QFAGR: A Q-learning-based Fast Adaptive Geographic Routing Protocol for Flying Ad hoc Networks

Due to the highly dynamic network topology in Flying Ad hoc Networks (FANETs), topology-based routing protocols are impractical, while known geographic routing protocols suffer from routing holes. In this paper, a Q-learning-based adaptive geographic routing protocol is presented, in which the impact of delay, mobility, and energy consumption on routing is comprehensively considered. To overcome the dynamic changes in routing caused by the high mobility of Unmanned Aerial Vehicles (UAVs), reinforcement learning parameters and HELLO message interval are adaptively adjusted by sensing local topology changes. Meanwhile, a new routing hole avoidance mechanism is proposed, which involves a scheme of broadcasting routing hole information and an approach of data forwarding route selection based on the node degree of UAVs and the distance to the destination node. To enable the routing protocol to adapt to highly dynamic changes in FANETs topology, link pre-learning and multi-Q learning methods are used to speed up the learning process. We use NS-3 to evaluate the proposed routing protocol. The results show that our protocol improves throughput by 6% and reduces delay by 20% compared to Q-learning-based Multi-objective Optimization Routing (QMR). It is also far superior to Q-learning-based Geographic Routing (QGeo) and Greedy Perimeter Stateless Routing (GPSR).