Predictable Track-based Routing in Flying Ad hoc Networks

Flying Ad-hoc network (FANETs), a new kind of mobile AD hoc network, uses the aircraft as the air wireless communication node to construct a network and achieve effective communication at the network layer as a result of the ongoing expansion of communication networks into many industries. However, the high dynamics, limited node energy, and low network density of the FANETs present significant challenges to the FANETs routing protocol, which requires urgent attention in terms of its design. In order to address the issues associated with the routing metric of the FANETs routing protocol, such as the underutilization of link information and the inadequate consideration of the motion of UAVs, which result in high network packet loss rates, unstable routing, and long route reconvergence times, this paper proposes the Predictable Track-based Routing Protocol (PTP). PTP divides the routing protocol into three stages: route establishment, data transmission, and route maintenance. PTP creatively suggests Node Stability Factors (NSF) for UAV in the route establishment stage and develops a Q-learning algorithm to maintain it in accordance with the status of the nodes and nearby nodes. NSF combined the link quality calculated by HELLO messages to generate a link quality based on Q-learning(LQQ) metric. After that, the optimal path from the source node to the destination node is calculated based on this metric. In the data transfer phase, data is transmitted through the routes calculated in the route establishment phase. Create a new HELLO message during the routing maintenance phase to learn the location of the neighbor node and the two-hop neighbor node, predict the node’s future location using the Kalman filter algorithm based on the node’s past location, and react quickly when the link is about to change. Compared to conventional approaches, experiments demonstrate that PTP may successfully raise the successful data delivery rate by 10% to 30%, increase the average route survival time by up to 60%, and cut the average route reconvergence time by up to 80%.