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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
02.12.2022
DOI
https://doi.org/10.7307/ptt.v34i6.4159
ISSUE
2022 (Vol 34), Issue 6
LICENSE
Copyright (c) 2022 Pavle Bugarčić, Nenad Jevtić, Marija Malnar
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This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Reinforcement Learning-Based Routing Protocols in Vehicular and Flying Ad Hoc Networks – A Literature Survey

Authors:Pavle Bugarčić, Nenad Jevtić, Marija Malnar

Abstract

Vehicular and flying ad hoc networks (VANETs and FANETs) are becoming increasingly important with the development of smart cities and intelligent transportation systems (ITSs). The high mobility of nodes in these networks leads to frequent link breaks, which complicates the discovery of optimal route from source to destination and degrades network performance. One way to overcome this problem is to use machine learning (ML) in the routing process, and the most promising among different ML types is reinforcement learning (RL). Although there are several surveys on RL-based routing protocols for VANETs and FANETs, an important issue of integrating RL with well-established modern technologies, such as software-defined networking (SDN) or blockchain, has not been adequately addressed, especially when used in complex ITSs. In this paper, we focus on performing a comprehensive categorisation of RL-based routing protocols for both network types, having in mind their simultaneous use and the inclusion with other technologies. A detailed comparative analysis of protocols is carried out based on different factors that influence the reward function in RL and the consequences they have on network performance. Also, the key advantages and limitations of RL-based routing are discussed in detail.

Keywords:reinforcement learning, Q-learning, routing protocols, VANET, FANET, ITS

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