Adaptive 3D routing protocol for flying ad hoc networks based on prediction-driven Q-learning

Abstract

The routing protocols are paramount to guarantee the Quality of Service (QoS) for Flying Ad Hoc Networks (FANETs). However, they still face several challenges owing to high mobility and dynamic topology. This paper mainly focuses on the adaptive routing protocol and proposes a Three Dimensional Q-Learning (3DQ) based routing protocol to guarantee the packet delivery ratio and improve the QoS. In 3DQ routing, we propose a Q-Learning based routing decision scheme, which contains a link-state prediction module and routing decision module. The link-state prediction module allows each Unmanned Aerial Vehicle (UAV) to predict the link-state of Neighboring UAVs (NUs), considering their Three Dimensional mobility and packet arrival. Then, UAV can produce routing decisions with the help of the routing decision module considering the link-state. We evaluate the various performance of 3DQ routing, and simulation results demonstrate that 3DQ can improve packet delivery ratio, goodput and delay of baseline protocol at most 71.36%, 89.32% and 83.54% in FANETs over a variety of communication scenarios.