Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F04274644%3A_____%2F22%3A%230000896" target="_blank" >RIV/04274644:_____/22:#0000896 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2227-7390/10/16/3017" target="_blank" >https://www.mdpi.com/2227-7390/10/16/3017</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/math10163017" target="_blank" >10.3390/math10163017</a>

Result language

angličtina

Original language name

Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review

Original language description

In recent years, flying ad hoc networks have attracted the attention of many researchers in industry and universities due to easy deployment, proper operational costs, and diverse applications. Designing an efficient routing protocol is challenging due to unique characteristics of these networks such as very fast motion of nodes, frequent changes of topology, and low density. Routing protocols determine how to provide communications between drones in a wireless ad hoc network. Today, reinforcement learning (RL) provides powerful solutions to solve the existing problems in the routing protocols, and designs autonomous, adaptive, and self-learning routing protocols. The main purpose of these routing protocols is to ensure a stable routing solution with low delay and minimum energy consumption. In this paper, the reinforcement learning-based routing methods in FANET are surveyed and studied. Initially, reinforcement learning, the Markov decision process (MDP), and reinforcement learning algorithms are briefly described. Then, flying ad hoc networks, various types of drones, and their applications, are introduced. Furthermore, the routing process and its challenges are briefly explained in FANET. Then, a classification of reinforcement learning-based routing protocols is suggested for the flying ad hoc networks. This classification categorizes routing protocols based on the learning algorithm, the routing algorithm, and the data dissemination process. Finally, we present the existing opportunities and challenges in this field to provide a detailed and accurate view for researchers to be aware of the future research directions in order to improve the existing reinforcement learning-based routing algorithms

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10200 - Computer and information sciences

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Mathematics

ISSN

2227-7390

e-ISSN

—

Volume of the periodical

10

Issue of the periodical within the volume

16

Country of publishing house

CH - SWITZERLAND

Number of pages

60

Pages from-to

1-60

UT code for WoS article

000845416900001

EID of the result in the Scopus database

2-s2.0-85137389472