Abstract
Flying ad-hoc networks (FANET) are one of the most important branches of wireless ad-hoc networks, consisting of multiple unmanned air vehicles (UAVs) performing assigned tasks and communicating with each other. Nowadays FANETs are being used for commercial and civilian applications such as handling traffic congestion, remote data collection, remote sensing, network relaying, and delivering products. However, there are some major challenges, such as adaptive routing protocols, flight trajectory selection, energy limitations, charging, and autonomous deployment that need to be addressed in FANETs. Several researchers have been working for the last few years to resolve these problems. The main obstacles are the high mobility and unpredictable changes in the topology of FANETs. Hence, many researchers have introduced reinforcement learning (RL) algorithms in FANETs to overcome these shortcomings. In this study, we comprehensively surveyed and qualitatively compared the applications of RL in different scenarios of FANETs such as routing protocol, flight trajectory selection, relaying, and charging. We also discuss open research issues that can provide researchers with clear and direct insights for further research.
Highlights
Flying ad-hoc networks (FANETs) are gaining popularity because of their versatility, easy deployment, high mobility, and low operational cost [1]
We discuss the basics of the routing protocol, the reinforcement learning (RL)-based approaches for solving routing protocol problems such as energy consumption, end-to-end delay, and path stability and we present a comparative analysis among them
The latest applications of RL in FANETs have been exhaustively reviewed in terms of major features and characteristics and qualitatively compared with each other
Summary
Flying ad-hoc networks (FANETs) are gaining popularity because of their versatility, easy deployment, high mobility, and low operational cost [1]. FANET is an ad-hoc network of UAVs. Generally, in FANETs small UAVs are used because coordination and collaboration among small UAVs can outperform the large UAVs. small UAVs have low acquisition, operational costs, increased scalability, and survivability [9].FANET has some major challenges to overcome such as. Routing protocol: Routing in FANETs is a challenge owing to the high mobility and power constraints of the UAVs. Many routing protocols have been designed for ad-hoc networks but FANET requires a highly dynamic routing protocol to cope with the dynamic changes in the FANET topology [12]. The major issues include routing protocol, selecting flight trajectory, charging UAVs, antijamming, and ensuring the QoS of FANETs
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