Investigating Routing in the VANET Network: Review and Classification of Approaches
Arun Kumar Sangaiah,
Amir Javadpour,
Chung-Chian Hsu,
Anandakumar Haldorai,
Ahmad Zeynivand
Affiliations
Arun Kumar Sangaiah
International Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
Amir Javadpour
Department of Computer Science and Technology (Cyberspace Security), Harbin Institute of Technology, Shenzhen 518057, China
Chung-Chian Hsu
Department of Information Management, International Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
Anandakumar Haldorai
Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore 642109, India
Ahmad Zeynivand
Department of Electrical & Computer Engineering, Tarbiat Modares University, Tehran 14115-111, Iran
Vehicular Ad Hoc Network (VANETs) need methods to control traffic caused by a high volume of traffic during day and night, the interaction of vehicles, and pedestrians, vehicle collisions, increasing travel delays, and energy issues. Routing is one of the most critical problems in VANET. One of the machine learning categories is reinforcement learning (RL), which uses RL algorithms to find a more optimal path. According to the feedback they get from the environment, these methods can affect the system through learning from previous actions and reactions. This paper provides a comprehensive review of various methods such as reinforcement learning, deep reinforcement learning, and fuzzy learning in the traffic network, to obtain the best method for finding optimal routing in the VANET network. In fact, this paper deals with the advantages, disadvantages and performance of the methods introduced. Finally, we categorize the investigated methods and suggest the proper performance of each of them.
