Reinforcement Learning-Based Routing Protocols in Vehicular Ad Hoc Networks for Intelligent Transport System (ITS): A Survey

Jan Lansky, Amir Masoud Rahmani () and Mehdi Hosseinzadeh ()
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Jan Lansky: Department of Computer Science and Mathematics, Faculty of Economic Studies, University of Finance and Administration, 101 00 Prague, Czech Republic
Amir Masoud Rahmani: Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, Douliou 64002, Taiwan
Mehdi Hosseinzadeh: Pattern Recognition and Machine Learning Lab, Gachon University, 1342 Seongnamdaero, Sujeonggu, Seongnam 13120, Republic of Korea

Mathematics, 2022, vol. 10, issue 24, 1-45

Abstract: Today, the use of safety solutions in Intelligent Transportation Systems (ITS) is a serious challenge because of novel progress in wireless technologies and the high number of road accidents. Vehicular ad hoc network (VANET) is a momentous element in this system because they can improve safety and efficiency in ITS. In this network, vehicles act as moving nodes and work with other nodes within their communication range. Due to high-dynamic vehicles and their different speeds in this network, links between vehicles are valid for a short time interval. Therefore, routing is a challenging work in these networks. Recently, reinforcement learning (RL) plays a significant role in developing routing algorithms for VANET. In this paper, we review reinforcement learning and its characteristics and study how to use this technique for creating routing protocols in VANETs. We propose a categorization of RL-based routing schemes in these networks. This paper helps researchers to understand how to design RL-based routing algorithms in VANET and improve the existing methods by understanding the challenges and opportunities in this area.

Keywords: vehicular ad hoc network (VANET); reinforcement learning (RL); artificial intelligence (AI); machine learning (ML); wireless networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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