuz Haberleşme
• VANET
• FANET
• Makine Öğrenmesi

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