Q-Learning Based Multi-objective Optimization Routing Strategy in UAVs Deterministic Network

In disaster areas, Flying Ad-Hoc Network (FANET) is a critical method for emergency communication. However, the limited payload capacity of Unmanned Aerial Vehicle (UAV) leads to communication link interruption and routing congestion, thus it is a great challenging to the deterministic routing of FANET. This paper proposes a Q-learning based multi-objective optimization routing strategy (QMR) in the UAVs deterministic network. The strategy considers delay, bandwidth and energy as QoS measurement to select a main path. Moreover, the UAV node can sense the energy of next node in the main path. When the energy of next node is low, the current node calculates a disjoint backup path. The simulation results show that compared with the existing routing methods, our strategy can provide higher packet reception rate, lower average end-to-end delay and lower energy consumption.