An efficient secure distributed anonymous routing protocol for mobile and wireless ad hoc networks

2005 ◽  
Vol 28 (10) ◽  
pp. 1193-1203 ◽  
Author(s):  
Azzedine Boukerche ◽  
Khalil El-Khatib ◽  
Li Xu ◽  
Larry Korba
Keyword(s):  
Ad Hoc Networks ◽  
Wireless Ad Hoc Networks ◽  
Routing Protocol ◽  
Ad Hoc ◽  
Anonymous Routing ◽  
Hoc Networks

scholarly journals A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 449
Author(s):  
Sifat Rezwan ◽  
Wooyeol Choi
Keyword(s):  
Reinforcement Learning ◽  
Ad Hoc Networks ◽  
Wireless Ad Hoc Networks ◽  
Routing Protocol ◽  
Traffic Congestion ◽  
Ad Hoc ◽  
High Mobility ◽  
Flight Trajectory ◽  
Research Issues ◽  
Hoc Networks

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.

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