QLR‐FANET: A Q‐learning and rate control‐based routing protocol for flying ad hoc network

Abstract In recent years, flying ad hoc networks (FANETs) have been extensively applied owing to rapid advancements in unmanned aerial vehicles (UAVs). The continuous mobility of UAVs, coupled with their operational environment in three‐dimensional space, presents significant challenges for routing in FANETs. Additionally, factors such as link stability, link quality, link bitrate, and the ability to forward packets of the next‐hop node directly impact routing efficiency. Combining machine learning and location‐based routing approaches helps address these challenges. This paper proposes a location‐based routing protocol that integrates the Q‐learning (QL) reinforcement learning algorithm with dynamic link bitrate adjustment and a penalty mechanism for QL based on retransmission signals from the link layer. Performance evaluations conducted using OMNeT++ demonstrate that the proposed algorithm has markedly enhanced network performance in terms of packet delivery ratio, network throughput, end‐to‐end delay, link broken count, and packet error rate compared with other well‐known routing algorithms.